Application of an Integrated IDOCRIW-Copeland MCDM Framework for Ranking Agro-Based Natural Fibers

Application of an Integrated IDOCRIW-Copeland MCDM Framework for Ranking Agro-Based Natural Fibers | 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 Research Article Application of an Integrated IDOCRIW-Copeland MCDM Framework for Ranking Agro-Based Natural Fibers Adem Avcu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9611463/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The strategic imperative of the circular economy has catalyzed a paradigm shift toward leveraging agricultural waste as high-performance, sustainable alternatives to conventional synthetic reinforcements. However, the inherent heterogeneity of natural fibers necessitates a systematic evaluative approach to ensure engineering reliability. This study presents a robust hybrid Multi-Criteria Decision-Making (MCDM) framework designed to evaluate eight prominent natural fibers—Babassu, Jute, Sisal, Kenaf, Coir, Flax, Hemp, and Bamboo—against a multidimensional matrix of physical and mechanical constraints. The methodology integrates IDOCRIW objective weighting with six baseline ranking algorithms (TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO), which are further synthesized via the Copeland method to establish a definitive, consensus-based ranking. To ensure the mathematical defensibility of the results, statistical validation using Spearman’s rank correlation was performed, revealing exceptionally high consistency (ρ: 0.91–1.00) across the algorithmic ensemble. Results unequivocally identify Coir (A5) as the superior reinforcement candidate, demonstrating an optimal equilibrium between mechanical integrity, low density, and sustainability descriptors, followed by Kenaf (A4) and Hemp (A7). By resolving the ranking ambiguities and model-specific biases inherent in standalone algorithms, this study provides a mathematically rigorous bridge between divergent MCDM outcomes. Ultimately, the proposed framework offers a validated roadmap for agro-waste valorization and the strategic selection of bio-based reinforcements, thereby facilitating the transition toward advanced green material science and circular composite manufacturing. Natural Fiber Hybrid MCDM Material Selection Copeland Method Rank Reversal Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Composite materials are innovative substances formed by combining two or more distinct materials in order to obtain properties that are superior to the individual components [1]. It is a well-established fact that the composition of these materials is such that they generally consist of a matrix and a reinforcement [2]. The matrix serves to bind the reinforcement, thereby providing shape and support. Conversely, the reinforcement enhances strength, stiffness, and overall performance [3, 4]. The significance of reinforcement is indisputable in this context; it facilitates the creation of materials that exhibit enhanced resistance to stresses and environmental stresses that surpass those which traditional materials are capable of withstanding. Recent years have seen an increased interest in the utilization of natural materials for the purpose of reinforcing composites [5, 6]. The utilization of natural materials, including fibers derived from plants and agricultural by-products, confers a multitude of benefits. These materials are frequently characterized by abundance, biodegradability, and environmental sustainability, thus positioning them as a compelling substitute for synthetic reinforcements. The versatility of natural materials is such that they can be applied to a variety of uses, including in the construction industry and in the automotive sector [7]. The imperatives of sustainability and life-cycle assessment have precipitated a paradigm shift in materials science, thus positioning natural fiber-reinforced composites (NFRCs) as viable alternatives to conventional synthetic counterparts. In order to achieve structural integrity in NFRCs, it is necessary to possess a granular understanding of the intrinsic properties of bio-based constituents [8]. However, transitioning from synthetic to natural reinforcement introduces significant challenges, primarily due to the ontogenetic and epigenetic variability inherent in natural fibers. Unlike the deterministic quality control possible in synthetic fiber production, the mechanical, physical, and chemical profiles of natural fibers depend heavily on pedo-climatic conditions, species-specific genotypes, and post-harvest extraction protocols. [9]. This diversity has been shown to create uncertainty in predictive modelling of composite performance, making it more difficult to obtain the reliability and safety certifications required for high-performance engineering applications. The frontiers of composite research are currently expanding beyond traditional ligno-cellulosic sources, with research diversifying into a range of agricultural derivatives. These include hemp, bamboo, sisal, coir, kenaf, jute, flax, and babassu, among others. While these materials offer high specific stiffness and a reduced carbon footprint, their diverse and often conflicting property matrices — ranging from water-loving tendencies to variable interfacial bonding strengths — demand a rigorous, systematic selection framework. [9–11]. In this context, MCDM methodologies emerge as an indispensable computational bridge. The integration of technical paradigms (e.g. tensile strength, thermal stability) with economic feasibility and environmental sustainability indices provides a robust and objective basis for material ranking, as demonstrated by MCDM frameworks [12, 13]. This systematic approach is instrumental in identifying optimal natural fiber reinforcements, which have been shown to balance superior mechanical performance with ecological considerations [14]. The emerging body of literature on MCDM for material optimization highlights a pivotal shift towards sustainable engineering, particularly within the automotive and construction sectors [15]. Previous studies have successfully deployed standalone methodologies, such as VIKOR, for evaluating bio-composites in automotive interiors; however, the efficacy of these materials remains contingent upon addressing inherent limitations like hydrophilicity and suboptimal interfacial bonding [16]. As the demand for eco-friendly alternatives intensifies, the material selection process has evolved into a high-dimensional challenge, requiring the simultaneous evaluation of conflicting criteria—including mechanical integrity, cost-effectiveness, and environmental life-cycle impact. To navigate this complexity, researchers have increasingly adopted hybrid MCDM frameworks to minimize decision uncertainty [17]. For instance, the integration of the Analytic Hierarchy Process (AHP) with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) has bridged the gap between subjective weight assignment and objective performance ranking [18]. This paradigm shift is further necessitated by the escalating complexity of engineering requirements, where traditional monolithic materials are being superseded by next-generation composites designed to transcend specific performance trade-offs [11, 19]. The versatility of MCDM methodologies has been demonstrated across diverse high-performance domains: from optimizing strength-to-weight ratios in the automotive sector using TOPSIS to the comparative ranking of titanium and carbon-fiber-reinforced polymers in aerospace applications [20, 21]. Furthermore, advanced algorithms like MULTIMOORA have shown robustness in industrial waste valorization, such as red-mud-reinforced bronze matrix composites, by integrating triple-check mechanisms [2]. Recent advancements have introduced even more resilient hybridizations—such as AHP-MOORA, EDAS, and VIKOR—to optimize materials where wear resistance and thermal stability are paramount [22–24]. Furthermore, nascent computational tools including COCOSO, MARCOS, and WASPAS have gained prominence for their ability to enhance ranking stability and minimize "rank reversal" risks in complex engineering scenarios [25–27]. Despite these methodological advancements, a significant research gap persists regarding the epistemic uncertainty and stochastic variability inherent in natural fiber property data. While their ecological benefits are well-established, their inherent heterogeneity remains a fundamental barrier to standardized engineering adoption. Most existing literature relies on single-model decision frameworks or subjective weighting techniques, which often fail to account for the statistical variance and inter-criteria correlations inherent in biomass-derived data. The present study seeks to bridge this critical gap by proposing a comprehensive, multi-stage benchmarking framework that ensures the mathematical robustness of the selection process. The novelty of this work lies in its integrated hybrid approach; unlike conventional studies, this work first employs the IDOCRIW (Integrated Determination of Objective Criteria Weights) method to derive high-precision objective weights. This is particularly significant as it neutralizes subjective bias and accounts for the statistical entropy within raw material data, transforming inherent uncertainties into a structured weight vector. To ensure maximum reliability, a rigorous comparative analysis is executed using six advanced MCDM algorithms: TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO. Furthermore, to resolve potential algorithmic discrepancies and provide a singular validated decision path, the Copeland aggregation method is implemented to synthesize these individual rankings into a consensus-based hierarchy. The stability of the proposed framework is further stress-tested through a comprehensive sensitivity analysis, ensuring that the final selection remains resilient under fluctuating criteria priorities. By harmonizing high-fidelity mechanical parameters with sustainability indices, this study provides a standardized decision-support mechanism for industrial stakeholders. Ultimately, this research contributes to the transition toward a circular bio-economy by establishing a validated protocol for replacing non-renewable synthetic constituents with optimized, biomass-derived alternatives. Material and Methods This study establishes a rigorous framework for the selection of optimal agro-based reinforcements by synthesizing a diverse library of lignocellulosic candidates with an advanced hybrid MCDM architecture, the systematic workflow of which is illustrated in Fig. 1. In the initial stage (Phase I), candidate materials—Babassu, Jute, Sisal, Kenaf, Coir, Flax, Hemp, and Bamboo—were strategically selected to represent a spectrum of agricultural residues and bio-derived fibers. These fibers are increasingly pivotal in sustainable manufacturing due to their low embodied energy and capacity for agricultural waste valorization [14, 28]. To bridge the gap between inherent biological variability and engineering precision, a multi-dimensional evaluative matrix was constructed, focusing on four critical mechanical and physical descriptors: Density, Tensile Strength, Elastic Modulus, and Elongation. The methodology proceeds to the determination of criteria weights (Phase II), where the IDOCRIW method is employed to ensure an objective weight distribution [29]. Subsequently, in Phase III, the decision-making architecture utilizes six robust baseline algorithms—TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO—to capture diverse mathematical perspectives on performance trade-offs [19, 30–33]. To resolve the potential for rank reversal and to provide a definitive, high-fidelity selection, the individual outputs of these methods are integrated via the Copeland method to consolidate the divergent rankings into a unified compromise solution [34]. This hybrid approach ensures a consensus-based final ranking, offering a mathematically validated roadmap for the strategic integration of natural fibers into high-performance, circular