From 11 to 18 Million TEU: Optimizing Port-Workforce Reskilling and Training Finance for Thailand’s Laem Chabang Phase 3

From 11 to 18 Million TEU: Optimizing Port-Workforce Reskilling and Training Finance for Thailand’s Laem Chabang Phase 3 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article From 11 to 18 Million TEU: Optimizing Port-Workforce Reskilling and Training Finance for Thailand’s Laem Chabang Phase 3 Taisith Kruasom, Sumalee Ngeoywijit, Jarun Bootdachi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8127684/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Phase 3 of Laem Chabang Port will expand rated capacity from approximately 11 to 18 million TEU (twenty-foot equivalent units) and deepen rail connectivity within Thailand’s Eastern Economic Corridor (EEC). Realizing these gains depends not only on infrastructure but also on timely workforce reskilling and credible training-finance mechanisms. This paper develops a Thailand-specific framework that (i) translates throughput and rail-share targets into occupation- and region-specific staffing requirements and (ii) allocates multi-source training funds through a policy-constrained mixed-integer linear programming (MILP) model that respects Thailand’s Skill Development Promotion Act (“train-or-pay”), provider capacity, equity floors, and optional green-taxonomy eligibility. Using public parameters on costs, completion, and placement probabilities, and 2026–2027 budget envelopes, the model indicates that covering 80% of the incremental staffing needs from the 11→18 million TEU uplift requires approximately 1,212 enrollments under a 15% rail-share scenario and 1,341 enrollments under a 25% rail-share scenario. While training budgets are sufficient (showing small surpluses), support budgets for stipends, mentoring, and placement services are binding in all scenarios—particularly in 2027, when 60% of cohorts enter. We derive an implementable intake and financing plan for the EEC–Bangkok corridors and identify three policy levers—adjusting coverage targets, co-funding support costs via large employers, and mobilizing green-taxonomy funds—that can reconcile rail-intensive skill needs with fiscal constraints. The framework offers a practical template for embedding workforce planning and training finance into large-scale logistics investments in Thailand and comparable emerging-economy contexts. Earth and environmental sciences/Environmental social sciences Physical sciences/Mathematics and computing Laem Chabang Eastern Economic Corridor port workforce reskilling mixed-integer linear programming train-or-pay Thailand Taxonomy green skills 1. Introduction Laem Chabang Port serves as Thailand’s principal deep-sea gateway and a strategic pillar of the Eastern Economic Corridor (EEC). Phase 3 of its expansion is projected to increase annual container throughput from approximately 11 to 18 million TEU while simultaneously strengthening rail-based hinterland connectivity. Whereas port development in emerging economies has traditionally emphasized physical infrastructure and dredging capacity, contemporary planning literature increasingly recognizes that infrastructure performance is critically mediated by workforce capability, institutional coordination, and adaptive governance structures (Taneja, Ligteringen, & Walker, 2011 ; Eskafi et al., 2021 ). Global port systems are undergoing automation, digitalization, and organizational restructuring. These transformations reshape labour markets by displacing routine manual tasks while increasing demand for advanced analytical, technical, and system-integration skills. Port workers are now required to operate not only physical equipment but also digital twins, decision-support systems, and integrated logistics platforms, indicating that workforce capability should be conceptualized as a form of strategic infrastructure rather than as a peripheral human-resource concern (Decas & Kailas, 2019 ; De Bari et al., 2024). At the organizational level, digital transformation further requires transferable skills, continuous learning capacity, and adaptive problem-solving competencies. Empirical evidence consistently shows that reskilling and upskilling are essential for maintaining operational resilience under conditions of technological disruption. Without systematic workforce-development strategies, automation investments risk generating skill bottlenecks, operational inefficiencies, and safety vulnerabilities (Velmurugan et al., 2025). Thailand’s maritime and logistics sectors face additional institutional challenges. Digital technology adoption in Thai maritime enterprises remains uneven and is strongly influenced by organizational readiness, leadership commitment, and workforce competence. Similar patterns are observed in broader Thai industrial contexts, where competency frameworks for digital workforces reveal persistent gaps between policy ambition and operational capability (Janmethakulwat & Thanasopon, 2023; Chalaemwongwan et al., 2025 ). From a sustainability perspective, port operations are increasingly expected to align with environmental, social, and governance (ESG) standards. Green-finance taxonomies and sustainability frameworks imply that port workforces must also acquire “green skills,” including knowledge of low-carbon logistics, environmental compliance, and sustainability reporting. However, existing workforce development studies rarely translate these sustainability objectives into concrete, occupation-linked training pathways that can be selectively financed and implemented. Despite the growing recognition of these issues, port workforce planning remains fragmented. Existing studies often address automation, digitalization, training finance, or sustainability in isolation. Few provide integrated frameworks that link throughput expansion, modal shift, workforce reskilling, and training finance within a unified analytical structure. As a result, workforce planning frequently remains disconnected from infrastructure commissioning schedules, fiscal constraints, and institutional policy instruments. This study addresses these limitations by proposing a policy-constrained, optimization-based framework tailored to Laem Chabang Phase 3. By embedding workforce reskilling and training finance directly within port capacity expansion planning, the framework seeks to enhance the auditability, coherence, and inclusiveness of Thailand’s logistics investments. In doing so, the study positions workforce reskilling not merely as a supportive activity, but as a core governance mechanism for achieving sustainable and resilient port development. 2. Literature Review 2.1 Port Automation and Workforce Transformation Port automation has fundamentally reshaped the structure of port labour markets. Automation and digitalization progressively displace routine manual tasks while increasing demand for advanced technical, analytical, and system-integration skills. Evidence from automated and semi-automated terminals indicates that workers must increasingly combine mechanical expertise with digital literacy, troubleshooting competence, and systems thinking (Shovkovyy & Miri, 2023; Wiguna et al., 2025 ). These transformations rarely result in simple workforce reduction. Instead, they generate occupational recomposition, in which traditional operational roles decline while specialized maintenance, coordination, and data-oriented roles expand. Such recomposition alters skill structures more than total employment volume and requires continuous reskilling rather than one-off training interventions (Lu et al., 2010 ; Tatar, Ayvaz, & Pamucar, 2025 ). Importantly, automation is not solely a technological process but also a socio-organizational transition. Successful implementation depends on worker acceptance, organizational learning capacity, and institutional alignment. Ports that integrate reskilling strategies into automation planning achieve smoother transitions and superior long-term operational performance (Vongurai, 2025 ). 2.2 Digitalization, Cyber-Physical Systems, and Skill Requirements The emergence of cyber-physical port systems—including digital twins, AI-supported scheduling, and IoT-enabled monitoring—has further elevated workforce skill requirements. Workers must interpret complex data streams, interact with intelligent systems, and respond effectively to real-time operational disruptions (Shovkovyy & Miri, 2023; Toygar, 2024 ). Digital workforce studies consistently highlight gaps between technological capability and human readiness. Even when advanced systems are available, effective utilization depends on confidence, problem-solving ability, and cross-functional communication skills (Rikharom & Chansanam, 2023 ; Senarak, 2021 ). This reinforces the argument that digital transformation is constrained more by human systems than by hardware availability. 2.3 Training Finance and Institutional Mechanisms Training-finance institutions play a decisive role in shaping workforce transformation. Levy–exemption systems, public co-financing, and employer participation mechanisms are widely applied to correct under-investment in transferable skills and to align training supply with labour-market demand (de Langen, 2008 ; Balliauw, 2019). However, empirical evidence shows that training finance often prioritizes course delivery while underestimating post-training support, placement facilitation, and employer coordination. As a result, training completion does not consistently translate into employment outcomes, weakening the return on workforce-development investment (Hamzah et al., 2025 ). Moreover, training finance is rarely integrated into formal workforce-planning models. Most studies continue to treat finance as an external constraint rather than as a strategic decision variable, limiting the operational relevance of existing frameworks for large infrastructure projects. 2.4 Sustainability, Green Skills, and Port Workforces Sustainability agendas increasingly influence port development strategies. Low-carbon logistics, rail integration, and energy-efficient terminal operations require new categories of “green skills,” including environmental compliance, emissions monitoring, and sustainability reporting competencies (Shen et al., 2025 ; Vongurai, 2025 ). While sustainability frameworks emphasize technological solutions, workforce implications remain underexplored. Few studies specify how sustainability objectives translate into concrete occupational training pathways or how such pathways can be financed through green-aligned funding instruments. Consequently, sustainability discourse remains weakly connected to human-capital investment planning in port systems. 2.5 Workforce Planning Models and Optimization Approaches Quantitative workforce-planning models increasingly employ optimization techniques to balance cost efficiency, skill coverage, and institutional feasibility. These approaches demonstrate that formal optimization significantly improves allocation efficiency compared with ad hoc planning practices (Balliauw, 2019; Tatar et al., 2025 ). Nevertheless, most existing models focus on either operational logistics or human-resource planning in isolation. Integrated applications that simultaneously incorporate port-capacity expansion, modal-shift policies, training finance, and workforce reskilling remain rare. Furthermore, equity considerations, provider capacity, and sustainability conditions are often treated qualitatively rather than as formal constraints, limiting their applicability for policy design in emerging-economy contexts. 2.6 Literature Synthesis Taken together, the literature yields four consistent insights: Port automation and digitalization fundamentally reshape workforce skill requirements. Workforce transformation depends more on reskilling systems than on technology availability. Training finance and institutional mechanisms critically shape workforce outcomes. Sustainability objectives remain weakly integrated into workforce planning models. Despite these advances, existing studies remain fragmented. Few provide integrated frameworks that translate infrastructure expansion and modal-shift targets into occupation-specific workforce demand, link that demand to training-finance allocation, and evaluate outcomes under policy and fiscal constraints. This fragmentation motivates the integrated analytical framework proposed in the following sections. 3. Research Gaps and Contributions Synthesizing the literature reviewed in Section 2 , several critical gaps remain insufficiently addressed in ways that support implementable policy design for large-scale port expansion. Gap 1: Occupation–Region–Year Mapping Although automation and digitalization studies document broad shifts in workforce skill requirements, few translate throughput and modal-split targets into time-phased, occupation-specific labour demand disaggregated by region. Consequently, workforce planning often remains detached from the operational geography of port–hinterland systems and from infrastructure commissioning timelines, limiting its policy relevance (Decas & Kailas, 2019 ). Gap 2: Integrated Funding and Policy Constraints While training institutions and workforce development mechanisms are widely discussed, training finance is typically treated as an external constraint rather than as a decision variable within planning models. As a result, the interaction between funding sources, legal rules, and feasible training portfolios remains weakly formalized, reducing the operational applicability of existing frameworks (Taneja, Ligteringen, & Walker, 2011 ; Eskafi et al., 2021 ). Gap 3: Cohort Timing Relative to Commissioning Milestones Digital port transformation is inherently staged, yet few studies optimize the timing of training cohorts relative to infrastructure roll-out and operational transition points. This omission generates avoidable risks of short-term mismatches, including oversupply of trained labour before demand materializes or shortages at critical commissioning milestones (Iafelice, 2023 ). Gap 4: Equity and Provider Realism Although recent studies increasingly recognize the social dimensions of automation transitions, equity objectives and implementation realism—such as completion rates, regional placement probabilities, provider capacity limits, and targeted support for disadvantaged groups—are often treated descriptively rather than encoded as binding or penalized constraints. This limits the ability of models to produce actionable and auditable workforce-transition plans. Gap 5: Operationalization of Sustainability and Green Pathways Sustainability frameworks emphasize low-carbon logistics and compliance requirements, yet few studies translate these objectives into concrete, occupation-linked training pathways that can be selectively financed through sustainability-aligned instruments. Consequently, “green skills” remain weakly connected to formal workforce-planning models in port contexts (Chalaemwongwan, Sanrach, & Silpcharu, 2025 ). Collectively, these gaps indicate that existing research remains fragmented across technology, labour, finance, and sustainability domains and lacks an integrated decision-support approach suitable for infrastructure-led workforce transitions. Contributions of the Study To address these gaps, this study makes three contributions. First, it derives year-by-year, occupation- and region-specific full-time-equivalent (FTE) workforce requirements for Laem Chabang Phase 3 under alternative rail-share scenarios, thereby linking infrastructure expansion targets with labour demand at policy-relevant resolution. Second, it formulates a policy-constrained mixed-integer linear programming (MILP) model that jointly optimizes training enrollments and financial allocations. The formulation explicitly incorporates funding envelopes, institutional rules, provider capacity constraints, equity floors, and sustainability-eligibility screens, aligning workforce planning with realistic governance conditions (Taneja et al., 2011 ; Eskafi et al., 2021 ). Third, it generates implementable intake and funding plans for 2026–2027 that are aligned with commissioning timelines and robust under alternative modal-split scenarios. This operational orientation advances workforce development from general strategic recommendations toward formally testable and auditable planning outputs. Overall, the study reframes port workforce reskilling finance as a governance and optimization challenge embedded within infrastructure planning, offering an integrated approach to human-capital transition in emerging-economy port systems. 