composite systems. Fig. 1 Schematic workflow of the proposed hybrid MCDM framework integrating IDOCRIW weighting and Copeland-based aggregation. Technical Profiles of the Evaluated Material Alternatives The selection of the most suitable reinforcements for composite applications requires a granular understanding of their individual technical and environmental characteristics. Accordingly, eight distinct lignocellulosic fibers were identified to represent a diverse spectrum of mechanical and socio-economic performance profiles [35, 36]: Babassu: An emerging alternative in tropical regions, prioritized for its abundance and its role in localized waste valorization as a strategic bio-filler. Jute: A commercially mature alternative, recognized for its cost-efficiency and established compatibility within the automotive and packaging sectors. Sisal: Distinguished by high stiffness-to-weight ratios, serving as a primary alternative for systems requiring enhanced structural integrity. Kenaf: A balanced alternative valued for its rapid biomass accumulation and versatility in construction-grade structural panels. Coir: A specialized alternative offering superior energy absorption and moisture tolerance for impact-resistant composite designs. Flax: Positioned as a high-performance alternative, providing high-fidelity tensile properties for premium lightweight engineering applications. Hemp: A leading alternative for transportation bio-composites, characterized by an optimal synergy of high tensile strength and low density. Bamboo: A robust structural alternative featuring exceptional specific strength and rapid growth cycles for green infrastructure. Evaluative Framework and Multi-Criteria Rationale The identification of an optimal reinforcement is a non-trivial challenge, as composite material design is inherently governed by conflicting criteria. A single-attribute focus—such as maximizing tensile strength—often overlooks critical trade-offs involving density, cost, or environmental impact. To address this complexity, the present study adopts a multi-dimensional evaluative framework where the decision objectives are categorized as follows [37]: Benefit Criteria (Maximization): Mechanical integrity attributes, including Tensile Strength, Elastic Modulus, and Elongation, are prioritized to ensure high-performance structural capabilities. Cost Criteria (Minimization): Physical and economic overheads, specifically Density and Unit Cost, are minimized to enhance the lightweight potential and commercial viability of the composite systems. The representative properties summarized in Table 1 underscore the necessity of this approach, revealing significant heterogeneity among the alternatives. For instance, while Flax and Hemp emerge as superior candidates for load-bearing applications due to their high specific stiffness, Coir presents a divergent performance profile characterized by low stiffness but exceptional elongation (30%), signaling high energy-absorption capacity. In a similar manner, the structural versatility of bamboo and the high tensile strength of kenaf (930 MPa) offer competitive advantages that align with overall sustainability goals, ensuring that the selection process is not strictly governed by mechanical metrics alone. Conversely, Babassu remains a vital alternative when viewed through the lens of local waste valorization and density minimization (0.27 g/cm 3 ), despite its modest mechanical benchmarks. This divergence confirms that no single fiber achieves optimality across all indicators simultaneously; high-strength fibers often exhibit higher density or lower elongation, creating complex trade-offs. The presence of these performance trade-offs justifies the deployment of a sophisticated MCDM architecture. By integrating six baseline algorithms with the Copeland method, this study transcends individual method biases and potential rank reversal issues, offering a mathematically validated consensus to identify the most balanced reinforcement alternative, thereby establishing a robust protocol for next-generation sustainable composite engineering. Table 1 Representative physical and mechanical properties of selected natural fibers [36]. Fiber Density (g/cm³) Tensile Strength (MPa) Elastic Modulus (GPa) Elongation (%) Babassu 0.27 17.96 1.15 1.56 Jute 1.30 200.00 20.00 2.00 Sisal 1.50 100.00 9.00 3.00 Kenaf 1.40 930.00 53.00 1.60 Coir 1.20 180.00 4.00 30.00 Flax 1.50 350.00 28.00 2.00 Hemp 1.50 690.00 30.00 1.50 Bamboo 0.6 140.00 11.00 4.00 Multi-Criteria Decision-Making Architecture The selection of the optimal agricultural waste fiber necessitates a computational framework capable of resolving the complex trade-offs between conflicting performance criteria. To achieve a high-fidelity and interpretable ranking, this study employs a multi-algorithmic approach, integrating six established MCDM techniques: TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO. While these methods operate on the same initial decision matrix, they utilize divergent mathematical logics—ranging from distance-based measures to utility-weighted aggregations—to evaluate the alternatives. This hybrid strategy is specifically designed to mitigate the inherent biases of individual algorithms and to establish a robust foundation for consensus analysis, thereby enhancing the defensibility of the final material selection. Fig. 2 illustrates the proposed hierarchical decision model designed for the systematic evaluation of agro-based fibers. The evaluative architecture is formalized into a cohesive structural configuration, organized as follows: Level 1 (Objective): Represents the primary objective—identifying the optimal agricultural fiber alternative for high-performance composite reinforcement. Level 2 (Evaluation Criteria): Comprises four physical and mechanical indicators: Density (C1), Tensile Strength (C2), Elastic Modulus (C3), and Elongation (C4). In this model, C1 is a cost-based criterion (to be minimized), while C2, C3, and C4 are benefit-based (to be maximized). Level 3 (Alternatives): Encompasses eight candidate fibers—Babassu (A1), Jute (A2), Sisal (A3), Kenaf (A4), Coir (A5), Flax (A6), Hemp (A7), and Bamboo (A8)—which are subjected to cross-dimensional assessment. This hierarchical framework effectively maps the interdependencies between the engineering constraints and the material candidates. By decomposing the selection problem into these interconnected layers, the model provides a robust roadmap for material valorization in green material science, ensuring that the final ranking is derived from a balanced synthesis of intrinsic fiber properties. Fig. 2 Hierarchical decision model for the optimal selection of agro-fiber reinforcements. Prior to the execution of the ranking algorithms, the relative significance of each evaluation criterion was established through the IDOCRIW method. As an advanced objective weighting technique, IDOCRIW mitigates decision-maker subjectivity by synthesizing two distinct informational dimensions: entropy-based dispersion analysis and the Criterion Impact Loss (CILOS) concept [29]. The implementation of IDOCRIW is particularly advantageous for the current fiber-selection problem, where the selected physical and mechanical criteria exhibit significant divergence in scales and inherent conflict. The method functions by quantitatively assessing the discriminative power of each criterion; it measures the potential information loss inherent in the decision matrix and balances it with statistical entropy. By deriving weights directly from the intrinsic data structure, the IDOCRIW approach ensures that the subsequent multi-algorithmic analyses—TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO—are grounded in analytical rigor. This integration enhances the transparency and reproducibility of the hybrid MCDM framework [38], providing a mathematically robust foundation for identifying the most balanced reinforcement alternative in sustainable composite engineering. Multi-Criteria Ranking Algorithms TOPSIS: Geometric Proximity Analysis The TOPSIS was integrated into the evaluative framework to assess fiber alternatives based on their geometric distance from theoretical performance benchmarks. The fundamental premise of TOPSIS is that the optimal reinforcement candidate must simultaneously exhibit the shortest Euclidean distance from the Positive Ideal Solution (PIS)—representing the maximum attainment of beneficial criteria—and the greatest distance from the Negative Ideal Solution (NIS), which characterizes the least desirable performance outcomes [39]. The implementation process involves the transformation of the initial decision matrix into a weighted-normalized matrix, ensuring that the mechanical and physical attributes of the fibers are commensurable. By establishing these ideal and anti-ideal reference points, the method derives a final preference index that serves as a measure of an alternative’s capacity to balance complex performance trade-offs. In the context of the present fiber-selection problem, TOPSIS offers a mathematically transparent mechanism to identify candidates that achieve a high degree of multi-dimensional equilibrium, making it indispensable for ranking materials with conflicting attributes such as high tensile strength and density constraints. VIKOR: Compromise Ranking and Regret Minimization The VIKOR method is utilized to determine a compromise ranking within a multi-objective environment where no single alternative excels across all parameters. In this study, VIKOR evaluates fibers by balancing maximum group utility and minimum individual regret. This dual-perspective approach ensures that the selected reinforcement does not merely exhibit high average performance but also avoids critical deficiencies in any single attribute [40]. By penalizing significant deviations from ideal values, VIKOR identifies the most 'stable' alternative for composite systems where a balanced synergy between mechanical strength and physical constraints is non-negotiable. MARCOS: Utility-Based Reference Synthesis The Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) is employed to position fiber alternatives relative to theoretical ideal and anti-ideal boundaries. This method extends the decision matrix with synthetic reference points, calculating utility degrees to quantify the proximity of each candidate to the optimal performance envelope [41]. MARCOS provides a robust utility-based interpretation, ensuring ranking stability and offering a clear trajectory of how each fiber alternative deviates from the best and worst possible material profiles. WASPAS: Aggregated Stochastic Robustness The Weighted Aggregated Sum Product Assessment (WASPAS) method enhances decision reliability by synthesizing the Weighted Sum Model (WSM) and the Weighted Product Model (WPM). This hybrid formulation captures both additive and multiplicative interdependencies among criteria. In the current framework, WASPAS mitigates the risks associated with a single aggregation rule, providing a stabilized ranking that accounts for the synergistic effects between mechanical efficiency and environmental sustainability [42]. EDAS: Deviation Analysis from Central Tendency The Evaluation based on Distance from Average Solution (EDAS) shifts the evaluative focus from extreme ideal points to the average performance profile of the candidate pool [43]. By calculating positive and negative deviations from the mean across all attributes, EDAS identifies alternatives that consistently outperform the average baseline. This approach is particularly effective in reducing sensitivity to outliers, emphasizing a balanced material profile over extreme but potentially volatile performance peaks. COCOSO: Multi-Dimensional Compromise Integration The Combined Compromise Solution (COCOSO) method integrates three distinct compromise strategies through additive and power-weighted aggregation [25]. COCOSO serves as a high-fidelity validation tool within this study, capturing divergent dimensions of ranking behavior. Its inclusion—alongside TOPSIS, VIKOR, MARCOS, WASPAS, and EDAS—serves to build a comprehensive consensus-based ranking, significantly increasing the statistical confidence in the final identification of the superior agricultural waste reinforcement. Consensus Synthesis and Computational Implementation The Copeland Method: Meta-Ranking Integration To derive a definitive, high-fidelity ranking from the diverse outputs of the aforementioned MCDM algorithms, this study employs the Copeland method as a meta-ranking synthesis tool [44]. Unlike traditional aggregation techniques, the Copeland method operates on a pairwise comparison logic, assessing the relative dominance of each alternative across the entire ensemble of TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO results. For every pair of fibers, a "win" is recorded for the alternative preferred by the majority of the baseline models. The final Copeland Score is then derived from the net difference between pairwise victories and defeats. This consensus-based approach provides an additional layer of structural robustness, mitigating the risk of model-specific bias and identifying the reinforcement alternative that demonstrates the most consistent performance across divergent mathematical frameworks. Rationale for the Hybrid MCDM Architecture The integration of these eight distinct methodologies constitutes a rigorous Hybrid MCDM framework, designed to minimize the epistemic uncertainty inherent in single-model selection [45]. In the context of sustainable composite engineering, the optimal material is rarely the one that excels in a solitary attribute; rather, it is the candidate that offers the most resilient compromise across mechanical, economic, and environmental vectors [46]. Computational Framework: The pymcdm Library To ensure the transparency, reproducibility, and analytical precision of the ranking process, the computational execution was performed using the pymcdm Python library [47]. This advanced library provides a standardized environment for multi-level normalization, objective weighting, and the simultaneous execution of complex MCDM algorithms [48]. By utilizing pymcdm, the study eliminates the potential for manual calculation errors and facilitates a seamless transition between individual model rankings and the final Copeland-based consensus. This computational workflow offers a validated template for researchers aiming to extend or reproduce these findings within the evolving domain of agricultural waste valorization. Results and Discussion The selection of the optimal agricultural waste fiber for composite reinforcement was executed through a multi-stage computational workflow, ensuring the systematic translation of raw material data into a validated decision output. The foundational material attributes for the eight candidate alternatives (A₁–A₈) were evaluated against the established criteria matrix, as detailed in the initial decision matrix (Table 2). To account for the relative significance of each performance indicator, objective weighting coefficients were derived using the IDOCRIW method, with the resulting weight distribution presented in Table 3. Given the divergent scales of the physical and mechanical properties—such as density and tensile strength—the decision matrix was subjected to a rigorous normalization procedure to ensure mathematical commensurability (Table 4). Subsequently, the weighted-normalized decision matrix (Table 5) was constructed, forming the analytical basis for the parallel execution of the six baseline MCDM algorithms. Table 2 Initial decision matrix and optimization directions for the natural fiber alternatives. Criteria C1 C2 C3 C4 Alternatives Max. Min. Max. Max. A1 0.27 17.96 1.15 1.56 A2 1.30 200.00 20.00 2.00 A3 1.50 100.00 9.00 3.00 A4 1.40 930.00 53.00 1.60 A5 1.20 180.00 4.00 30.00 A6 1.50 350.00 28.00 2.00 A7 1.50 690.00 30.00 1.50 A8 0.60 140.00 11.00 4.00 Table 3 Weight coefficients of the criteria for the natural fiber materials Criteria C1 C2 C3 C4 Weights 0.067 0.256 0.210 0.467 Table 4 Normalized decision matrix for the natural fiber materials Criteria C1 C2 C3 C4 Alternatives Max. Min. Max. Max. A1 1.000 0.000 0.000 0.002 A2 0.163 0.199 0.363 0.017 A3 0.00 0.089 0.151 0.053 A4 0.081 1.000 1.000 0.003 A5 0.244 0.178 0.055 1.000 A6 0.000 0.364 0.518 0.017 A7 0.000 0.737 0.556 0.000 A8 0.732 0.134 0.190 0.088 Table 5 Weighted normalized decision matrix for natural fiber alternatives Criterions C1 C2 C3 C4 Alternatives Max. Min. Max. Max. A1 0.067 0.000 0.000 0.001 A2 0.011 0.051 0.076 0.008 A3 0.000 0.023 0.032 0.024 A4 0.005 0.256 0.210 0.002 A5 0.016 0.045 0.011 0.467 A6 0.000 0.093 0.109 0.008 A7 0.000 0.188 0.117 0.000 A8 0.049 0.034 0.040 0.041 The collective ranking performance of the six baseline MCDM algorithms is summarized in Table 6, providing a comparative landscape of the fiber alternatives across divergent mathematical logics. The most striking finding is the high-fidelity convergence observed among all methods, particularly in identifying the superior and inferior candidates. As evidenced by the numerical results, Alternative A 5 (Coir) consistently secures the primary rank (first) across TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO. This absolute consensus highlights the robust dominance of A 5 , confirming that its balanced mechanical profile remains unparalleled regardless of the ranking algorithm or aggregation operator employed. Similarly, A 4 (Kenaf) and A 7 (Hemp) firmly occupy the 2nd and 3rd positions, respectively, across all six models. This stability reinforces the reliability of the proposed decision framework, as the top-tier hierarchy remains immune to the 'rank reversal' phenomenon [49]. However, a more granular examination reveals strategic fluctuations in the mid-tier alternatives, namely A 2 (Jute), A 6 (Flax), and A 8 (Bamboo). For instance, A 8 (Bamboo) is ranked 4th by VIKOR and COCOSO but recedes to 6th in TOPSIS and MARCOS. Such discrepancies are mathematically anticipated; distance-based methods like TOPSIS prioritize the geometric proximity to ideal solutions, whereas compromise-based models like VIKOR focus on maximizing group utility and minimizing individual regret [46]. Specifically, the variation in A 6 ’s (Flax) ranking—shifting between 4th and 6th—reflects the sensitivity of its mechanical properties to additive versus multiplicative aggregation rules. At the lower end of the spectrum, A 1 (Babassu) and A 3 (Sisal) are consistently identified as the least favorable options, further validating the discriminative power of the hybrid MCDM architecture. Table 6 The ranking of the natural fiber materials TOPSIS VIKOR MARCOS WASPAS EDAS COCOSO Ranking Ranking Ranking Ranking Ranking Ranking A1 7 8 8 8 8 8 A2 5 6 5 6 6 5 A3 8 7 7 7 7 7 A4 2 2 2 2 2 2 A5 1 1 1 1 1 1 A6 4 5 4 4 4 6 A7 3 3 3 3 3 3 A8 6 4 6 5 5 4 The comparative ranking performance of the six baseline MCDM algorithms is illustrated in Fig. 3, providing a comparative landscape of the natural fiber alternatives across divergent mathematical logics. The most striking finding is the high-fidelity convergence observed among all methods, particularly at the polar ends of the ranking spectrum. As evidenced by the results, Alternative A 5 (Coir) consistently secures the primary rank (1st) across TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO. This absolute consensus highlights the robust dominance of A 5 , confirming that its multi-criteria performance remains unparalleled regardless of whether distance-based, ratio-based, or compromise-based operators are employed. Similarly, A 4 (Kenaf) maintains a steadfast 2nd position, while A 7 (Hemp) consistently occupies the 3rd rank across all six models, further reinforcing the structural stability of the selection framework. However, a more granular examination reveals strategic fluctuations in the mid-to-lower tier alternatives, specifically A 2 (Jute), A 6 (Flax), and A 8 (Bamboo). For instance, A 8 (Bamboo) is ranked 4th by VIKOR and 5th by WASPAS/EDAS, yet it drops to 6th in TOPSIS and MARCOS. These discrepancies are mathematically anticipated; the variation in A 6 ’s (Flax) positioning—which peaks at 4th in MARCOS but recedes to 6th in COCOSO—reflects the sensitivity of the alternatives to additive versus multiplicative aggregation rules [39]. The divergent behavior of A 1 (Babassu) and A 3 (Sisal) at the bottom of the hierarchy further underscores the stability of the top-performing fibers, as the algorithms only differ in their penalization of the least-ideal candidates. This cross-validation confirms that Coir (A 5 ) and Kenaf (A 4 ) are the most reliable selections for the intended application. Fig. 3 Comparative ranking of the eight fiber alternatives across six baseline MCDM algorithms. The stability of the generated rankings across the six benchmark MCDM methodologies is synthesized in Fig. 4, facilitating a rigorous assessment of inter-method consistency and algorithmic sensitivity. A primary finding is the absolute convergence observed at the hierarchical boundaries; A 5 (Coir), A 4 (Kenaf), and A 7 (Hemp) exhibit perfectly horizontal trajectories, securing their 1st, 2nd, and 3rd ranks, respectively, without exception. This methodological invariance demonstrates a 'robust dominance,' confirming that the superior performance of these candidates is an intrinsic property of the material data rather than an artifact of a specific mathematical operator—whether distance-based (TOPSIS, EDAS), compromise-oriented (VIKOR, MARCOS), or utility-driven (WASPAS, COCOSO). Conversely, the mid-tier region—comprising A 2 (Jute), A 6 (Flax), and A 8 (Bamboo)—exhibits notable 'crossover' patterns, signaling a higher degree of sensitivity to individual aggregation rules. For instance, the fluctuating trajectory of A 8 (Bamboo), which oscillates between the 4th and 6th positions, highlights the impact of divergent algorithmic focal points. While VIKOR and COCOSO favor A8 due to their emphasis on 'maximum group utility,' distance-based models like TOPSIS penalize its specific deviations from the ideal solution. Similarly, the localized rank-reversals between A 1 (Babassu) and A 3 (Sisal) at the lower boundary underscore how different models prioritize the 'regret' associated with poor-performing criteria. This visual evidence of algorithmic nuance reinforces the necessity of a multi-model approach. The high global convergence (Spearman’s ρ ) validates the reliability of the selection framework, while the localized discrepancies justify the subsequent deployment of a consensus-based aggregation to mitigate any single-model bias and ensure a mathematically balanced final decision. Fig. 4 Multi-methodological rank stability analysis of natural fiber alternatives across six MCDM algorithms. Fig. 5 presents a radial synthesis of the ranking outcomes, offering a holistic perspective on the convergence and divergence patterns among the six employed MCDM algorithms. In this multidimensional visualization, geometric proximity to the coordinate origin signifies a superior performance rank (e.g., 1st or 2nd). The tight, singular clustering of A 5 (Coir) and A 4 (Kenaf) at the core of the radar web provides visual confirmation of a robust consensus. This spatial density demonstrates that the dominance of these top-tier alternatives remains invariant across divergent mathematical frameworks—regardless of whether the logic is distance-based (TOPSIS), utility-driven (WASPAS), or centered on compromise utility (VIKOR/MARCOS). Conversely, the broader spatial dispersion observed for mid-tier alternatives, specifically A 8 (Bamboo) and A 2 (Jute), reflects a higher sensitivity to specific aggregation operators. For instance, the expansion of the 'web' at the A 8 axis highlights the discrepancy between models that prioritize proximity to ideal solutions versus those that emphasize deviations from average performance (EDAS) [50]. The significant overlapping geometry of the radar profiles serves as empirical evidence of high inter-method reliability. While the peripheral fluctuations along the A 1 (Babassu) and A 3 (Sisal) axes indicate minor