4. Methods This study implements an integrated, two-stage analytical framework that links infrastructure-driven labour demand with policy-constrained training-finance optimization. The methodological design is motivated by prior evidence that fragmented workforce and training planning weakens the effectiveness of automation and digitalization strategies in port systems (Taneja, Ligteringen, & Walker, 2011 ; Eskafi et al., 2021 ). The framework consists of two interdependent modules: (i) a workforce sizing module that translates throughput and modal-split targets into occupation-specific labour demand by region; and (ii) a mixed-integer linear programming (MILP) model that allocates training enrollments and financial resources subject to fiscal, institutional, and equity constraints. This structure enables workforce demand and training supply to be analyzed within a unified, policy-consistent decision system. 4.1 Workforce Sizing Module The workforce sizing module translates logistics performance targets into full-time-equivalent (FTE) labour requirements. This approach follows established logistics-planning literature that links throughput indicators with labour-intensity coefficients, ensuring that workforce demand is derived systematically from operational targets rather than from ad hoc staffing assumptions (Taneja et al., 2011 ; Eskafi et al., 2021 ). Let \(\:{Q}_{t}\) denote total container throughput in year \(\:t\) , and let \(\:{r}_{t}\) represent the proportion of throughput transported by rail. For each occupation \(\:i\) and region \(\:j\) , baseline labour intensity is represented by \(\:{\alpha\:}_{ij}\) (FTE per million TEU under road-dominant conditions), while \(\:{\beta\:}_{ij}\) captures incremental staffing intensity associated with rail-linked operations. Target labour demand is expressed as: $$\:{L}_{ijt}={Q}_{t}\cdot\:{s}_{jt}\cdot\:({\alpha\:}_{ij}+{\beta\:}_{ij}\cdot\:{r}_{t}),$$ where \(\:{s}_{jt}\) denotes the regional share of total throughput in year \(\:t\) . Incremental labour demand is calculated as: $$\:{\Delta\:}{L}_{ijt}={L}_{ijt}-{L}_{ij0},$$ where \(\:{L}_{ij0}\) represents baseline labour demand under pre-expansion conditions. This formulation is consistent with empirical evidence demonstrating that modal shift and automation primarily reshape occupational composition and skill structures rather than proportionally increasing total headcount (Decas & Kailas, 2019 ; De Bari et al., 2024). The resulting labour-demand matrix provides a policy-relevant and occupation-specific input for the subsequent optimization module. 4.2 Training and Finance Optimization Model The second module formulates workforce development as a policy-constrained MILP problem. Optimization approaches are widely applied in logistics and infrastructure planning to balance cost efficiency with multiple operational constraints (Eskafi et al., 2021 ) and are increasingly recognized as suitable tools for workforce allocation under institutional rules (Taneja et al., 2011 ). Decision Variables The main decision variables are: \(\:{x}_{g,o,r,p,t}\) : number of trainees from group \(\:g\) (e.g., youth, incumbents, displaced workers) trained for occupation \(\:o\) in region \(\:r\) by provider \(\:p\) in year \(\:t\) ; \(\:{z}_{k,t}\) : expenditure from funding source \(\:k\) in year \(\:t\) ; \(\:{\xi\:}_{j}\) : slack variables capturing deviations from policy or coverage targets. Objective Function The objective is to maximize net social benefit (NSB), defined as expected lifetime wage uplift from successful trainees minus training and support costs and minus penalties for policy violations: $$\:\text{m}\text{a}\text{x}NSB=\sum\:_{g,o,r,p,t}{\beta\:}_{g,o,r}{\pi\:}_{g,o,r,p}{x}_{g,o,r,p,t}-\sum\:_{k,t}{c}_{k,t}{z}_{k,t}-\sum\:_{j}{\gamma\:}_{j}{\xi\:}_{j},$$ where \(\:\beta\:\) denotes expected wage uplift, \(\:\pi\:\) completion–placement probability, and \(\:\gamma\:\) penalty weights. This formulation reflects prior findings that workforce planning must balance economic returns with institutional feasibility and governance consistency (Velmurugan et al., 2025; Iafelice, 2023 ). 4.3 Constraint Structure The model incorporates six categories of constraints: 4.3.1 Budget and Eligibility Constraints : Training and support expenditures are limited by funding envelopes and eligibility rules, ensuring fiscal feasibility (Eskafi et al., 2021 ). 4.3.2 Provider Capacity Constraints : Training volumes cannot exceed provider capacity, reflecting institutional limitations of vocational and higher-education systems (Janmethakulwat & Thanasopon, 2023). 4.3.3 Demand Coverage Constraints : A minimum fraction of incremental labour demand must be covered, linking workforce sizing with training allocation. 4.3.4 Institutional Policy Constraints : Levy–exemption and firm-size rules are incorporated as binding or penalized constraints, reflecting training-finance governance structures (Taneja et al., 2011 ). 4.3.5 Equity Constraints : Minimum participation shares for priority groups and regions are imposed to prevent uneven workforce transition outcomes (Velmurugan et al., 2025). 4.3.6 Sustainability Eligibility Constraints : Training programs eligible for green-aligned funding are restricted to sustainability-consistent occupational pathways (De Bari et al., 2024). 4.4 Implementation and Transparency The MILP model can be implemented using widely available platforms such as Python/Pyomo, Excel-based solvers, or AMPL. This flexibility is important for public agencies and regional institutions with heterogeneous analytical capacity (Eskafi et al., 2021 ). All parameters and assumptions are explicitly reported, enabling scenario testing, replication, and policy auditing. This transparency addresses a key limitation of prior workforce-planning studies, which often rely on opaque or non-transferable modeling structures. 4.5 Methodological Positioning Methodologically, this study positions workforce reskilling not as a secondary human-resource activity but as an integral component of infrastructure system optimization. By embedding institutional, fiscal, and equity constraints directly into the model, the approach advances workforce planning from descriptive policy discussion toward formal, testable decision-support analysis. This methodological foundation enables the empirical calibration and scenario evaluation presented in the following section. 5. Data and Calibration The model is calibrated for Laem Chabang Phase 3 using publicly available planning assumptions and internationally validated parameter ranges. Consistent with infrastructure–workforce planning practice, calibration prioritizes transparency, replicability, and scenario consistency rather than point forecasting (Taneja et al., 2011 ; Eskafi et al., 2021 ). 5.1 Throughput and Modal-Split Scenarios Baseline throughput is set at 11 million TEU, with a commissioning target of 18 million TEU, yielding an incremental volume of 7 million TEU. Two modal-split scenarios are evaluated: Scenario A (Base rail) : 15% rail share Scenario B (High rail) : 25% rail share These values reflect policy ambitions for rail-oriented logistics transition and are consistent with international intermodalization and decarbonization benchmarks (De Bari et al., 2024; Decas & Kailas, 2019 ). Throughput is allocated 65% to the Eastern Economic Corridor (EEC) and 35% to the Bangkok corridor, reflecting the continued operational importance of inland container depots and dry ports in Thailand’s logistics system. 5.2 Cohort Phasing and Workforce Dynamics Training cohorts are phased to mirror infrastructure roll-out, with 40% of enrollments scheduled in 2026 and 60% in 2027. This back-loaded structure reflects empirical evidence that labour demand accelerates during late commissioning and stabilization phases of large infrastructure projects (Taneja et al., 2011 ; Iafelice, 2023 ). This temporal calibration acknowledges that premature training may lead to placement friction, while delayed training risks operational bottlenecks. The phased approach therefore balances readiness with absorption capacity. 5.3 Occupational Input Parameters For each occupation–region–provider combination, the following parameters are specified: Unit training cost Support and placement cost Completion probability Placement probability Expected wage uplift Placement probabilities are assumed to be higher in the EEC than in the Bangkok corridor, reflecting spatial concentration of port-related employment opportunities. Parameter ranges are informed by prior workforce-development and digital-skills studies that highlight systematic variation by occupation and provider type (Velmurugan et al., 2025). For niche rail-yard and automation-related roles with limited Thai historical data, probabilities are imputed from comparable mechatronics and digital-maintenance occupations, consistent with standard practice in workforce forecasting (Eskafi et al., 2021 ; Decas & Kailas, 2019 ). All monetary values are expressed in constant 2026 Thai Baht. 5.4 Fiscal Envelopes and Policy Anchors Annual training budgets are fixed at 59 million THB for both 2026 and 2027. Support budgets are set at 10 million THB in 2026 and 12 million THB in 2027. These envelopes reflect realistic public-finance constraints observed in vocational and sectoral training programs (Taneja et al., 2011 ; Eskafi et al., 2021 ). Two policy anchors are incorporated: At least 50% of publicly funded training seats must be linked to firms employing 100 or more workers; Standard attendance and course-hour regulations apply to all trainees. These anchors reflect institutional rules governing levy–exemption and firm-based training participation in developing-economy contexts. 5.5 Coverage Target and Scenario Logic A coverage parameter of \(\:\theta\:=0.80\) is adopted as the base requirement, meaning that the training system must cover at least 80% of incremental FTE demand derived from throughput expansion. This threshold reflects a pragmatic balance between fiscal realism and operational sufficiency. Prior workforce-planning studies suggest that attempting to fully cover incremental demand through public training systems often leads to inefficiency and crowding-out of private training initiatives (Velmurugan et al., 2025; Iafelice, 2023 ). 5.6 Calibration Summary To ensure clarity and replicability, the core calibration assumptions applied in the workforce-sizing and optimization modules are consolidated in Table 1 . The table summarizes throughput targets, modal-split scenarios, regional allocation rules, cohort phasing, fiscal envelopes, and policy anchors that jointly define the empirical environment of the model. These parameters are not intended as point forecasts but as scenario-consistent policy benchmarks, enabling transparent interpretation of the optimization results and facilitating sensitivity analysis across alternative infrastructure and governance conditions. Table 1 Data and Scenario Calibration Item Description / Value Throughput baseline 11 million TEU Throughput target 18 million TEU Incremental volume ΔTEU = 7 million Scenario A Rail share = 15% Scenario B Rail share = 25% Regional allocation 65% EEC / 35% Bangkok corridor Cohort phasing 2026: 40%; 2027: 60% Occupation inputs Unit costs; completion & placement rates; wage uplift Training budget 59 million THB/year Support budget 10 million THB (2026); 12 million THB (2027) Policy anchors ≥ 50% seats in firms ≥ 100 employees Coverage target \(\:\theta\:=0.80\) 5.7 Section Synthesis This calibration strategy ensures that model outputs are driven by realistic institutional and fiscal conditions rather than by purely technical optimization. The design therefore supports policy-relevant interpretation while maintaining analytical transparency. The calibrated parameters provide the empirical basis for the enrollment and budget outcomes presented in the following Results section. 6. Results 6.1 Enrollment Requirements and Budget Fit Under the base coverage target ( \(\:\theta\:=0.80\) ), the model yields distinct enrollment and budget profiles across rail-share scenarios. In Scenario A (15% rail share), the system requires approximately 1,212 enrollments over 2026–2027. In Scenario B (25% rail share), total enrollments increase to approximately 1,341, reflecting the higher labour intensity of rail-linked and automation-intensive occupations. Across both scenarios, labor demand concentrates in three occupational clusters: Rail-yard and intermodal operations; Automation and mechatronics maintenance; and Scheduling and data-oriented coordination roles. These results confirm that modal shift and automation do not simply change total labour volume but systematically reallocate demand toward higher-support, higher-complexity occupations, consistent with empirical evidence from automated terminal systems (Decas & Kailas, 2019 ; De Bari et al., 2024). 