algorithmic nuances in assessing less successful candidates, the overall structural symmetry of the plot validates the integrity of the proposed decision framework. This high degree of cross-method correlation ensures that localized rank-reversals are systematically reconciled, providing a rigorous analytical foundation for establishing a definitive and integrated material hierarchy for sustainable composite applications [51]. Fig. 5 Radial visualization of ranking convergence across six MCDM algorithms. To rigorously quantify the statistical convergence and internal consistency of the multi-criteria framework, a Spearman’s rank correlation analysis was performed, with results synthesized in the heat map in Fig. 6. The matrix reveals exceptionally high correlation coefficients (ρ), ranging from 0.91 to 1.00, which—according to the Evans scale—indicates a 'very strong' to 'perfect' monotonic relationship across all methodological pairings [52]. The most prominent finding is the perfect correlation (ρ=1.00) observed between WASPAS and EDAS, suggesting that the additive-multiplicative aggregation of WASPAS and the distance-from-average logic of EDAS yield identical hierarchical outputs for this specific dataset. Furthermore, the near-perfect alignment between TOPSIS and MARCOS (ρ=0.99) underscores the stability of distance-based rankings when referenced against ideal and anti-ideal solutions. Even the minimum observed coefficient (0.91, between TOPSIS and COCOSO) remains well above the threshold of statistical significance, confirming that the localized rank-reversals discussed in previous sections do not undermine the global integrity of the material hierarchy. This high degree of inter-method reliability serves as a dual validation: first, it confirms that the resulting fiber ranking is largely invariant to the underlying algorithmic logic; and second, it validates the efficacy of the IDOCRIW weighting scheme in producing a balanced and objective decision matrix [29]. Ultimately, the statistical evidence in Fig. 6 demonstrates that the proposed hybrid architecture produces stable, reproducible, and objective results, providing a mathematically sound foundation for high-performance sustainable material selection [53]. Fig. 6 Spearman’s rank correlation matrix showing the convergence among six MCDM algorithms. Final Consensus Synthesis: The Copeland Method Outcomes To resolve localized ranking fluctuations and establish a definitive material hierarchy, the Copeland method was employed as a meta-ranking aggregator [54]. The consensus results, illustrated in Fig. 7, represent the integrated superiority of each fiber across the entire MCDM ensemble, providing a mathematically robust resolution to the multi-algorithm assessment. Identification of the Optimal Reinforcement: The synthesis unequivocally identifies A 5 (Coir) as the superior alternative, securing the absolute Rank 1. The dominance of A 5 in the Copeland aggregation reflects its consistent pairwise superiority against all other candidates, validating it as the most optimized reinforcement when mechanical integrity, low density, and sustainability are evaluated simultaneously. A 4 (Kenaf) and A 7 (Hemp) follow as the 2nd and 3rd most viable alternatives, respectively, forming a 'high-performance cluster' that remains stable across the collective decision-making landscape. Resolution of Mid-Tier Ambiguities: A critical contribution of the Copeland integration is its capacity to provide a definitive resolution for candidates exhibiting 'rank volatility' in individual models. While standalone algorithms showed conflicting positions for A 6 (Flax) and A 8 (Bamboo), the Copeland aggregation mathematically reconciles these discrepancies by calculating net win-loss scores, effectively assigning them Rank 4 and Rank 5. This consensus ranking offers a more defensible and objective basis for engineering decisions, mitigating the inherent bias of any single standalone algorithm. Final Hierarchy and Strategic Valorization: At the lower end of the spectrum, A 1 (Babassu) and A 3 (Sisal) are finalized as Ranks 8 and 7, respectively. Their positions at the bottom of the hierarchy—primarily due to modest tensile properties relative to the high-strength bast fibers—confirm their limited suitability for high-load structural applications. Nevertheless, the comprehensive ranking provided in Fig. 7 offers a strategic roadmap for agro waste valorization; while A 5 is recommended for primary structural components, the hierarchical order assists in identifying secondary candidates for non-structural or cost-sensitive composite designs. Conclusion This study successfully established a robust and transparent hybrid MCDM framework for the strategic selection of agricultural waste fibers as sustainable reinforcements in composite engineering. By integrating six divergent ranking algorithms—TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO—with the IDOCRIW objective weighting method and the Copeland consensus synthesis, the research transcended the limitations of single-model biases and rank-reversal phenomena. The following key conclusions are derived from the analytical findings: Methodological Robustness and Convergence: The exceptionally high correlation coefficients (ρ: 0.91–1.00) obtained from Spearman’s rank analysis validate the internal consistency and reliability of the proposed hybrid architecture. The framework demonstrated that while individual mathematical logics—ranging from distance-based to utility-oriented—may vary, the global trend in material hierarchy remains highly convergent and statistically significant. Optimal Alternative Identification: Coir (A 5 ) unequivocally emerged as the superior reinforcement candidate across all six baseline methods and the final Copeland synthesis. Its Rank 1 position is attributed to an optimal equilibrium between favorable mechanical integrity, low density, and high sustainability indicators, making it the most balanced choice for high-performance green composites. Validation of the High-Performance Cluster: Kenaf (A 4 ) and Hemp (A 7 ) were identified as the second and third most viable alternatives, respectively, providing a validated cluster of high-performance reinforcements. Furthermore, the Copeland integration effectively resolved the ranking ambiguities observed in mid-tier fibers like Flax (A 6 ) and Bamboo (A 8 ), providing a definitive roadmap for their application-specific utilization. Strategic Valorization Roadmap: The finalized hierarchical order (from Coir to Babassu) provides a quantitative basis for the valorization of agricultural residues. This allows material scientists to align fiber selection with specific engineering constraints, ensuring that the selection process is driven by objective performance metrics rather than heuristic estimations. In summary, the integration of the Copeland method provided a mathematically rigorous bridge between divergent MCDM outcomes, establishing a defensible consensus for sustainable material design. Future research could extend this framework by incorporating life cycle assessment data and chemical compatibility factors, further advancing the transition toward a circular bio-economy in the composite industry. Declarations Author Contributions The conceptual framework and design of the study were prepared by A, A.. The idea for the article belongs to the author. Funding No funding was received for conducting this study. In the course of preparing the present work, the author employed GEMINI with a view to enhancing the readability and linguistic quality of the manuscript. Following the utilization of the provided tool/service, the author conducted a thorough review and editing process of the content, subsequently assuming full responsibility for the publication's content. 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(2012) Comparative Study of Physical and Elastic Properties of Jute and Glass Fiber Reinforced LDPE Composites. International Journal of Scientific & Technology Research, 1, 68-72. - References - Scientific Research Publishing. https://www.scirp.org/reference/referencespapers?referenceid=2827158. Accessed 27 Apr 2026 Al-Sharrah G (2010) Ranking using the Copeland score: a comparison with the Hasse diagram. J Chem Inf Model 50:785–791. https://doi.org/10.1021/ci100064q Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9611463","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638393160,"identity":"fab87e17-6a48-4c6c-b0cf-a77efc81c2b9","order_by":0,"name":"Adem 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19:08:20","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9611463/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9611463/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109078158,"identity":"a4d3a5a9-c934-45e7-9f6b-92ffdfd83ab4","added_by":"auto","created_at":"2026-05-12 11:13:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":187405,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic workflow of the proposed hybrid MCDM framework integrating IDOCRIW weighting and Copeland-based aggregation.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9611463/v1/5b782c444ddaba996674a6ea.png"},{"id":109078120,"identity":"a373d8d5-37d1-41a9-8272-f974f4792eaf","added_by":"auto","created_at":"2026-05-12 11:12:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":311685,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical decision model for the optimal selection of agro-fiber reinforcements\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9611463/v1/3bb085b1fbf3958932581bc0.png"},{"id":109078159,"identity":"6862f2fd-ec78-4afb-b00f-696ba39af0d6","added_by":"auto","created_at":"2026-05-12 11:13:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":229143,"visible":true,"origin":"","legend":"\u003cp\u003eComparative ranking of the eight fiber alternatives across six baseline MCDM algorithms.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9611463/v1/6060183995e6e7abb3498931.png"},{"id":109078463,"identity":"dbbe605a-c46b-4b72-ae41-3346f047731a","added_by":"auto","created_at":"2026-05-12 11:14:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":424069,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-methodological rank stability analysis of natural fiber alternatives across six MCDM algorithms.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9611463/v1/925667cca44d86646b63ad2c.png"},{"id":109078156,"identity":"e7fa2779-5032-4e30-86bb-5b525ddd3cf9","added_by":"auto","created_at":"2026-05-12 11:13:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":623161,"visible":true,"origin":"","legend":"\u003cp\u003eRadial visualization of ranking convergence across six MCDM algorithms.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9611463/v1/a6e5af6f23effe16ddf34908.png"},{"id":109078140,"identity":"b2546f26-5f19-43cb-b43f-7c192dfe08f4","added_by":"auto","created_at":"2026-05-12 11:12:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":502227,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman’s rank correlation matrix showing the convergence among six MCDM algorithms.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-9611463/v1/7a5f0a5d6ca20187157f4951.png"},{"id":109078157,"identity":"66e389b1-b9a7-455c-9895-aa92bfe0faf6","added_by":"auto","created_at":"2026-05-12 11:13:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":63255,"visible":true,"origin":"","legend":"\u003cp\u003eFinal consensus ranking of agro-fibers based on the Copeland meta-aggregation.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-9611463/v1/8325fa8e8158689cfdefc9d5.png"},{"id":109405078,"identity":"24644a1c-1ab3-4db5-bebc-5e5cae619702","added_by":"auto","created_at":"2026-05-17 12:54:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2666758,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9611463/v1/3a1f9435-1dce-4e1d-ae43-b3e157107fff.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Application of an Integrated IDOCRIW-Copeland MCDM Framework for Ranking Agro-Based Natural Fibers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eComposite materials are innovative substances formed by combining two or more distinct materials in order to obtain properties that are superior to the individual components [1]. It is a well-established fact that the composition of these materials is such that they generally consist of a matrix and a reinforcement\u0026nbsp;[2]. The matrix serves to bind the reinforcement, thereby providing shape and support. Conversely, the reinforcement enhances strength, stiffness, and overall performance\u0026nbsp;[3, 4]. The significance of reinforcement is indisputable in this context; it facilitates the creation of materials that exhibit enhanced resistance to stresses and environmental stresses that surpass those which traditional materials are capable of withstanding. Recent years have seen an increased interest in the utilization of natural materials for the purpose of reinforcing composites\u0026nbsp;[5, 6]. The utilization of natural materials, including fibers derived from plants and agricultural by-products, confers a multitude of benefits. These materials are frequently characterized by abundance, biodegradability, and environmental sustainability, thus positioning them as a compelling substitute for synthetic reinforcements. The versatility of natural materials is such that they can be applied to a variety of uses, including in the construction industry and in the automotive sector\u0026nbsp;[7].\u003c/p\u003e\n\u003cp\u003eThe imperatives of sustainability and life-cycle assessment have precipitated a paradigm shift in materials science, thus positioning natural fiber-reinforced composites (NFRCs) as viable alternatives to conventional synthetic counterparts. In order to achieve structural integrity in NFRCs, it is necessary to possess a granular understanding of the intrinsic properties of bio-based constituents\u0026nbsp;[8]. However, transitioning from synthetic to natural reinforcement introduces significant challenges, primarily due to the ontogenetic and epigenetic variability inherent in natural fibers. Unlike the deterministic quality control possible in synthetic fiber production, the mechanical, physical, and chemical profiles of natural fibers depend heavily on pedo-climatic conditions, species-specific genotypes, and post-harvest extraction protocols.\u0026nbsp;[9]. This diversity has been shown to create uncertainty in predictive modelling of composite performance, making it more difficult to obtain the reliability and safety certifications required for high-performance engineering applications. The frontiers of composite research are currently expanding beyond traditional ligno-cellulosic sources, with research diversifying into a range of agricultural derivatives. These include hemp, bamboo, sisal, coir, kenaf, jute, flax, and babassu, among others. While these materials offer high specific stiffness and a reduced carbon footprint, their diverse and often conflicting property matrices \u0026mdash; ranging from water-loving tendencies to variable interfacial bonding strengths \u0026mdash; demand a rigorous, systematic selection framework.\u0026nbsp;[9\u0026ndash;11]. In this context, MCDM methodologies emerge as an indispensable computational bridge. The integration of technical paradigms (e.g. tensile strength, thermal stability) with economic feasibility and environmental sustainability indices provides a robust and objective basis for material ranking, as demonstrated by MCDM frameworks\u0026nbsp;[12, 13]. This systematic approach is instrumental in identifying optimal natural fiber reinforcements, which have been shown to balance superior mechanical performance with ecological considerations\u0026nbsp;[14].\u003c/p\u003e\n\u003cp\u003eThe emerging body of literature on MCDM for material optimization highlights a pivotal shift towards sustainable engineering, particularly within the automotive and construction sectors\u0026nbsp;[15]. Previous studies have successfully deployed standalone methodologies, such as VIKOR, for evaluating bio-composites in automotive interiors; however, the efficacy of these materials remains contingent upon addressing inherent limitations like hydrophilicity and suboptimal interfacial bonding\u0026nbsp;[16]. As the demand for eco-friendly alternatives intensifies, the material selection process has evolved into a high-dimensional challenge, requiring the simultaneous evaluation of conflicting criteria\u0026mdash;including mechanical integrity, cost-effectiveness, and environmental life-cycle impact. To navigate this complexity, researchers have increasingly adopted hybrid MCDM frameworks to minimize decision uncertainty\u0026nbsp;[17]. For instance, the integration of the Analytic Hierarchy Process (AHP) with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) has bridged the gap between subjective weight assignment and objective performance ranking\u0026nbsp;[18]. This paradigm shift is further necessitated by the escalating complexity of engineering requirements, where traditional monolithic materials are being superseded by next-generation composites designed to transcend specific performance trade-offs\u0026nbsp;[11, 19]. The versatility of MCDM methodologies has been demonstrated across diverse high-performance domains: from optimizing strength-to-weight ratios in the automotive sector using TOPSIS to the comparative ranking of titanium and carbon-fiber-reinforced polymers in aerospace applications\u0026nbsp;[20, 21]. \u0026nbsp;Furthermore, advanced algorithms like MULTIMOORA have shown robustness in industrial waste valorization, such as red-mud-reinforced bronze matrix composites, by integrating triple-check mechanisms\u0026nbsp;[2]. Recent advancements have introduced even more resilient hybridizations\u0026mdash;such as AHP-MOORA, EDAS, and VIKOR\u0026mdash;to optimize materials where wear resistance and thermal stability are paramount [22\u0026ndash;24]. Furthermore, nascent computational tools including COCOSO, MARCOS, and WASPAS have gained prominence for their ability to enhance ranking stability and minimize \u0026quot;rank reversal\u0026quot; risks in complex engineering scenarios [25\u0026ndash;27]. Despite these methodological advancements, a significant research gap persists regarding the epistemic uncertainty and stochastic variability inherent in natural fiber property data. While their ecological benefits are well-established, their inherent heterogeneity remains a fundamental barrier to standardized engineering adoption. Most existing literature relies on single-model decision frameworks or subjective weighting techniques, which often fail to account for the statistical variance and inter-criteria correlations inherent in biomass-derived data.\u003c/p\u003e\n\u003cp\u003eThe present study seeks to bridge this critical gap by proposing a comprehensive, multi-stage benchmarking framework that ensures the mathematical robustness of the selection process. The novelty of this work lies in its integrated hybrid approach; unlike conventional studies, this work first employs the IDOCRIW (Integrated Determination of Objective Criteria Weights) method to derive high-precision objective weights. This is particularly significant as it neutralizes subjective bias and accounts for the statistical entropy within raw material data, transforming inherent uncertainties into a structured weight vector. To ensure maximum reliability, a rigorous comparative analysis is executed using six advanced MCDM algorithms: TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO. Furthermore, to resolve potential algorithmic discrepancies and provide a singular validated decision path, the Copeland aggregation method is implemented to synthesize these individual rankings into a consensus-based hierarchy. The stability of the proposed framework is further stress-tested through a comprehensive sensitivity analysis, ensuring that the final selection remains resilient under fluctuating criteria priorities. By harmonizing high-fidelity mechanical parameters with sustainability indices, this study provides a standardized decision-support mechanism for industrial stakeholders. Ultimately, this research contributes to the transition toward a circular bio-economy by establishing a validated protocol for replacing non-renewable synthetic constituents with optimized, biomass-derived alternatives.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eThis study establishes a rigorous framework for the selection of optimal agro-based reinforcements by synthesizing a diverse library of lignocellulosic candidates with an advanced hybrid MCDM architecture, the systematic workflow of which is illustrated in Fig. 1. In the initial stage (Phase I), candidate materials—Babassu, Jute, Sisal, Kenaf, Coir, Flax, Hemp, and Bamboo—were strategically selected to represent a spectrum of agricultural residues and bio-derived fibers. These fibers are increasingly pivotal in sustainable manufacturing due to their low embodied energy and capacity for agricultural waste valorization [14, 28]. To bridge the gap between inherent biological variability and engineering precision, a multi-dimensional evaluative matrix was constructed, focusing on four critical mechanical and physical descriptors: Density, Tensile Strength, Elastic Modulus, and Elongation. The methodology proceeds to the determination of criteria weights (Phase II), where the IDOCRIW method is employed to ensure an objective weight distribution [29]. Subsequently, in Phase III, the decision-making architecture utilizes six robust baseline algorithms—TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO—to capture diverse mathematical perspectives on performance trade-offs [19, 30–33]. To resolve the potential for rank reversal and to provide a definitive, high-fidelity selection, the individual outputs of these methods are integrated via the Copeland method to consolidate the divergent rankings into a unified compromise solution [34]. This hybrid approach ensures a consensus-based final ranking, offering a mathematically validated roadmap for the strategic integration of natural fibers into high-performance, circular composite systems.\u003cv:shapetype id=\"_x0000_t75\" coordsize=\"21600,21600\" o:spt=\"75\" o:preferrelative=\"t\" path=\"m@4@5l@4@11@9@11@9@5xe\" filled=\"f\" stroked=\"f\"\u003e\u0026nbsp;\u003cv:stroke joinstyle=\"miter\"\u003e\u0026nbsp;\u003cv:formulas\u003e\u0026nbsp;\u003cv:f eqn=\"if lineDrawn pixelLineWidth 0\"\u003e\u0026nbsp;\u003cv:f eqn=\"sum @0 1 0\"\u003e\u0026nbsp;\u003cv:f eqn=\"sum 0 0 @1\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @2 1 2\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @3 21600 pixelWidth\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @3 21600 pixelHeight\"\u003e\u0026nbsp;\u003cv:f eqn=\"sum @0 0 1\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @6 1 2\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @7 21600 pixelWidth\"\u003e\u0026nbsp;\u003cv:f eqn=\"sum @8 21600 0\"\u003e\u0026nbsp;\u003cv:f eqn=\"prod @7 21600 pixelHeight\"\u003e\u0026nbsp;\u003cv:f eqn=\"sum @10 21600 0\"\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:f\u003e\u0026nbsp;\u003c/v:formulas\u003e\n \u003cv:path o:extrusionok=\"f\" gradientshapeok=\"t\" o:connecttype=\"rect\"\u003e\u0026nbsp;\u003c/v:path\u003e\u0026nbsp;\n \u003c/v:stroke\u003e\u0026nbsp;\u003c/v:shapetype\u003e\n\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 1\u003c/strong\u003e Schematic workflow of the proposed hybrid MCDM framework integrating IDOCRIW weighting and Copeland-based aggregation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTechnical Profiles of the Evaluated Material Alternatives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe selection of the most suitable reinforcements for composite applications requires a granular understanding of their individual technical and environmental characteristics. Accordingly, eight distinct lignocellulosic fibers were identified to represent a diverse spectrum of mechanical and socio-economic performance profiles [35, 36]:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eBabassu: An emerging alternative in tropical regions, prioritized for its abundance and its role in localized waste valorization as a strategic bio-filler.