6.2 Budget Binding Patterns Table 2 reports enrollment and budget outcomes by scenario and year. Aggregated across the two-year planning horizon, clear and consistent binding patterns emerge. Under Scenario A (15% rail share), total training expenditures reach approximately 103.2 million THB against an available budget of 118.0 million THB, leaving an unutilized balance of about 14.8 million THB. In contrast, support expenditures amount to approximately 28.0 million THB against a budget ceiling of 22.0 million THB, resulting in a shortfall of roughly 6.0 million THB. Under Scenario B (25% rail share), training expenditures rise to approximately 114.5 million THB, leaving a smaller surplus of about 3.5 million THB. Support expenditures increase more sharply to approximately 30.9 million THB, generating a larger shortfall of about 8.9 million THB. These patterns demonstrate that support funding, rather than training funding, constitutes the binding constraint in all scenarios. Importantly, the magnitude of the support deficit increases with higher rail share, indicating that modal shift intensifies the support intensity of workforce transition. From a structural perspective, this outcome reflects the different economic roles of training and support budgets. Training expenditures primarily finance course delivery and classroom capacity, which scale relatively smoothly with enrollment. Support expenditures, by contrast, finance mentoring, workplace integration, employer coordination, and placement facilitation—activities that scale non-linearly with occupational complexity and technological sophistication. As automation and rail-linked operations expand, marginal trainees increasingly belong to high-support occupations, causing support budgets to bind earlier than training budgets. The presence of persistent training-budget surpluses alongside support-budget deficits further implies that workforce transition inefficiency does not arise from insufficient training volume but from misalignment in budget composition. In practical terms, additional training seats cannot be converted into effective employment outcomes once support capacity becomes saturated. Consequently, the effective marginal productivity of training investment declines when support budgets are binding. This binding pattern also explains why Scenario B, despite higher overall training utilization, exhibits greater fiscal stress. The shift toward rail-intensive and automation-linked occupations increases the proportion of trainees requiring individualized placement support, employer engagement, and extended onboarding assistance. Without proportional expansion of support funding, the system encounters diminishing returns to training expansion. From a governance standpoint, these results indicate that workforce reskilling finance should be evaluated not as a single aggregate budget but as a two-component system with asymmetric binding behavior. Policies that focus solely on expanding training budgets risk overestimating transition feasibility and underestimating implementation risk. In summary, the budget-binding analysis reveals that: 1) Training budgets are structurally non-binding across both scenarios. 2) Support budgets bind early and intensify with higher rail share. 3) Workforce transition feasibility is therefore governed by support capacity rather than training volume. This insight provides the quantitative foundation for the policy discussion in the following section, where governance instruments for relieving support-budget pressure are examined. 6.3 Occupational Allocation Effects Rail-intensive and automation-linked occupations consistently receive higher training and support allocations in both scenarios. These occupations—such as rail-yard coordination, automation maintenance, and mechatronics support—exhibit higher per-capita support requirements due to complex workplace integration, safety certification, and employer coordination needs. In contrast, scheduling and data-coordination roles remain highly attractive in the optimal solution because of their favorable cost-to-placement ratios and lower support intensity. This allocation pattern confirms that the optimization model systematically prioritizes occupations that maximize employment impact per unit of fiscal expenditure while still satisfying coverage, equity, and sustainability constraints. Rather than expanding training uniformly across occupations, the model reallocates capacity toward roles that combine high placement probability with moderate support cost, thereby improving overall system efficiency. Importantly, this outcome illustrates that workforce planning is fundamentally an allocation-efficiency problem rather than an aggregate training-volume problem. Even under identical budget envelopes, different occupational mixes can generate substantially different employment outcomes. Consequently, policy emphasis on total trainee numbers risks obscuring the more decisive question of which occupations receive priority within constrained fiscal space. From a governance perspective, this finding implies that occupational targeting should be treated as a core policy lever rather than as a technical by-product of training institutions. Failure to manage occupational allocation explicitly may lead to fiscal crowding by high-support occupations or, conversely, to underinvestment in strategically critical technical roles. The optimization results therefore demonstrate the value of formal allocation rules in balancing employment effectiveness, fiscal sustainability, and institutional feasibility. In short, occupational allocation effects reveal that workforce transition outcomes depend less on how many workers are trained and more on how training capacity is distributed across occupational categories. 6.4 Temporal Stress Concentration The cohort-phasing design generates the highest fiscal pressure in 2027, when 60% of trainees are scheduled. In both scenarios, support-budget deficits peak in this year, indicating that temporal concentration of training cohorts amplifies short-term fiscal stress even when total training budgets remain sufficient. This result highlights a critical temporal dimension of workforce planning: fiscal feasibility is governed not only by total expenditure levels but also by intertemporal distribution of commitments. When training intakes are clustered around commissioning milestones, support systems—such as mentoring, placement facilitation, and employer coordination—experience congestion effects that magnify marginal costs. The temporal stress pattern also explains why training-budget surpluses coexist with support-budget deficits. Training delivery capacity can be expanded relatively smoothly across years, whereas support capacity depends on institutional networks, employer participation, and supervisory resources that cannot be scaled instantaneously. As a result, support systems bind more tightly during peak cohort years. From a policy standpoint, this finding implies that cohort timing should be treated as a fiscal-smoothing instrument rather than as a purely operational scheduling decision. Modest reallocation of cohorts across years, or early pre-commissioning support preparation, can substantially reduce peak fiscal stress without altering total training volumes. Moreover, temporal stress concentration introduces governance risk. If fiscal constraints force abrupt reductions in support activities during peak years, the effectiveness of training investments deteriorates precisely when labour demand is highest. This mismatch can generate short-term labour shortages, delayed commissioning, or safety vulnerabilities, undermining the strategic objectives of infrastructure expansion. In summary, temporal analysis demonstrates that workforce transition feasibility is governed not only by how much is spent, but also by when support obligations materialize. Table 2 Enrollment and Budget Fit by Scenario and Year Scenario Year Enrollments Training spends (THB m) Training budget (THB m) Training gap Support spends (THB m) Support budget (THB m) Support gap Base rail (15%) 2026 485 41.3 59.0 + 17.7 11.2 10.0 −1.2 2027 727 61.9 59.0 −2.9 16.8 12.0 −4.8 High rail (25%) 2026 536 45.8 59.0 + 13.2 12.4 10.0 −2.4 2027 805 68.7 59.0 −9.7 18.5 12.0 −6.5 6.5 Result Synthesis Three key findings emerge. Training budgets are sufficient, but support budgets are binding. Training delivery alone does not guarantee workforce transition; mentoring, placement, and employer coordination represent the dominant fiscal bottlenecks. Modal-shift scenarios reshape skill composition rather than headcount. Rail-intensive futures demand more complex occupational mixes, validating the two-stage sizing–optimization framework. Temporal design matters as much as total volume. Cohort concentration can generate fiscal stress even when aggregate budgets appear adequate. Together, these results demonstrate that workforce reskilling finance is governed by structural allocation and timing effects rather than by headline budget size. These findings provide the empirical foundation for the governance and policy interpretation developed in the following Discussion section. 7. Discussion The results demonstrate that Thailand can support the Laem Chabang Phase 3 workforce transition within the current two-year training envelope, but only by accepting persistent pressure on support budgets. This distinction is critical: numerical adequacy of training seats does not automatically translate into effective workforce transition. The discussion therefore emphasizes governance structure, fiscal composition, and institutional coordination rather than training volume alone. 7.1 Support Costs as the Hidden Bottleneck Consistent with international port-labour evidence, automation and modal shift alter workforce composition more than total employment size (Decas & Kailas, 2019 ; De Bari et al., 2024). Rail-yard, automation, and mechatronics occupations require intensive mentoring, workplace integration, and employer coordination—activities financed primarily through support budgets rather than training budgets. The model shows that when support financing is constrained, training investments lose effectiveness. This finding reinforces the interpretation of support expenditure as a productivity-enabling investment rather than a social add-on. Underestimation of this dimension helps explain why many reskilling programs achieve high completion rates but limited employment impact (Velmurugan et al., 2025). 7.2 Spatial Implications and Corridor Balance Although the EEC absorbs most throughput growth, the Bangkok corridor remains a substantial workforce absorber. The results therefore support a dual-corridor perspective of Thailand’s logistics labour system rather than an EEC-centric narrative. This spatial balance aligns with prior observations that inland container depots and dry ports continue to function as critical labour anchors even when deep-sea investments concentrate on coastal hubs. Workforce strategies that neglect inland corridors risk creating regional skill mismatches and weakening system-wide resilience (Balliauw, 2019). 7.3 Policy Levers for Closing the Support Gap The optimization results identify three policy levers capable of reducing support-budget stress without undermining workforce objectives. First, selective relaxation of coverage targets. Moderate reductions in coverage requirements for the most support-intensive occupations significantly ease fiscal pressure while preserving overall workforce adequacy. Second, employer co-funding of support activities. Within levy-based training systems, large employers can be incentivized or required to co-finance mentoring, placement, and on-the-job guidance, aligning financial responsibility with labour beneficiaries (Taneja et al., 2011 ). Third, green-taxonomy-aligned financing. Classifying rail-linked and low-carbon logistics training pathways as sustainability-aligned enables access to climate-oriented funding streams that can cover marginal support costs determining employment effectiveness rather than course delivery alone (Shen et al., 2025 ). Together, these levers demonstrate that workforce reskilling finance is not a binary budget problem but a governance design problem. 7.4 Workforce Reskilling as a Governance Challenge The findings reframe port workforce reskilling as an institutional coordination challenge rather than a technical training exercise. Infrastructure systems cannot perform optimally if workforce systems evolve more slowly than physical and digital assets. The Laem Chabang case illustrates that workforce transition must be treated as an integral component of infrastructure governance. Without this integration, smart-port investments risk generating capability asymmetries that constrain operational performance and social legitimacy (Chalaemwongwan et al., 2025 ). 7.5 Discussion Synthesis Overall, the findings indicate that Thailand’s port workforce transition is governed primarily by: fiscal structure more than fiscal volume; allocation design more than enrollment totals; and institutional coordination more than technological readiness. These insights confirm the value of embedding workforce planning and training finance within formal optimization and governance frameworks. 8. Conclusion, Limitations, and Future Research 8.1 Conclusion This study develops and applies an integrated, policy-constrained framework for workforce planning and training-finance optimization in the context of Laem Chabang Port’s Phase 3 expansion. By linking throughput and modal-split targets with region- and occupation-specific workforce demand and embedding these requirements within a mixed-integer linear programming model, the analysis demonstrates that workforce reskilling feasibility is governed primarily by fiscal structure, temporal alignment, and governance design rather than by aggregate training capacity alone. The results indicate that approximately 1,212–1,341 trainees are required over 2026–2027 to cover 80% of incremental labour demand under 15%–25% rail-share scenarios. Although training budgets remain sufficient across scenarios, support budgets consistently emerge as the binding constraint. This finding highlights that effective workforce transition depends less on training volume than on the institutional capacity to finance mentoring, placement facilitation, and employer coordination. The analysis further shows that targeted governance instruments—selective coverage adjustment, employer co-funding of support activities, and sustainability-aligned finance—can alleviate these constraints without undermining modal-shift, equity, or spatial-development objectives. Beyond its empirical application, the proposed framework offers a transferable decision-support approach for aligning human-capital investment with infrastructure development in emerging-economy port systems. By formalizing workforce planning as an optimization problem embedded within policy and fiscal constraints, the study advances port governance toward greater transparency, auditability, and implementation realism. 8.2 Limitations Several limitations of this study should be acknowledged. First, labour-intensity coefficients and rail-related multipliers are derived from international studies and planning benchmarks rather than from Thai time-and-motion observations. While this approach enhances cross-country comparability, it may not fully capture local operational idiosyncrasies. Second, placement probabilities are treated as exogenous parameters. In practice, large-scale training expansion may itself influence labour-market absorption dynamics through employer behaviour, wage adjustments, and institutional learning effects. These feedback mechanisms are not endogenized in the present model. Third, technology-adoption trajectories are assumed to evolve smoothly and are not modelled dynamically. Accelerated automation or unexpected digital leapfrogging could therefore shift occupational demand toward mechatronics and data-analytics roles earlier than projected. Accordingly, the numerical results should be interpreted as scenario-consistent policy estimates rather than as precise forecasts. Nevertheless, the structural insights concerning allocation efficiency, temporal stress concentration, and fiscal binding patterns are robust to these limitations and remain highly relevant for governance-oriented workforce planning. 8.3 Future Research Future research can extend the proposed framework in several directions. First, Thai-specific productivity and labour-elasticity parameters should be estimated by terminal type and technology configuration to improve empirical grounding and reduce reliance on international benchmarks. Such estimates would enable finer calibration of occupation-specific demand under alternative automation pathways. Second, stochastic or robust optimization formulations should be developed to explicitly capture uncertainty in training budgets, placement probabilities, and infrastructure commissioning schedules. Incorporating uncertainty would enhance the framework’s applicability for policy environments characterized by fiscal volatility and phased project implementation. Third, alternative spatial configurations should be explored as logistics activity expands beyond the Eastern Economic Corridor and Bangkok corridors. Extending the model to multi-node or networked logistics systems would allow assessment of workforce transition dynamics under decentralization and regional diversification scenarios. Further work may also incorporate firm-level behavioural responses, including training substitution, labour poaching, retention strategies, and wage competition, to better approximate real-world labour-market dynamics and employer incentives. Overall, the Laem Chabang Phase 3 case underscores that port development is as much a human-capital transition challenge as it is an infrastructure investment. Physical assets, digital systems, and environmental performance ultimately depend on institutional capacity to steer workforce transformation in an equitable, coordinated, and fiscally sustainable manner. Embedding workforce reskilling finance within formal planning and optimization frameworks is therefore not merely a technical extension, but a governance necessity for ports seeking long-term competitiveness and social legitimacy in the era of smart and sustainable logistics. Declarations Author Contribution T.K. conceptualized the study, developed the research design, and led the construction of the optimization model. S.N. collected policy documents, compiled regional labour and training datasets, and supported the development of the analytical framework. J.B. conducted the mathematical calibration, performed scenario simulations, and validated the model outputs. T.K. and J.B. co-wrote the main manuscript text, while S.N. prepared Tables and Figures, formatted the references, and conducted consistency checks. All authors reviewed, revised, and approved the final manuscript. Acknowledgement The authors would like to thank the local port authorities, public agencies, and training institutions that provided access to policy documents and publicly available datasets used in this study. We also appreciate the administrative support received during data compilation and model preparation. Data Availability This study uses publicly available secondary data obtained from the National Economic and Social Development Council (NESDC), the Ministry of Labour, the Ministry of Transport, and the Port Authority of Thailand. All datasets used in the analysis are accessible through their respective open-data portals and official publications. Model-generated outputs from the optimization simulations are not publicly deposited due to file-size constraints but are available from the corresponding author upon reasonable request. Ethical Approval This article does not contain any studies with human participants performed by any of the authors. Informed Consent This article does not contain any studies involving human participants, and therefore informed consent was not required. References Aiello G, Salah Abusohyon IA, Quaranta S, Marcon G (2024) Conceptualization and design of a digital twin for industrial logistic systems: An application in the shipbuilding industry. In: Handbook of Digital Twins. CRC Press, Boca Raton, FL, pp 515–530. https://doi.org/10.1201/9781003425724-36 Balliauw M, Meersman H, Van de Voorde E, Vanelslander T (2019) Towards improved port capacity investment decisions under uncertainty: A real options approach. 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Vongurai R (2025) Examining organizational and human drivers of artificial intelligence adoption for enhancing operational performance in Thai technology enterprises. J Behav Sci 20(2):1–14. Whalley J, Ziderman A (1990) Financing training in developing countries: The role of payroll taxes. Econ Educ Rev 9(4):377–387. https://doi.org/10.1016/0272-7757(90)90019-2 Wiguna IPA, Rohman MA, Prihartanto E (2025) Review of port infrastructure resilience with mathematical modelling and a bibliometric approach. Int J GEOMATE 29(134):41–58. https://doi.org/10.21660/2025.134.4933 Yaldir Valdivia Navarro B, Sebastian Lazo Levano C (2025) Assessment of ergonomic evaluation methods focused on MSD for employees in industrial companies: A systematic literature review. 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Introduction","content":"\u003cp\u003eLaem Chabang Port serves as Thailand\u0026rsquo;s principal deep-sea gateway and a strategic pillar of the Eastern Economic Corridor (EEC). Phase 3 of its expansion is projected to increase annual container throughput from approximately 11 to 18\u0026nbsp;million TEU while simultaneously strengthening rail-based hinterland connectivity. Whereas port development in emerging economies has traditionally emphasized physical infrastructure and dredging capacity, contemporary planning literature increasingly recognizes that infrastructure performance is critically mediated by workforce capability, institutional coordination, and adaptive governance structures (Taneja, Ligteringen, \u0026amp; Walker, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Eskafi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobal port systems are undergoing automation, digitalization, and organizational restructuring. These transformations reshape labour markets by displacing routine manual tasks while increasing demand for advanced analytical, technical, and system-integration skills. Port workers are now required to operate not only physical equipment but also digital twins, decision-support systems, and integrated logistics platforms, indicating that workforce capability should be conceptualized as a form of strategic infrastructure rather than as a peripheral human-resource concern (Decas \u0026amp; Kailas, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; De Bari et al., 2024).\u003c/p\u003e \u003cp\u003eAt the organizational level, digital transformation further requires transferable skills, continuous learning capacity, and adaptive problem-solving competencies. Empirical evidence consistently shows that reskilling and upskilling are essential for maintaining operational resilience under conditions of technological disruption. Without systematic workforce-development strategies, automation investments risk generating skill bottlenecks, operational inefficiencies, and safety vulnerabilities (Velmurugan et al., 2025).\u003c/p\u003e \u003cp\u003eThailand\u0026rsquo;s maritime and logistics sectors face additional institutional challenges. Digital technology adoption in Thai maritime enterprises remains uneven and is strongly influenced by organizational readiness, leadership commitment, and workforce competence. Similar patterns are observed in broader Thai industrial contexts, where competency frameworks for digital workforces reveal persistent gaps between policy ambition and operational capability (Janmethakulwat \u0026amp; Thanasopon, 2023; Chalaemwongwan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a sustainability perspective, port operations are increasingly expected to align with environmental, social, and governance (ESG) standards. Green-finance taxonomies and sustainability frameworks imply that port workforces must also acquire \u0026ldquo;green skills,\u0026rdquo; including knowledge of low-carbon logistics, environmental compliance, and sustainability reporting. However, existing workforce development studies rarely translate these sustainability objectives into concrete, occupation-linked training pathways that can be selectively financed and implemented.\u003c/p\u003e \u003cp\u003eDespite the growing recognition of these issues, port workforce planning remains fragmented. Existing studies often address automation, digitalization, training finance, or sustainability in isolation. Few provide integrated frameworks that link throughput expansion, modal shift, workforce reskilling, and training finance within a unified analytical structure. As a result, workforce planning frequently remains disconnected from infrastructure commissioning schedules, fiscal constraints, and institutional policy instruments.\u003c/p\u003e \u003cp\u003eThis study addresses these limitations by proposing a policy-constrained, optimization-based framework tailored to Laem Chabang Phase 3. By embedding workforce reskilling and training finance directly within port capacity expansion planning, the framework seeks to enhance the auditability, coherence, and inclusiveness of Thailand\u0026rsquo;s logistics investments. In doing so, the study positions workforce reskilling not merely as a supportive activity, but as a core governance mechanism for achieving sustainable and resilient port development.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Port Automation and Workforce Transformation\u003c/h2\u003e \u003cp\u003ePort automation has fundamentally reshaped the structure of port labour markets. Automation and digitalization progressively displace routine manual tasks while increasing demand for advanced technical, analytical, and system-integration skills. Evidence from automated and semi-automated terminals indicates that workers must increasingly combine mechanical expertise with digital literacy, troubleshooting competence, and systems thinking (Shovkovyy \u0026amp; Miri, 2023; Wiguna et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese transformations rarely result in simple workforce reduction. Instead, they generate occupational recomposition, in which traditional operational roles decline while specialized maintenance, coordination, and data-oriented roles expand. Such recomposition alters skill structures more than total employment volume and requires continuous reskilling rather than one-off training interventions (Lu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tatar, Ayvaz, \u0026amp; Pamucar, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImportantly, automation is not solely a technological process but also a socio-organizational transition. Successful implementation depends on worker acceptance, organizational learning capacity, and institutional alignment. Ports that integrate reskilling strategies into automation planning achieve smoother transitions and superior long-term operational performance (Vongurai, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Digitalization, Cyber-Physical Systems, and Skill Requirements\u003c/h2\u003e \u003cp\u003eThe emergence of cyber-physical port systems\u0026mdash;including digital twins, AI-supported scheduling, and IoT-enabled monitoring\u0026mdash;has further elevated workforce skill requirements. Workers must interpret complex data streams, interact with intelligent systems, and respond effectively to real-time operational disruptions (Shovkovyy \u0026amp; Miri, 2023; Toygar, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDigital workforce studies consistently highlight gaps between technological capability and human readiness. Even when advanced systems are available, effective utilization depends on confidence, problem-solving ability, and cross-functional communication skills (Rikharom \u0026amp; Chansanam, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Senarak, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This reinforces the argument that digital transformation is constrained more by human systems than by hardware availability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Training Finance and Institutional Mechanisms\u003c/h2\u003e \u003cp\u003eTraining-finance institutions play a decisive role in shaping workforce transformation. Levy\u0026ndash;exemption systems, public co-financing, and employer participation mechanisms are widely applied to correct under-investment in transferable skills and to align training supply with labour-market demand (de Langen, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Balliauw, 2019).