\u003c/li\u003e\n \u003cli\u003eJute: A commercially mature alternative, recognized for its cost-efficiency and established compatibility within the automotive and packaging sectors.\u003c/li\u003e\n \u003cli\u003eSisal: Distinguished by high stiffness-to-weight ratios, serving as a primary alternative for systems requiring enhanced structural integrity.\u003c/li\u003e\n \u003cli\u003eKenaf: A balanced alternative valued for its rapid biomass accumulation and versatility in construction-grade structural panels.\u003c/li\u003e\n \u003cli\u003eCoir: A specialized alternative offering superior energy absorption and moisture tolerance for impact-resistant composite designs.\u003c/li\u003e\n \u003cli\u003eFlax: Positioned as a high-performance alternative, providing high-fidelity tensile properties for premium lightweight engineering applications.\u003c/li\u003e\n \u003cli\u003eHemp: A leading alternative for transportation bio-composites, characterized by an optimal synergy of high tensile strength and low density.\u003c/li\u003e\n \u003cli\u003eBamboo: A robust structural alternative featuring exceptional specific strength and rapid growth cycles for green infrastructure.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluative Framework and Multi-Criteria Rationale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe identification of an optimal reinforcement is a non-trivial challenge, as composite material design is inherently governed by conflicting criteria. A single-attribute focus—such as maximizing tensile strength—often overlooks critical trade-offs involving density, cost, or environmental impact. To address this complexity, the present study adopts a multi-dimensional evaluative framework where the decision objectives are categorized as follows [37]:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eBenefit Criteria (Maximization): Mechanical integrity attributes, including Tensile Strength, Elastic Modulus, and Elongation, are prioritized to ensure high-performance structural capabilities.\u003c/li\u003e\n \u003cli\u003eCost Criteria (Minimization): Physical and economic overheads, specifically Density and Unit Cost, are minimized to enhance the lightweight potential and commercial viability of the composite systems.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe representative properties summarized in Table 1 underscore the necessity of this approach, revealing significant heterogeneity among the alternatives. For instance, while Flax and Hemp emerge as superior candidates for load-bearing applications due to their high specific stiffness, Coir presents a divergent performance profile characterized by low stiffness but exceptional elongation (30%), signaling high energy-absorption capacity. In a similar manner, the structural versatility of bamboo and the high tensile strength of kenaf (930 MPa) offer competitive advantages that align with overall sustainability goals, ensuring that the selection process is not strictly governed by mechanical metrics alone. Conversely, Babassu remains a vital alternative when viewed through the lens of local waste valorization and density minimization (0.27 g/cm\u003csup\u003e3\u003c/sup\u003e), despite its modest mechanical benchmarks. This divergence confirms that no single fiber achieves optimality across all indicators simultaneously; high-strength fibers often exhibit higher density or lower elongation, creating complex trade-offs. The presence of these performance trade-offs justifies the deployment of a sophisticated MCDM architecture. By integrating six baseline algorithms with the Copeland method, this study transcends individual method biases and potential rank reversal issues, offering a mathematically validated consensus to identify the most balanced reinforcement alternative, thereby establishing a robust protocol for next-generation sustainable composite engineering.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Representative physical and mechanical properties of selected natural fibers [36].\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFiber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDensity (g/cm³)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTensile Strength (MPa)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eElastic Modulus (GPa)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eElongation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBabassu\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eJute\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e200.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSisal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eKenaf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e930.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoir\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e180.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFlax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e350.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHemp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e690.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBamboo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e140.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eMulti-Criteria Decision-Making Architecture\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe selection of the optimal agricultural waste fiber necessitates a computational framework capable of resolving the complex trade-offs between conflicting performance criteria. To achieve a high-fidelity and interpretable ranking, this study employs a multi-algorithmic approach, integrating six established MCDM techniques: TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO. While these methods operate on the same initial decision matrix, they utilize divergent mathematical logics—ranging from distance-based measures to utility-weighted aggregations—to evaluate the alternatives. This hybrid strategy is specifically designed to mitigate the inherent biases of individual algorithms and to establish a robust foundation for consensus analysis, thereby enhancing the defensibility of the final material selection. Fig. 2 illustrates the proposed hierarchical decision model designed for the systematic evaluation of agro-based fibers. The evaluative architecture is formalized into a cohesive structural configuration, organized as follows:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eLevel 1 (Objective):\u003c/strong\u003e Represents the primary objective—identifying the optimal agricultural fiber alternative for high-performance composite reinforcement.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLevel 2 (Evaluation Criteria):\u003c/strong\u003e Comprises four physical and mechanical indicators: Density (C1), Tensile Strength (C2), Elastic Modulus (C3), and Elongation (C4). In this model, C1 is a cost-based criterion (to be minimized), while C2, C3, and C4 are benefit-based (to be maximized).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLevel 3 (Alternatives):\u003c/strong\u003e Encompasses eight candidate fibers—Babassu (A1), Jute (A2), Sisal (A3), Kenaf (A4), Coir (A5), Flax (A6), Hemp (A7), and Bamboo (A8)—which are subjected to cross-dimensional assessment.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis hierarchical framework effectively maps the interdependencies between the engineering constraints and the material candidates. By decomposing the selection problem into these interconnected layers, the model provides a robust roadmap for material valorization in green material science, ensuring that the final ranking is derived from a balanced synthesis of intrinsic fiber properties.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 2\u003c/strong\u003e Hierarchical decision model for the optimal selection of agro-fiber reinforcements.\u003c/p\u003e\n\u003cp\u003ePrior to the execution of the ranking algorithms, the relative significance of each evaluation criterion was established through the\u0026nbsp;IDOCRIW\u0026nbsp;method. As an advanced objective weighting technique, IDOCRIW mitigates decision-maker subjectivity by synthesizing two distinct informational dimensions: entropy-based dispersion analysis and the Criterion Impact Loss (CILOS) concept [29]. The implementation of IDOCRIW is particularly advantageous for the current fiber-selection problem, where the selected physical and mechanical criteria exhibit significant divergence in scales and inherent conflict. The method functions by quantitatively assessing the discriminative power of each criterion; it measures the potential information loss inherent in the decision matrix and balances it with statistical entropy. By deriving weights directly from the intrinsic data structure, the IDOCRIW approach ensures that the subsequent multi-algorithmic analyses—TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO—are grounded in analytical rigor. This integration enhances the transparency and reproducibility of the hybrid MCDM framework [38], providing a mathematically robust foundation for identifying the most balanced reinforcement alternative in sustainable composite engineering.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMulti-Criteria Ranking Algorithms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTOPSIS: Geometric Proximity Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TOPSIS was integrated into the evaluative framework to assess fiber alternatives based on their geometric distance from theoretical performance benchmarks. The fundamental premise of TOPSIS is that the optimal reinforcement candidate must simultaneously exhibit the shortest Euclidean distance from the Positive Ideal Solution (PIS)—representing the maximum attainment of beneficial criteria—and the greatest distance from the Negative Ideal Solution (NIS), which characterizes the least desirable performance outcomes\u0026nbsp;[39]. The implementation process involves the transformation of the initial decision matrix into a weighted-normalized matrix, ensuring that the mechanical and physical attributes of the fibers are commensurable. By establishing these ideal and anti-ideal reference points, the method derives a final preference index that serves as a measure of an alternative’s capacity to balance complex performance trade-offs. In the context of the present fiber-selection problem, TOPSIS offers a mathematically transparent mechanism to identify candidates that achieve a high degree of multi-dimensional equilibrium, making it indispensable for ranking materials with conflicting attributes such as high tensile strength and density constraints.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVIKOR: Compromise Ranking and Regret Minimization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe VIKOR method is utilized to determine a\u0026nbsp;compromise ranking\u0026nbsp;within a multi-objective environment where no single alternative excels across all parameters. In this study, VIKOR evaluates fibers by balancing\u0026nbsp;maximum group utility and minimum individual regret. This dual-perspective approach ensures that the selected reinforcement does not merely exhibit high average performance but also avoids critical deficiencies in any single attribute [40]. By penalizing significant deviations from ideal values, VIKOR identifies the most 'stable' alternative for composite systems where a balanced synergy between mechanical strength and physical constraints is non-negotiable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMARCOS: Utility-Based Reference Synthesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) is employed to position fiber alternatives relative to theoretical ideal and anti-ideal boundaries. This method extends the decision matrix with synthetic reference points, calculating utility degrees to quantify the proximity of each candidate to the optimal performance envelope [41]. MARCOS provides a robust utility-based interpretation, ensuring ranking stability and offering a clear trajectory of how each fiber alternative deviates from the best and worst possible material profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWASPAS: Aggregated Stochastic Robustness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Weighted Aggregated Sum Product Assessment (WASPAS) method enhances decision reliability by synthesizing the Weighted Sum Model (WSM) and the Weighted Product Model (WPM). This hybrid formulation captures both additive and multiplicative interdependencies among criteria. In the current framework, WASPAS mitigates the risks associated with a single aggregation rule, providing a stabilized ranking that accounts for the synergistic effects between mechanical efficiency and environmental sustainability [42].