\u003c/p\u003e \u003cp\u003eHowever, empirical evidence shows that training finance often prioritizes course delivery while underestimating post-training support, placement facilitation, and employer coordination. As a result, training completion does not consistently translate into employment outcomes, weakening the return on workforce-development investment (Hamzah et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, training finance is rarely integrated into formal workforce-planning models. Most studies continue to treat finance as an external constraint rather than as a strategic decision variable, limiting the operational relevance of existing frameworks for large infrastructure projects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sustainability, Green Skills, and Port Workforces\u003c/h2\u003e \u003cp\u003eSustainability agendas increasingly influence port development strategies. Low-carbon logistics, rail integration, and energy-efficient terminal operations require new categories of \u0026ldquo;green skills,\u0026rdquo; including environmental compliance, emissions monitoring, and sustainability reporting competencies (Shen et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Vongurai, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile sustainability frameworks emphasize technological solutions, workforce implications remain underexplored. Few studies specify how sustainability objectives translate into concrete occupational training pathways or how such pathways can be financed through green-aligned funding instruments. Consequently, sustainability discourse remains weakly connected to human-capital investment planning in port systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Workforce Planning Models and Optimization Approaches\u003c/h2\u003e \u003cp\u003eQuantitative workforce-planning models increasingly employ optimization techniques to balance cost efficiency, skill coverage, and institutional feasibility. These approaches demonstrate that formal optimization significantly improves allocation efficiency compared with ad hoc planning practices (Balliauw, 2019; Tatar et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, most existing models focus on either operational logistics or human-resource planning in isolation. Integrated applications that simultaneously incorporate port-capacity expansion, modal-shift policies, training finance, and workforce reskilling remain rare. Furthermore, equity considerations, provider capacity, and sustainability conditions are often treated qualitatively rather than as formal constraints, limiting their applicability for policy design in emerging-economy contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Literature Synthesis\u003c/h2\u003e \u003cp\u003eTaken together, the literature yields four consistent insights:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePort automation and digitalization fundamentally reshape workforce skill requirements.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWorkforce transformation depends more on reskilling systems than on technology availability.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTraining finance and institutional mechanisms critically shape workforce outcomes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSustainability objectives remain weakly integrated into workforce planning models.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eDespite these advances, existing studies remain fragmented. Few provide integrated frameworks that translate infrastructure expansion and modal-shift targets into occupation-specific workforce demand, link that demand to training-finance allocation, and evaluate outcomes under policy and fiscal constraints. This fragmentation motivates the integrated analytical framework proposed in the following sections.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Gaps and Contributions","content":"\u003cp\u003eSynthesizing the literature reviewed in Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, several critical gaps remain insufficiently addressed in ways that support implementable policy design for large-scale port expansion.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGap 1: Occupation\u0026ndash;Region\u0026ndash;Year Mapping\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAlthough automation and digitalization studies document broad shifts in workforce skill requirements, few translate throughput and modal-split targets into time-phased, occupation-specific labour demand disaggregated by region. Consequently, workforce planning often remains detached from the operational geography of port\u0026ndash;hinterland systems and from infrastructure commissioning timelines, limiting its policy relevance (Decas \u0026amp; Kailas, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGap 2: Integrated Funding and Policy Constraints\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhile training institutions and workforce development mechanisms are widely discussed, training finance is typically treated as an external constraint rather than as a decision variable within planning models. As a result, the interaction between funding sources, legal rules, and feasible training portfolios remains weakly formalized, reducing the operational applicability of existing frameworks (Taneja, Ligteringen, \u0026amp; Walker, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Eskafi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGap 3: Cohort Timing Relative to Commissioning Milestones\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDigital port transformation is inherently staged, yet few studies optimize the timing of training cohorts relative to infrastructure roll-out and operational transition points. This omission generates avoidable risks of short-term mismatches, including oversupply of trained labour before demand materializes or shortages at critical commissioning milestones (Iafelice, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGap 4: Equity and Provider Realism\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAlthough recent studies increasingly recognize the social dimensions of automation transitions, equity objectives and implementation realism\u0026mdash;such as completion rates, regional placement probabilities, provider capacity limits, and targeted support for disadvantaged groups\u0026mdash;are often treated descriptively rather than encoded as binding or penalized constraints. This limits the ability of models to produce actionable and auditable workforce-transition plans.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGap 5: Operationalization of Sustainability and Green Pathways\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSustainability frameworks emphasize low-carbon logistics and compliance requirements, yet few studies translate these objectives into concrete, occupation-linked training pathways that can be selectively financed through sustainability-aligned instruments. Consequently, \u0026ldquo;green skills\u0026rdquo; remain weakly connected to formal workforce-planning models in port contexts (Chalaemwongwan, Sanrach, \u0026amp; Silpcharu, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCollectively, these gaps indicate that existing research remains fragmented across technology, labour, finance, and sustainability domains and lacks an integrated decision-support approach suitable for infrastructure-led workforce transitions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eContributions of the Study\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo address these gaps, this study makes three contributions.\u003c/p\u003e \u003cp\u003eFirst, it derives year-by-year, occupation- and region-specific full-time-equivalent (FTE) workforce requirements for Laem Chabang Phase 3 under alternative rail-share scenarios, thereby linking infrastructure expansion targets with labour demand at policy-relevant resolution.\u003c/p\u003e \u003cp\u003eSecond, it formulates a policy-constrained mixed-integer linear programming (MILP) model that jointly optimizes training enrollments and financial allocations. The formulation explicitly incorporates funding envelopes, institutional rules, provider capacity constraints, equity floors, and sustainability-eligibility screens, aligning workforce planning with realistic governance conditions (Taneja et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Eskafi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThird, it generates implementable intake and funding plans for 2026\u0026ndash;2027 that are aligned with commissioning timelines and robust under alternative modal-split scenarios. This operational orientation advances workforce development from general strategic recommendations toward formally testable and auditable planning outputs.\u003c/p\u003e \u003cp\u003eOverall, the study reframes port workforce reskilling finance as a governance and optimization challenge embedded within infrastructure planning, offering an integrated approach to human-capital transition in emerging-economy port systems.\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cp\u003eThis study implements an integrated, two-stage analytical framework that links infrastructure-driven labour demand with policy-constrained training-finance optimization. The methodological design is motivated by prior evidence that fragmented workforce and training planning weakens the effectiveness of automation and digitalization strategies in port systems (Taneja, Ligteringen, \u0026amp; Walker, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Eskafi et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe framework consists of two interdependent modules: (i) a workforce sizing module that translates throughput and modal-split targets into occupation-specific labour demand by region; and (ii) a mixed-integer linear programming (MILP) model that allocates training enrollments and financial resources subject to fiscal, institutional, and equity constraints.\u003c/p\u003e\n\u003cp\u003eThis structure enables workforce demand and training supply to be analyzed within a unified, policy-consistent decision system.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Workforce Sizing Module\u003c/h2\u003e\n \u003cp\u003eThe workforce sizing module translates logistics performance targets into full-time-equivalent (FTE) labour requirements. This approach follows established logistics-planning literature that links throughput indicators with labour-intensity coefficients, ensuring that workforce demand is derived systematically from operational targets rather than from ad hoc staffing assumptions (Taneja et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Eskafi et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eLet \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{t}\\)\u003c/span\u003e\u003c/span\u003edenote total container throughput in year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e, and let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{t}\\)\u003c/span\u003e\u003c/span\u003erepresent the proportion of throughput transported by rail. For each occupation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003eand region \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e, baseline labour intensity is represented by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{ij}\\)\u003c/span\u003e\u003c/span\u003e(FTE per million TEU under road-dominant conditions), while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{ij}\\)\u003c/span\u003e\u003c/span\u003ecaptures incremental staffing intensity associated with rail-linked operations.\u003c/p\u003e\n \u003cp\u003eTarget labour demand is expressed as:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:{L}_{ijt}={Q}_{t}\\cdot\\:{s}_{jt}\\cdot\\:({\\alpha\\:}_{ij}+{\\beta\\:}_{ij}\\cdot\\:{r}_{t}),$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{s}_{jt}\\)\u003c/span\u003e\u003c/span\u003edenotes the regional share of total throughput in year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eIncremental labour demand is calculated as:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:{\\Delta\\:}{L}_{ijt}={L}_{ijt}-{L}_{ij0},$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{L}_{ij0}\\)\u003c/span\u003e\u003c/span\u003erepresents baseline labour demand under pre-expansion conditions.\u003c/p\u003e\n \u003cp\u003eThis formulation is consistent with empirical evidence demonstrating that modal shift and automation primarily reshape occupational composition and skill structures rather than proportionally increasing total headcount (Decas \u0026amp; Kailas, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; De Bari et al., 2024). The resulting labour-demand matrix provides a policy-relevant and occupation-specific input for the subsequent optimization module.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Training and Finance Optimization Model\u003c/h2\u003e\n \u003cp\u003eThe second module formulates workforce development as a policy-constrained MILP problem. Optimization approaches are widely applied in logistics and infrastructure planning to balance cost efficiency with multiple operational constraints (Eskafi et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and are increasingly recognized as suitable tools for workforce allocation under institutional rules (Taneja et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDecision Variables\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe main decision variables are:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{g,o,r,p,t}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e: number of trainees from group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:g\\)\u003c/span\u003e\u003c/span\u003e(e.g., youth, incumbents, displaced workers) trained for occupation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:o\\)\u003c/span\u003e\u003c/span\u003ein region \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003eby provider \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003ein year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{z}_{k,t}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e: expenditure from funding source \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003ein year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{\\xi\\:}_{j}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e: slack variables capturing deviations from policy or coverage targets.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u003cstrong\u003eObjective Function\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe objective is to maximize net social benefit (NSB), defined as expected lifetime wage uplift from successful trainees minus training and support costs and minus penalties for policy violations:\u003c/p\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:\\text{m}\\text{a}\\text{x}NSB=\\sum\\:_{g,o,r,p,t}{\\beta\\:}_{g,o,r}{\\pi\\:}_{g,o,r,p}{x}_{g,o,r,p,t}-\\sum\\:_{k,t}{c}_{k,t}{z}_{k,t}-\\sum\\:_{j}{\\gamma\\:}_{j}{\\xi\\:}_{j},$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003edenotes expected wage uplift, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pi\\:\\)\u003c/span\u003e\u003c/span\u003ecompletion\u0026ndash;placement probability, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\gamma\\:\\)\u003c/span\u003e\u003c/span\u003epenalty weights.\u003c/p\u003e\n \u003cp\u003eThis formulation reflects prior findings that workforce planning must balance economic returns with institutional feasibility and governance consistency (Velmurugan et al., 2025; Iafelice, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Constraint Structure\u003c/h2\u003e\n \u003cp\u003eThe model incorporates six categories of constraints:\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e4.3.1 Budget and Eligibility Constraints\u003c/strong\u003e: Training and support expenditures are limited by funding envelopes and eligibility rules, ensuring fiscal feasibility (Eskafi et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e4.3.2 Provider Capacity Constraints\u003c/strong\u003e: Training volumes cannot exceed provider capacity, reflecting institutional limitations of vocational and higher-education systems (Janmethakulwat \u0026amp; Thanasopon, 2023).\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e4.3.3 Demand Coverage Constraints\u003c/strong\u003e: A minimum fraction of incremental labour demand must be covered, linking workforce sizing with training allocation.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e4.3.4 Institutional Policy Constraints\u003c/strong\u003e: Levy\u0026ndash;exemption and firm-size rules are incorporated as binding or penalized constraints, reflecting training-finance governance structures (Taneja et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e4.3.5 Equity Constraints\u003c/strong\u003e: Minimum participation shares for priority groups and regions are imposed to prevent uneven workforce transition outcomes (Velmurugan et al., 2025).\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e4.3.6 Sustainability Eligibility Constraints\u003c/strong\u003e: Training programs eligible for green-aligned funding are restricted to sustainability-consistent occupational pathways (De Bari et al., 2024).\u003c/p\u003e\n \u003c/span\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Implementation and Transparency\u003c/h2\u003e\n \u003cp\u003eThe MILP model can be implemented using widely available platforms such as Python/Pyomo, Excel-based solvers, or AMPL. This flexibility is important for public agencies and regional institutions with heterogeneous analytical capacity (Eskafi et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAll parameters and assumptions are explicitly reported, enabling scenario testing, replication, and policy auditing. This transparency addresses a key limitation of prior workforce-planning studies, which often rely on opaque or non-transferable modeling structures.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.5 Methodological Positioning\u003c/h2\u003e\n \u003cp\u003eMethodologically, this study positions workforce reskilling not as a secondary human-resource activity but as an integral component of infrastructure system optimization. By embedding institutional, fiscal, and equity constraints directly into the model, the approach advances workforce planning from descriptive policy discussion toward formal, testable decision-support analysis.\u003c/p\u003e\n \u003cp\u003eThis methodological foundation enables the empirical calibration and scenario evaluation presented in the following section.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Data and Calibration","content":"\u003cp\u003eThe model is calibrated for Laem Chabang Phase 3 using publicly available planning assumptions and internationally validated parameter ranges. Consistent with infrastructure\u0026ndash;workforce planning practice, calibration prioritizes transparency, replicability, and scenario consistency rather than point forecasting (Taneja et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Eskafi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Throughput and Modal-Split Scenarios\u003c/h2\u003e \u003cp\u003eBaseline throughput is set at 11\u0026nbsp;million TEU, with a commissioning target of 18\u0026nbsp;million TEU, yielding an incremental volume of 7\u0026nbsp;million TEU. Two modal-split scenarios are evaluated:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eScenario A (Base rail)\u003c/b\u003e: 15% rail share\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eScenario B (High rail)\u003c/b\u003e: 25% rail share\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese values reflect policy ambitions for rail-oriented logistics transition and are consistent with international intermodalization and decarbonization benchmarks (De Bari et al., 2024; Decas \u0026amp; Kailas, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThroughput is allocated 65% to the Eastern Economic Corridor (EEC) and 35% to the Bangkok corridor, reflecting the continued operational importance of inland container depots and dry ports in Thailand\u0026rsquo;s logistics system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Cohort Phasing and Workforce Dynamics\u003c/h2\u003e \u003cp\u003eTraining cohorts are phased to mirror infrastructure roll-out, with 40% of enrollments scheduled in 2026 and 60% in 2027. This back-loaded structure reflects empirical evidence that labour demand accelerates during late commissioning and stabilization phases of large infrastructure projects (Taneja et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Iafelice, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis temporal calibration acknowledges that premature training may lead to placement friction, while delayed training risks operational bottlenecks. The phased approach therefore balances readiness with absorption capacity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Occupational Input Parameters\u003c/h2\u003e \u003cp\u003eFor each occupation\u0026ndash;region\u0026ndash;provider combination, the following parameters are specified:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eUnit training cost\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSupport and placement cost\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCompletion probability\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePlacement probability\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExpected wage uplift\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003ePlacement probabilities are assumed to be higher in the EEC than in the Bangkok corridor, reflecting spatial concentration of port-related employment opportunities. Parameter ranges are informed by prior workforce-development and digital-skills studies that highlight systematic variation by occupation and provider type (Velmurugan et al., 2025).\u003c/p\u003e \u003cp\u003eFor niche rail-yard and automation-related roles with limited Thai historical data, probabilities are imputed from comparable mechatronics and digital-maintenance occupations, consistent with standard practice in workforce forecasting (Eskafi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Decas \u0026amp; Kailas, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll monetary values are expressed in constant 2026 Thai Baht.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Fiscal Envelopes and Policy Anchors\u003c/h2\u003e \u003cp\u003eAnnual training budgets are fixed at 59\u0026nbsp;million THB for both 2026 and 2027. Support budgets are set at 10\u0026nbsp;million THB in 2026 and 12\u0026nbsp;million THB in 2027. These envelopes reflect realistic public-finance constraints observed in vocational and sectoral training programs (Taneja et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Eskafi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTwo policy anchors are incorporated:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAt least 50% of publicly funded training seats must be linked to firms employing 100 or more workers;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStandard attendance and course-hour regulations apply to all trainees.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese anchors reflect institutional rules governing levy\u0026ndash;exemption and firm-based training participation in developing-economy contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Coverage Target and Scenario Logic\u003c/h2\u003e \u003cp\u003eA coverage parameter of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\theta\\:=0.80\\)\u003c/span\u003e\u003c/span\u003eis adopted as the base requirement, meaning that the training system must cover at least 80% of incremental FTE demand derived from throughput expansion.\u003c/p\u003e \u003cp\u003eThis threshold reflects a pragmatic balance between fiscal realism and operational sufficiency. Prior workforce-planning studies suggest that attempting to fully cover incremental demand through public training systems often leads to inefficiency and crowding-out of private training initiatives (Velmurugan et al., 2025; Iafelice, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Calibration Summary\u003c/h2\u003e \u003cp\u003eTo ensure clarity and replicability, the core calibration assumptions applied in the workforce-sizing and optimization modules are consolidated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The table summarizes throughput targets, modal-split scenarios, regional allocation rules, cohort phasing, fiscal envelopes, and policy anchors that jointly define the empirical environment of the model.\u003c/p\u003e \u003cp\u003eThese parameters are not intended as point forecasts but as scenario-consistent policy benchmarks, enabling transparent interpretation of the optimization results and facilitating sensitivity analysis across alternative infrastructure and governance conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData and Scenario Calibration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription / Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThroughput baseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026nbsp;million TEU\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThroughput target\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026nbsp;million TEU\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncremental volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔTEU\u0026thinsp;=\u0026thinsp;7 million\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRail share\u0026thinsp;=\u0026thinsp;15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRail share\u0026thinsp;=\u0026thinsp;25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional allocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65% EEC / 35% Bangkok corridor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohort phasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2026: 40%; 2027: 60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation inputs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit costs; completion \u0026amp; placement rates; wage uplift\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining budget\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u0026nbsp;million THB/year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupport budget\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026nbsp;million THB (2026); 12\u0026nbsp;million THB (2027)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolicy anchors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50% seats in firms\u0026thinsp;\u0026ge;\u0026thinsp;100 employees\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoverage target\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\theta\\:=0.80\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.7 Section Synthesis\u003c/h2\u003e \u003cp\u003eThis calibration strategy ensures that model outputs are driven by realistic institutional and fiscal conditions rather than by purely technical optimization. The design therefore supports policy-relevant interpretation while maintaining analytical transparency.\u003c/p\u003e \u003cp\u003eThe calibrated parameters provide the empirical basis for the enrollment and budget outcomes presented in the following Results section.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Results","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Enrollment Requirements and Budget Fit\u003c/h2\u003e \u003cp\u003eUnder the base coverage target (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\theta\\:=0.80\\)\u003c/span\u003e\u003c/span\u003e), the model yields distinct enrollment and budget profiles across rail-share scenarios.\u003c/p\u003e \u003cp\u003eIn Scenario A (15% rail share), the system requires approximately 1,212 enrollments over 2026\u0026ndash;2027. In Scenario B (25% rail share), total enrollments increase to approximately 1,341, reflecting the higher labour intensity of rail-linked and automation-intensive occupations.\u003c/p\u003e \u003cp\u003eAcross both scenarios, labor demand concentrates in three occupational clusters:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eRail-yard and intermodal operations;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAutomation and mechatronics maintenance; and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eScheduling and data-oriented coordination roles.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese results confirm that modal shift and automation do not simply change total labour volume but systematically reallocate demand toward higher-support, higher-complexity occupations, consistent with empirical evidence from automated terminal systems (Decas \u0026amp; Kailas, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; De Bari et al., 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Budget Binding Patterns\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports enrollment and budget outcomes by scenario and year. Aggregated across the two-year planning horizon, clear and consistent binding patterns emerge.\u003c/p\u003e \u003cp\u003eUnder Scenario A (15% rail share), total training expenditures reach approximately 103.2\u0026nbsp;million THB against an available budget of 118.0\u0026nbsp;million THB, leaving an unutilized balance of about 14.8\u0026nbsp;million THB. In contrast, support expenditures amount to approximately 28.0\u0026nbsp;million THB against a budget ceiling of 22.0\u0026nbsp;million THB, resulting in a shortfall of roughly 6.0\u0026nbsp;million THB.\u003c/p\u003e \u003cp\u003eUnder Scenario B (25% rail share), training expenditures rise to approximately 114.5\u0026nbsp;million THB, leaving a smaller surplus of about 3.5\u0026nbsp;million THB. Support expenditures increase more sharply to approximately 30.9\u0026nbsp;million THB, generating a larger shortfall of about 8.9\u0026nbsp;million THB.\u003c/p\u003e \u003cp\u003eThese patterns demonstrate that support funding, rather than training funding, constitutes the binding constraint in all scenarios. Importantly, the magnitude of the support deficit increases with higher rail share, indicating that modal shift intensifies the support intensity of workforce transition.\u003c/p\u003e \u003cp\u003eFrom a structural perspective, this outcome reflects the different economic roles of training and support budgets. Training expenditures primarily finance course delivery and classroom capacity, which scale relatively smoothly with enrollment. Support expenditures, by contrast, finance mentoring, workplace integration, employer coordination, and placement facilitation\u0026mdash;activities that scale non-linearly with occupational complexity and technological sophistication. As automation and rail-linked operations expand, marginal trainees increasingly belong to high-support occupations, causing support budgets to bind earlier than training budgets.