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEDAS: Deviation Analysis from Central Tendency\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Evaluation based on Distance from Average Solution (EDAS) shifts the evaluative focus from extreme ideal points to the average performance profile of the candidate pool [43]. By calculating positive and negative deviations from the mean across all attributes, EDAS identifies alternatives that consistently outperform the average baseline. This approach is particularly effective in reducing sensitivity to outliers, emphasizing a balanced material profile over extreme but potentially volatile performance peaks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOCOSO: Multi-Dimensional Compromise Integration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Combined Compromise Solution (COCOSO) method integrates three distinct compromise strategies through additive and power-weighted aggregation [25]. COCOSO serves as a high-fidelity validation tool within this study, capturing divergent dimensions of ranking behavior. Its inclusion—alongside TOPSIS, VIKOR, MARCOS, WASPAS, and EDAS—serves to build a comprehensive consensus-based ranking, significantly increasing the statistical confidence in the final identification of the superior agricultural waste reinforcement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsensus Synthesis and Computational Implementation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Copeland Method: Meta-Ranking Integration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo derive a definitive, high-fidelity ranking from the diverse outputs of the aforementioned MCDM algorithms, this study employs the Copeland method as a meta-ranking synthesis tool [44]. Unlike traditional aggregation techniques, the Copeland method operates on a pairwise comparison logic, assessing the relative dominance of each alternative across the entire ensemble of TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO results. For every pair of fibers, a \"win\" is recorded for the alternative preferred by the majority of the baseline models. The final Copeland Score is then derived from the net difference between pairwise victories and defeats. This consensus-based approach provides an additional layer of structural robustness, mitigating the risk of model-specific bias and identifying the reinforcement alternative that demonstrates the most consistent performance across divergent mathematical frameworks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRationale for the Hybrid MCDM Architecture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe integration of these eight distinct methodologies constitutes a rigorous Hybrid MCDM framework, designed to minimize the epistemic uncertainty inherent in single-model selection [45]. In the context of sustainable composite engineering, the optimal material is rarely the one that excels in a solitary attribute; rather, it is the candidate that offers the most resilient compromise across mechanical, economic, and environmental vectors [46].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComputational Framework: The pymcdm Library\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure the transparency, reproducibility, and analytical precision of the ranking process, the computational execution was performed using the pymcdm Python library [47]. This advanced library provides a standardized environment for multi-level normalization, objective weighting, and the simultaneous execution of complex MCDM algorithms [48]. By utilizing pymcdm, the study eliminates the potential for manual calculation errors and facilitates a seamless transition between individual model rankings and the final Copeland-based consensus. This computational workflow offers a validated template for researchers aiming to extend or reproduce these findings within the evolving domain of agricultural waste valorization.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eThe selection of the optimal agricultural waste fiber for composite reinforcement was executed through a multi-stage computational workflow, ensuring the systematic translation of raw material data into a validated decision output. The foundational material attributes for the eight candidate alternatives (A₁–A₈) were evaluated against the established criteria matrix, as detailed in the initial decision matrix (Table 2). To account for the relative significance of each performance indicator, objective weighting coefficients were derived using the IDOCRIW method, with the resulting weight distribution presented in Table 3. Given the divergent scales of the physical and mechanical properties—such as density and tensile strength—the decision matrix was subjected to a rigorous normalization procedure to ensure mathematical commensurability (Table 4). Subsequently, the weighted-normalized decision matrix (Table 5) was constructed, forming the analytical basis for the parallel execution of the six baseline MCDM algorithms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u0026nbsp; Initial decision matrix and optimization directions for the natural fiber alternatives.\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCriteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlternatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e200.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e930.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e180.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e350.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e690.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e140.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Weight coefficients of the criteria for the natural fiber materials\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCriteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWeights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Normalized decision matrix for the natural fiber materials\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCriteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlternatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e Weighted normalized decision matrix for natural fiber alternatives\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCriterions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlternatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe collective ranking performance of the six baseline MCDM algorithms is summarized in Table 6, providing a comparative landscape of the fiber alternatives across divergent mathematical logics. The most striking finding is the high-fidelity convergence observed among all methods, particularly in identifying the superior and inferior candidates. As evidenced by the numerical results, Alternative A\u003csub\u003e5\u003c/sub\u003e (Coir) consistently secures the primary rank (first) across TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO. This absolute consensus highlights the robust dominance of A\u003csub\u003e5\u003c/sub\u003e, confirming that its balanced mechanical profile remains unparalleled regardless of the ranking algorithm or aggregation operator employed. Similarly, A\u003csub\u003e4\u003c/sub\u003e (Kenaf) and A\u003csub\u003e7\u003c/sub\u003e (Hemp) firmly occupy the 2nd and 3rd positions, respectively, across all six models. This stability reinforces the reliability of the proposed decision framework, as the top-tier hierarchy remains immune to the 'rank reversal' phenomenon [49]. However, a more granular examination reveals strategic fluctuations in the mid-tier alternatives, namely A\u003csub\u003e2\u003c/sub\u003e (Jute), \u0026nbsp;A\u003csub\u003e6\u003c/sub\u003e (Flax), and A\u003csub\u003e8\u003c/sub\u003e (Bamboo). For instance, A\u003csub\u003e8\u003c/sub\u003e (Bamboo) is ranked 4th by VIKOR and COCOSO but recedes to 6th in TOPSIS and MARCOS. Such discrepancies are mathematically anticipated; distance-based methods like TOPSIS prioritize the geometric proximity to ideal solutions, whereas compromise-based models like VIKOR focus on maximizing group utility and minimizing individual regret [46]. Specifically, the variation in A\u003csub\u003e6\u003c/sub\u003e’s (Flax) ranking—shifting between 4th and 6th—reflects the sensitivity of its mechanical properties to additive versus multiplicative aggregation rules. At the lower end of the spectrum,\u0026nbsp;A\u003csub\u003e1\u0026nbsp;\u003c/sub\u003e(Babassu) and A\u003csub\u003e3\u003c/sub\u003e (Sisal) are consistently identified as the least favorable options, further validating the discriminative power of the hybrid MCDM architecture.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e The ranking of the natural fiber materials\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTOPSIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVIKOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMARCOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWASPAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEDAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCOCOSO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRanking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRanking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRanking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRanking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRanking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRanking\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe comparative ranking performance of the six baseline MCDM algorithms is illustrated in Fig. 3, providing a comparative landscape of the natural fiber alternatives across divergent mathematical logics. The most striking finding is the high-fidelity convergence observed among all methods, particularly at the polar ends of the ranking spectrum. As evidenced by the results, Alternative A\u003csub\u003e5\u003c/sub\u003e (Coir) consistently secures the primary rank (1st) across TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO. This absolute consensus highlights the robust dominance of A\u003csub\u003e5\u003c/sub\u003e, confirming that its multi-criteria performance remains unparalleled regardless of whether distance-based, ratio-based, or compromise-based operators are employed. Similarly, A\u003csub\u003e4\u003c/sub\u003e (Kenaf) maintains a steadfast 2nd position, while A\u003csub\u003e7\u003c/sub\u003e (Hemp) consistently occupies the 3rd rank across all six models, further reinforcing the structural stability of the selection framework. However, a more granular examination reveals strategic fluctuations in the mid-to-lower tier alternatives, specifically A\u003csub\u003e2\u003c/sub\u003e (Jute), A\u003csub\u003e6\u003c/sub\u003e (Flax), and A\u003csub\u003e8\u003c/sub\u003e (Bamboo). For instance, A\u003csub\u003e8\u003c/sub\u003e (Bamboo) is ranked 4th by VIKOR and 5th by WASPAS/EDAS, yet it drops to 6th in TOPSIS and MARCOS. These discrepancies are mathematically anticipated; the variation in A\u003csub\u003e6\u003c/sub\u003e’s (Flax) positioning—which peaks at 4th in MARCOS but recedes to 6th in COCOSO—reflects the sensitivity of the alternatives to additive versus multiplicative aggregation rules [39]. The divergent behavior of A\u003csub\u003e1\u003c/sub\u003e (Babassu) and A\u003csub\u003e3\u003c/sub\u003e (Sisal) at the bottom of the hierarchy further underscores the stability of the top-performing fibers, as the algorithms only differ in their penalization of the least-ideal candidates. This cross-validation confirms that Coir (A\u003csub\u003e5\u003c/sub\u003e) and Kenaf (A\u003csub\u003e4\u003c/sub\u003e) are the most reliable selections for the intended application.