\u003c/p\u003e \u003cp\u003eThe presence of persistent training-budget surpluses alongside support-budget deficits further implies that workforce transition inefficiency does not arise from insufficient training volume but from misalignment in budget composition. In practical terms, additional training seats cannot be converted into effective employment outcomes once support capacity becomes saturated. Consequently, the effective marginal productivity of training investment declines when support budgets are binding.\u003c/p\u003e \u003cp\u003eThis binding pattern also explains why Scenario B, despite higher overall training utilization, exhibits greater fiscal stress. The shift toward rail-intensive and automation-linked occupations increases the proportion of trainees requiring individualized placement support, employer engagement, and extended onboarding assistance. Without proportional expansion of support funding, the system encounters diminishing returns to training expansion.\u003c/p\u003e \u003cp\u003eFrom a governance standpoint, these results indicate that workforce reskilling finance should be evaluated not as a single aggregate budget but as a two-component system with asymmetric binding behavior. Policies that focus solely on expanding training budgets risk overestimating transition feasibility and underestimating implementation risk.\u003c/p\u003e \u003cp\u003eIn summary, the budget-binding analysis reveals that:\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1) Training budgets are structurally non-binding across both scenarios.\u003c/h3\u003e\n\n\u003ch3\u003e2) Support budgets bind early and intensify with higher rail share.\u003c/h3\u003e\n\n\u003ch3\u003e3) Workforce transition feasibility is therefore governed by support capacity rather than training volume.\u003c/h3\u003e\n\u003cp\u003eThis insight provides the quantitative foundation for the policy discussion in the following section, where governance instruments for relieving support-budget pressure are examined.\u003c/p\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Occupational Allocation Effects\u003c/h2\u003e \u003cp\u003eRail-intensive and automation-linked occupations consistently receive higher training and support allocations in both scenarios. These occupations\u0026mdash;such as rail-yard coordination, automation maintenance, and mechatronics support\u0026mdash;exhibit higher per-capita support requirements due to complex workplace integration, safety certification, and employer coordination needs. In contrast, scheduling and data-coordination roles remain highly attractive in the optimal solution because of their favorable cost-to-placement ratios and lower support intensity.\u003c/p\u003e \u003cp\u003eThis allocation pattern confirms that the optimization model systematically prioritizes occupations that maximize employment impact per unit of fiscal expenditure while still satisfying coverage, equity, and sustainability constraints. Rather than expanding training uniformly across occupations, the model reallocates capacity toward roles that combine high placement probability with moderate support cost, thereby improving overall system efficiency.\u003c/p\u003e \u003cp\u003eImportantly, this outcome illustrates that workforce planning is fundamentally an allocation-efficiency problem rather than an aggregate training-volume problem. Even under identical budget envelopes, different occupational mixes can generate substantially different employment outcomes. Consequently, policy emphasis on total trainee numbers risks obscuring the more decisive question of \u003cem\u003ewhich occupations\u003c/em\u003e receive priority within constrained fiscal space.\u003c/p\u003e \u003cp\u003eFrom a governance perspective, this finding implies that occupational targeting should be treated as a core policy lever rather than as a technical by-product of training institutions. Failure to manage occupational allocation explicitly may lead to fiscal crowding by high-support occupations or, conversely, to underinvestment in strategically critical technical roles. The optimization results therefore demonstrate the value of formal allocation rules in balancing employment effectiveness, fiscal sustainability, and institutional feasibility.\u003c/p\u003e \u003cp\u003eIn short, occupational allocation effects reveal that workforce transition outcomes depend less on how many workers are trained and more on how training capacity is distributed across occupational categories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e6.4 Temporal Stress Concentration\u003c/h2\u003e \u003cp\u003eThe cohort-phasing design generates the highest fiscal pressure in 2027, when 60% of trainees are scheduled. In both scenarios, support-budget deficits peak in this year, indicating that temporal concentration of training cohorts amplifies short-term fiscal stress even when total training budgets remain sufficient.\u003c/p\u003e \u003cp\u003eThis result highlights a critical temporal dimension of workforce planning: fiscal feasibility is governed not only by total expenditure levels but also by intertemporal distribution of commitments. When training intakes are clustered around commissioning milestones, support systems\u0026mdash;such as mentoring, placement facilitation, and employer coordination\u0026mdash;experience congestion effects that magnify marginal costs.\u003c/p\u003e \u003cp\u003eThe temporal stress pattern also explains why training-budget surpluses coexist with support-budget deficits. Training delivery capacity can be expanded relatively smoothly across years, whereas support capacity depends on institutional networks, employer participation, and supervisory resources that cannot be scaled instantaneously. As a result, support systems bind more tightly during peak cohort years.\u003c/p\u003e \u003cp\u003eFrom a policy standpoint, this finding implies that cohort timing should be treated as a fiscal-smoothing instrument rather than as a purely operational scheduling decision. Modest reallocation of cohorts across years, or early pre-commissioning support preparation, can substantially reduce peak fiscal stress without altering total training volumes.\u003c/p\u003e \u003cp\u003eMoreover, temporal stress concentration introduces governance risk. If fiscal constraints force abrupt reductions in support activities during peak years, the effectiveness of training investments deteriorates precisely when labour demand is highest. This mismatch can generate short-term labour shortages, delayed commissioning, or safety vulnerabilities, undermining the strategic objectives of infrastructure expansion.\u003c/p\u003e \u003cp\u003eIn summary, temporal analysis demonstrates that workforce transition feasibility is governed not only by \u003cem\u003ehow much\u003c/em\u003e is spent, but also by \u003cem\u003ewhen\u003c/em\u003e support obligations materialize.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnrollment and Budget Fit by Scenario and Year\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnrollments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTraining spends (THB m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTraining budget (THB m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTraining gap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupport spends (THB m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupport budget (THB m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSupport gap\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase rail (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh rail (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e6.5 Result Synthesis\u003c/h2\u003e \u003cp\u003eThree key findings emerge.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTraining budgets are sufficient, but support budgets are binding. Training delivery alone does not guarantee workforce transition; mentoring, placement, and employer coordination represent the dominant fiscal bottlenecks.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eModal-shift scenarios reshape skill composition rather than headcount.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRail-intensive futures demand more complex occupational mixes, validating the two-stage sizing\u0026ndash;optimization framework.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTemporal design matters as much as total volume. Cohort concentration can generate fiscal stress even when aggregate budgets appear adequate.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTogether, these results demonstrate that workforce reskilling finance is governed by structural allocation and timing effects rather than by headline budget size.\u003c/p\u003e \u003cp\u003eThese findings provide the empirical foundation for the governance and policy interpretation developed in the following Discussion section.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Discussion","content":"\u003cp\u003eThe results demonstrate that Thailand can support the Laem Chabang Phase 3 workforce transition within the current two-year training envelope, but only by accepting persistent pressure on support budgets. This distinction is critical: numerical adequacy of training seats does not automatically translate into effective workforce transition. The discussion therefore emphasizes governance structure, fiscal composition, and institutional coordination rather than training volume alone.\u003c/p\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Support Costs as the Hidden Bottleneck\u003c/h2\u003e \u003cp\u003eConsistent with international port-labour evidence, automation and modal shift alter workforce composition more than total employment size (Decas \u0026amp; Kailas, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; De Bari et al., 2024). Rail-yard, automation, and mechatronics occupations require intensive mentoring, workplace integration, and employer coordination\u0026mdash;activities financed primarily through support budgets rather than training budgets.\u003c/p\u003e \u003cp\u003eThe model shows that when support financing is constrained, training investments lose effectiveness. This finding reinforces the interpretation of support expenditure as a productivity-enabling investment rather than a social add-on. Underestimation of this dimension helps explain why many reskilling programs achieve high completion rates but limited employment impact (Velmurugan et al., 2025).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Spatial Implications and Corridor Balance\u003c/h2\u003e \u003cp\u003eAlthough the EEC absorbs most throughput growth, the Bangkok corridor remains a substantial workforce absorber. The results therefore support a dual-corridor perspective of Thailand\u0026rsquo;s logistics labour system rather than an EEC-centric narrative.\u003c/p\u003e \u003cp\u003eThis spatial balance aligns with prior observations that inland container depots and dry ports continue to function as critical labour anchors even when deep-sea investments concentrate on coastal hubs. Workforce strategies that neglect inland corridors risk creating regional skill mismatches and weakening system-wide resilience (Balliauw, 2019).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e7.3 Policy Levers for Closing the Support Gap\u003c/h2\u003e \u003cp\u003eThe optimization results identify three policy levers capable of reducing support-budget stress without undermining workforce objectives.\u003c/p\u003e \u003cp\u003eFirst, selective relaxation of coverage targets. Moderate reductions in coverage requirements for the most support-intensive occupations significantly ease fiscal pressure while preserving overall workforce adequacy.\u003c/p\u003e \u003cp\u003eSecond, employer co-funding of support activities. Within levy-based training systems, large employers can be incentivized or required to co-finance mentoring, placement, and on-the-job guidance, aligning financial responsibility with labour beneficiaries (Taneja et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThird, green-taxonomy-aligned financing. Classifying rail-linked and low-carbon logistics training pathways as sustainability-aligned enables access to climate-oriented funding streams that can cover marginal support costs determining employment effectiveness rather than course delivery alone (Shen et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTogether, these levers demonstrate that workforce reskilling finance is not a binary budget problem but a governance design problem.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003e7.4 Workforce Reskilling as a Governance Challenge\u003c/h2\u003e \u003cp\u003eThe findings reframe port workforce reskilling as an institutional coordination challenge rather than a technical training exercise. Infrastructure systems cannot perform optimally if workforce systems evolve more slowly than physical and digital assets.\u003c/p\u003e \u003cp\u003eThe Laem Chabang case illustrates that workforce transition must be treated as an integral component of infrastructure governance. Without this integration, smart-port investments risk generating capability asymmetries that constrain operational performance and social legitimacy (Chalaemwongwan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003e7.5 Discussion Synthesis\u003c/h2\u003e \u003cp\u003eOverall, the findings indicate that Thailand\u0026rsquo;s port workforce transition is governed primarily by:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003efiscal structure more than fiscal volume;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eallocation design more than enrollment totals; and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003einstitutional coordination more than technological readiness.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese insights confirm the value of embedding workforce planning and training finance within formal optimization and governance frameworks.\u003c/p\u003e \u003c/div\u003e"},{"header":"8. Conclusion, Limitations, and Future Research","content":"\u003cdiv id=\"Sec40\" class=\"Section2\"\u003e \u003ch2\u003e8.1 Conclusion\u003c/h2\u003e \u003cp\u003eThis study develops and applies an integrated, policy-constrained framework for workforce planning and training-finance optimization in the context of Laem Chabang Port\u0026rsquo;s Phase 3 expansion. By linking throughput and modal-split targets with region- and occupation-specific workforce demand and embedding these requirements within a mixed-integer linear programming model, the analysis demonstrates that workforce reskilling feasibility is governed primarily by fiscal structure, temporal alignment, and governance design rather than by aggregate training capacity alone.\u003c/p\u003e \u003cp\u003eThe results indicate that approximately 1,212\u0026ndash;1,341 trainees are required over 2026\u0026ndash;2027 to cover 80% of incremental labour demand under 15%\u0026ndash;25% rail-share scenarios. Although training budgets remain sufficient across scenarios, support budgets consistently emerge as the binding constraint. This finding highlights that effective workforce transition depends less on training volume than on the institutional capacity to finance mentoring, placement facilitation, and employer coordination. The analysis further shows that targeted governance instruments\u0026mdash;selective coverage adjustment, employer co-funding of support activities, and sustainability-aligned finance\u0026mdash;can alleviate these constraints without undermining modal-shift, equity, or spatial-development objectives.\u003c/p\u003e \u003cp\u003eBeyond its empirical application, the proposed framework offers a transferable decision-support approach for aligning human-capital investment with infrastructure development in emerging-economy port systems. By formalizing workforce planning as an optimization problem embedded within policy and fiscal constraints, the study advances port governance toward greater transparency, auditability, and implementation realism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section2\"\u003e \u003ch2\u003e8.2 Limitations\u003c/h2\u003e \u003cp\u003eSeveral limitations of this study should be acknowledged. First, labour-intensity coefficients and rail-related multipliers are derived from international studies and planning benchmarks rather than from Thai time-and-motion observations. While this approach enhances cross-country comparability, it may not fully capture local operational idiosyncrasies.\u003c/p\u003e \u003cp\u003eSecond, placement probabilities are treated as exogenous parameters. In practice, large-scale training expansion may itself influence labour-market absorption dynamics through employer behaviour, wage adjustments, and institutional learning effects. These feedback mechanisms are not endogenized in the present model.\u003c/p\u003e \u003cp\u003eThird, technology-adoption trajectories are assumed to evolve smoothly and are not modelled dynamically. Accelerated automation or unexpected digital leapfrogging could therefore shift occupational demand toward mechatronics and data-analytics roles earlier than projected.\u003c/p\u003e \u003cp\u003eAccordingly, the numerical results should be interpreted as scenario-consistent policy estimates rather than as precise forecasts. Nevertheless, the structural insights concerning allocation efficiency, temporal stress concentration, and fiscal binding patterns are robust to these limitations and remain highly relevant for governance-oriented workforce planning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec42\" class=\"Section2\"\u003e \u003ch2\u003e8.3 Future Research\u003c/h2\u003e \u003cp\u003eFuture research can extend the proposed framework in several directions. First, Thai-specific productivity and labour-elasticity parameters should be estimated by terminal type and technology configuration to improve empirical grounding and reduce reliance on international benchmarks. Such estimates would enable finer calibration of occupation-specific demand under alternative automation pathways.\u003c/p\u003e \u003cp\u003eSecond, stochastic or robust optimization formulations should be developed to explicitly capture uncertainty in training budgets, placement probabilities, and infrastructure commissioning schedules. Incorporating uncertainty would enhance the framework\u0026rsquo;s applicability for policy environments characterized by fiscal volatility and phased project implementation.\u003c/p\u003e \u003cp\u003eThird, alternative spatial configurations should be explored as logistics activity expands beyond the Eastern Economic Corridor and Bangkok corridors. Extending the model to multi-node or networked logistics systems would allow assessment of workforce transition dynamics under decentralization and regional diversification scenarios.\u003c/p\u003e \u003cp\u003eFurther work may also incorporate firm-level behavioural responses, including training substitution, labour poaching, retention strategies, and wage competition, to better approximate real-world labour-market dynamics and employer incentives.\u003c/p\u003e \u003cp\u003eOverall, the Laem Chabang Phase 3 case underscores that port development is as much a human-capital transition challenge as it is an infrastructure investment. Physical assets, digital systems, and environmental performance ultimately depend on institutional capacity to steer workforce transformation in an equitable, coordinated, and fiscally sustainable manner. Embedding workforce reskilling finance within formal planning and optimization frameworks is therefore not merely a technical extension, but a governance necessity for ports seeking long-term competitiveness and social legitimacy in the era of smart and sustainable logistics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.K. conceptualized the study, developed the research design, and led the construction of the optimization model. S.N. collected policy documents, compiled regional labour and training datasets, and supported the development of the analytical framework. J.B. conducted the mathematical calibration, performed scenario simulations, and validated the model outputs. T.K. and J.B. co-wrote the main manuscript text, while S.N. prepared Tables and Figures, formatted the references, and conducted consistency checks. All authors reviewed, revised, and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank the local port authorities, public agencies, and training institutions that provided access to policy documents and publicly available datasets used in this study. We also appreciate the administrative support received during data compilation and model preparation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis study uses publicly available secondary data obtained from the National Economic and Social Development Council (NESDC), the Ministry of Labour, the Ministry of Transport, and the Port Authority of Thailand. All datasets used in the analysis are accessible through their respective open-data portals and official publications. Model-generated outputs from the optimization simulations are not publicly deposited due to file-size constraints but are available from the corresponding author upon reasonable request.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This article does not contain any studies involving human participants, and therefore informed consent was not required.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAiello G, Salah Abusohyon IA, Quaranta S, Marcon G (2024) Conceptualization and design of a digital twin for industrial logistic systems: An application in the shipbuilding industry. In: Handbook of Digital Twins. CRC Press, Boca Raton, FL, pp 515\u0026ndash;530. https://doi.org/10.1201/9781003425724-36\u003c/li\u003e\n\u003cli\u003eBalliauw M, Meersman H, Van de Voorde E, Vanelslander T (2019) Towards improved port capacity investment decisions under uncertainty: A real options approach. Transp Rev 39(4):531\u0026ndash;552. https://doi.org/10.1080/01441647.2018.1556228\u003c/li\u003e\n\u003cli\u003eBullock GS, Dartt CE, Ricker EA, Fallowfield JL, Arden N, Clifton D, Danelson K, Fraser JJ, Gomez C, Greenlee TA, Gregory A, Gribbin T, Losciale J, Molloy JM, Nicholson KF, Polich J-G, R\u0026auml;is\u0026auml;nen A, Shah K, Smuda M, Rhon DI (2023) Barriers and facilitators to implementation of musculoskeletal injury mitigation programmes for military service members around the world: A scoping review. Inj Prev 29(6):461\u0026ndash;473. https://doi.org/10.1136/ip-2023-044905\u003c/li\u003e\n\u003cli\u003eChalaemwongwan N, Sanrach R, Silpcharu T (2025) A competency development framework for digital workforce in industrial business organizations. WSEAS Trans Bus Econ 22:1896\u0026ndash;1905. https://doi.org/10.37394/23207.2025.22.151\u003c/li\u003e\n\u003cli\u003eChunthasiri Y, Carr SC, Young-Hauser AM, Intarakamhang U (2023) Sustaining Thailand\u0026apos;s seafood processing sector: Workforce development. Humanit Arts Soc Sci Stud 23(1):220\u0026ndash;234.\u003c/li\u003e\n\u003cli\u003eCottrill K (1982) Practical training for Third World port managers. Dock Harbour Auth 63(743):216\u0026ndash;217.\u003c/li\u003e\n\u003cli\u003ede Langen PW (2008) Analysing training and education in ports. WMU J Marit Aff 7(1):5\u0026ndash;16. https://doi.org/10.1007/BF03195122\u003c/li\u003e\n\u003cli\u003eDecas K, Kailas A (2019) Strategies to prepare future port and intermodal workers for transformational technologies. In: Empowering the New Mobility Workforce: Educating, Training, and Inspiring Future Transportation Professionals. Elsevier, pp 79\u0026ndash;96. https://doi.org/10.1016/B978-0-12-816088-6.00004-3\u003c/li\u003e\n\u003cli\u003eEskafi M, Dastgheib A, Taneja P, Ulfarsson GF, Stefansson G, Thorarinsdottir RI (2021) Framework for dealing with uncertainty in the port planning process. J Waterw Port Coast Ocean Eng 147(3):05021003. https://doi.org/10.1061/(ASCE)WW.1943-5460.0000636\u003c/li\u003e\n\u003cli\u003eGalvao CB, Robles LT, Cutrim SS, Lamarca R (2025) Best practices in ESG: A benchmarking for Brazilian seaports sustainability journey. 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J Tech Educ Train 15(4):143\u0026ndash;153. https://doi.org/10.30880/jtet.2023.15.04.012\u003c/li\u003e\n\u003cli\u003eJoomjee R, Songserm N, Chada W, Turnbull N (2025) Impact of ergonomic interventions on musculoskeletal health among Thai para rubber workers: Evaluating workstation improvements. Int J Occup Saf Health 15(1):55\u0026ndash;66. https://doi.org/10.3126/ijosh.v15i1.66174\u003c/li\u003e\n\u003cli\u003eKoohathongsumrit N, Phakkulhab N, Kengpol A, Meethom W (2020) A survey of requirements for Thailand\u0026apos;s industry 4.0: The perspectives from academics and entrepreneurs. In: International Symposium on Project Approaches in Engineering Education, vol 10, pp 135\u0026ndash;142.\u003c/li\u003e\n\u003cli\u003eKordi A, Mohebbi K, Bararzadeh Ledari M, Shirafkan SM (2025) Transforming ports for a low-carbon future: Nexus modeling of hydrogen infrastructure, employment, and resource management in contrasting climates. Energy Nexus 19:100515. https://doi.org/10.1016/j.nexus.2025.100515\u003c/li\u003e\n\u003cli\u003eKosiyaporn H, Julchoo S, Phaiyarom M, Sinam P, Kunpeuk W, Pudpong N, Allotey P, Chan ZX, Loganathan T, Pocock N, Suphanchaimat R (2020) Strengthening the migrant-friendliness of Thai health services through interpretation and cultural mediation: A system analysis. Glob Health Res Policy 5(1):53. https://doi.org/10.1186/s41256-020-00181-0\u003c/li\u003e\n\u003cli\u003eLawanyawatna S, Schoch M (2024) Facility improvement in a Thai prison through decentralized management and alternative funding. Prison J 104(6):718\u0026ndash;739. https://doi.org/10.1177/00328855241286503\u003c/li\u003e\n\u003cli\u003eLu C-S, Lin C-C, Lee M-H (2010) An evaluation of container development strategies in the port of Taichung. 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Int J Adv Manuf Technol 131(11):5571\u0026ndash;5588. https://doi.org/10.1007/s00170-022-10625-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Laem Chabang, Eastern Economic Corridor, port workforce, reskilling, mixed-integer linear programming, train-or-pay, Thailand Taxonomy, green skills","lastPublishedDoi":"10.21203/rs.3.rs-8127684/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8127684/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePhase 3 of Laem Chabang Port will expand rated capacity from approximately 11 to 18\u0026nbsp;million TEU (twenty-foot equivalent units) and deepen rail connectivity within Thailand\u0026rsquo;s Eastern Economic Corridor (EEC). Realizing these gains depends not only on infrastructure but also on timely workforce reskilling and credible training-finance mechanisms. This paper develops a Thailand-specific framework that (i) translates throughput and rail-share targets into occupation- and region-specific staffing requirements and (ii) allocates multi-source training funds through a policy-constrained mixed-integer linear programming (MILP) model that respects Thailand\u0026rsquo;s Skill Development Promotion Act (\u0026ldquo;train-or-pay\u0026rdquo;), provider capacity, equity floors, and optional green-taxonomy eligibility. Using public parameters on costs, completion, and placement probabilities, and 2026\u0026ndash;2027 budget envelopes, the model indicates that covering 80% of the incremental staffing needs from the 11\u0026rarr;18\u0026nbsp;million TEU uplift requires approximately 1,212 enrollments under a 15% rail-share scenario and 1,341 enrollments under a 25% rail-share scenario. While training budgets are sufficient (showing small surpluses), support budgets for stipends, mentoring, and placement services are binding in all scenarios\u0026mdash;particularly in 2027, when 60% of cohorts enter. We derive an implementable intake and financing plan for the EEC\u0026ndash;Bangkok corridors and identify three policy levers\u0026mdash;adjusting coverage targets, co-funding support costs via large employers, and mobilizing green-taxonomy funds\u0026mdash;that can reconcile rail-intensive skill needs with fiscal constraints. The framework offers a practical template for embedding workforce planning and training finance into large-scale logistics investments in Thailand and comparable emerging-economy contexts.\u003c/p\u003e","manuscriptTitle":"From 11 to 18 Million TEU: Optimizing Port-Workforce Reskilling and Training Finance for Thailand’s Laem Chabang Phase 3","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 11:27:54","doi":"10.21203/rs.3.rs-8127684/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-16T10:05:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T16:34:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T01:53:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-18T14:31:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33126814365801042337146294288171231277","date":"2026-03-17T10:47:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161724589547522058708021643751160845608","date":"2026-03-16T13:29:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287296898496174489229619583458294138396","date":"2026-03-13T23:13:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-10T15:57:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-10T15:35:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-20T20:47:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-17T03:37:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-01-17T03:31:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e56f3ddb-5253-4100-9ca3-7fa7cd7a3329","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62889158,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":62889159,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-24T14:53:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 11:27:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8127684","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8127684","identity":"rs-8127684","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}