\u003c/p\u003e\n\u003cp\u003e\n \u003cv:shapetype id=\"_x0000_t75\" coordsize=\"21600,21600\" o:spt=\"75\" o:preferrelative=\"t\" path=\"m@4@5l@4@11@9@11@9@5xe\" filled=\"f\" stroked=\"f\"\u003e\u0026nbsp;\u003c/v:shapetype\u003e\u003cstrong\u003eFig. 3\u003c/strong\u003e Comparative ranking of the eight fiber alternatives across six baseline MCDM algorithms.\n\u003c/p\u003e\n\u003cp\u003eThe stability of the generated rankings across the six benchmark MCDM methodologies is synthesized in Fig. 4, facilitating a rigorous assessment of inter-method consistency and algorithmic sensitivity. A primary finding is the absolute convergence observed at the hierarchical boundaries; A\u003csub\u003e5\u003c/sub\u003e (Coir), A\u003csub\u003e4\u003c/sub\u003e (Kenaf), and A\u003csub\u003e7\u003c/sub\u003e (Hemp) exhibit perfectly horizontal trajectories, securing their 1st, 2nd, and 3rd ranks, respectively, without exception. This methodological invariance demonstrates a 'robust dominance,' confirming that the superior performance of these candidates is an intrinsic property of the material data rather than an artifact of a specific mathematical operator—whether distance-based (TOPSIS, EDAS), compromise-oriented (VIKOR, MARCOS), or utility-driven (WASPAS, COCOSO). Conversely, the mid-tier region—comprising A\u003csub\u003e2\u003c/sub\u003e (Jute), A\u003csub\u003e6\u003c/sub\u003e (Flax), and A\u003csub\u003e8\u003c/sub\u003e (Bamboo)—exhibits notable 'crossover' patterns, signaling a higher degree of sensitivity to individual aggregation rules. For instance, the fluctuating trajectory of A\u003csub\u003e8\u003c/sub\u003e (Bamboo), which oscillates between the 4th and 6th positions, highlights the impact of divergent algorithmic focal points. While VIKOR and COCOSO favor A8 due to their emphasis on 'maximum group utility,' distance-based models like TOPSIS penalize its specific deviations from the ideal solution. Similarly, the localized rank-reversals between A\u003csub\u003e1\u003c/sub\u003e (Babassu) and A\u003csub\u003e3\u003c/sub\u003e (Sisal) at the lower boundary underscore how different models prioritize the 'regret' associated with poor-performing criteria. This visual evidence of algorithmic nuance reinforces the necessity of a multi-model approach. The high global convergence (Spearman’s \u003cem\u003eρ\u003c/em\u003e) validates the reliability of the selection framework, while the localized discrepancies justify the subsequent deployment of a consensus-based aggregation to mitigate any single-model bias and ensure a mathematically balanced final decision.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFig. 4\u003c/strong\u003e Multi-methodological rank stability analysis of natural fiber alternatives across six MCDM algorithms.\u003c/p\u003e\n\u003cp\u003eFig. 5 presents a radial synthesis of the ranking outcomes, offering a holistic perspective on the convergence and divergence patterns among the six employed MCDM algorithms. In this multidimensional visualization, geometric proximity to the coordinate origin signifies a superior performance rank (e.g., 1st or 2nd). The tight, singular clustering of A\u003csub\u003e5\u003c/sub\u003e (Coir) and A\u003csub\u003e4\u003c/sub\u003e (Kenaf) at the core of the radar web provides visual confirmation of a robust consensus. This spatial density demonstrates that the dominance of these top-tier alternatives remains invariant across divergent mathematical frameworks—regardless of whether the logic is distance-based (TOPSIS), utility-driven (WASPAS), or centered on compromise utility (VIKOR/MARCOS). Conversely, the broader spatial dispersion observed for mid-tier alternatives, specifically A\u003csub\u003e8\u003c/sub\u003e (Bamboo) and A\u003csub\u003e2\u003c/sub\u003e (Jute), reflects a higher sensitivity to specific aggregation operators. For instance, the expansion of the 'web' at the A\u003csub\u003e8\u003c/sub\u003e axis highlights the discrepancy between models that prioritize proximity to ideal solutions versus those that emphasize deviations from average performance (EDAS) [50]. The significant overlapping geometry of the radar profiles serves as empirical evidence of high inter-method reliability. While the peripheral fluctuations along the A\u003csub\u003e1\u003c/sub\u003e (Babassu) and A\u003csub\u003e3\u003c/sub\u003e (Sisal) axes indicate minor algorithmic nuances in assessing less successful candidates, the overall structural symmetry of the plot validates the integrity of the proposed decision framework. This high degree of cross-method correlation ensures that localized rank-reversals are systematically reconciled, providing a rigorous analytical foundation for establishing a definitive and integrated material hierarchy for sustainable composite applications [51].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFig. 5\u003c/strong\u003e Radial visualization of ranking convergence across six MCDM algorithms.\u003c/p\u003e\n\u003cp\u003eTo rigorously quantify the statistical convergence and internal consistency of the multi-criteria framework, a Spearman’s rank correlation analysis was performed, with results synthesized in the heat map in Fig. 6. The matrix reveals exceptionally high correlation coefficients (ρ), ranging from 0.91 to 1.00, which—according to the Evans scale—indicates a 'very strong' to 'perfect' monotonic relationship across all methodological pairings [52]. The most prominent finding is the perfect correlation (ρ=1.00) observed between WASPAS and EDAS, suggesting that the additive-multiplicative aggregation of WASPAS and the distance-from-average logic of EDAS yield identical hierarchical outputs for this specific dataset. Furthermore, the near-perfect alignment between TOPSIS and MARCOS (ρ=0.99) underscores the stability of distance-based rankings when referenced against ideal and anti-ideal solutions. Even the minimum observed coefficient (0.91, between TOPSIS and COCOSO) remains well above the threshold of statistical significance, confirming that the localized rank-reversals discussed in previous sections do not undermine the global integrity of the material hierarchy. This high degree of inter-method reliability serves as a dual validation: first, it confirms that the resulting fiber ranking is largely invariant to the underlying algorithmic logic; and second, it validates the efficacy of the IDOCRIW weighting scheme in producing a balanced and objective decision matrix [29]. Ultimately, the statistical evidence in Fig. 6 demonstrates that the proposed hybrid architecture produces stable, reproducible, and objective results, providing a mathematically sound foundation for high-performance sustainable material selection [53].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFig. 6\u003c/strong\u003e Spearman’s rank correlation matrix showing the convergence among six MCDM algorithms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinal Consensus Synthesis: The Copeland Method Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo resolve localized ranking fluctuations and establish a definitive material hierarchy, the\u0026nbsp;Copeland method\u0026nbsp;was employed as a meta-ranking aggregator [54]. The consensus results, illustrated in\u0026nbsp;Fig. 7, represent the integrated superiority of each fiber across the entire MCDM ensemble, providing a mathematically robust resolution to the multi-algorithm assessment.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eIdentification of the Optimal Reinforcement:\u0026nbsp;The synthesis unequivocally identifies\u0026nbsp;A\u003csub\u003e5\u003c/sub\u003e (Coir) as the superior alternative, securing the absolute Rank 1. The dominance of A\u003csub\u003e5\u003c/sub\u003e in the Copeland aggregation reflects its consistent pairwise superiority against all other candidates, validating it as the most optimized reinforcement when mechanical integrity, low density, and sustainability are evaluated simultaneously. A\u003csub\u003e4\u003c/sub\u003e (Kenaf) and A\u003csub\u003e7\u003c/sub\u003e (Hemp) follow as the 2nd and 3rd most viable alternatives, respectively, forming a 'high-performance cluster' that remains stable across the collective decision-making landscape.\u003c/li\u003e\n \u003cli\u003eResolution of Mid-Tier Ambiguities:\u0026nbsp;A critical contribution of the Copeland integration is its capacity to provide a definitive resolution for candidates exhibiting 'rank volatility' in individual models. While standalone algorithms showed conflicting positions for\u0026nbsp;A\u003csub\u003e6\u003c/sub\u003e (Flax) and A\u003csub\u003e8\u003c/sub\u003e (Bamboo), the Copeland aggregation mathematically reconciles these discrepancies by calculating net win-loss scores, effectively assigning them Rank 4 and Rank 5. This consensus ranking offers a more defensible and objective basis for engineering decisions, mitigating the inherent bias of any single standalone algorithm.\u003c/li\u003e\n \u003cli\u003eFinal Hierarchy and Strategic Valorization:\u0026nbsp;At the lower end of the spectrum,\u0026nbsp;A\u003csub\u003e1\u003c/sub\u003e (Babassu) and A\u003csub\u003e3\u003c/sub\u003e (Sisal) are finalized as Ranks 8 and 7, respectively. Their positions at the bottom of the hierarchy—primarily due to modest tensile properties relative to the high-strength bast fibers—confirm their limited suitability for high-load structural applications. Nevertheless, the comprehensive ranking provided in Fig. 7 offers a strategic roadmap for agro waste valorization; while A\u003csub\u003e5\u003c/sub\u003e is recommended for primary structural components, the hierarchical order assists in identifying secondary candidates for non-structural or cost-sensitive composite designs.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study successfully established a robust and transparent hybrid MCDM framework for the strategic selection of agricultural waste fibers as sustainable reinforcements in composite engineering. By integrating six divergent ranking algorithms\u0026mdash;TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO\u0026mdash;with the IDOCRIW objective weighting method and the Copeland consensus synthesis, the research transcended the limitations of single-model biases and rank-reversal phenomena.\u003c/p\u003e\n\u003cp\u003eThe following key conclusions are derived from the analytical findings:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eMethodological Robustness and Convergence:\u0026nbsp;The exceptionally high correlation coefficients (\u0026rho;: 0.91\u0026ndash;1.00) obtained from Spearman\u0026rsquo;s rank analysis validate the internal consistency and reliability of the proposed hybrid architecture. The framework demonstrated that while individual mathematical logics\u0026mdash;ranging from distance-based to utility-oriented\u0026mdash;may vary, the global trend in material hierarchy remains highly convergent and statistically significant.\u003c/li\u003e\n \u003cli\u003eOptimal Alternative Identification: Coir (A\u003csub\u003e5\u003c/sub\u003e) unequivocally emerged as the superior reinforcement candidate across all six baseline methods and the final Copeland synthesis. Its Rank 1 position is attributed to an optimal equilibrium between favorable mechanical integrity, low density, and high sustainability indicators, making it the most balanced choice for high-performance green composites.\u003c/li\u003e\n \u003cli\u003eValidation of the High-Performance Cluster: Kenaf (A\u003csub\u003e4\u003c/sub\u003e) and Hemp (A\u003csub\u003e7\u003c/sub\u003e) were identified as the second and third most viable alternatives, respectively, providing a validated cluster of high-performance reinforcements. Furthermore, the Copeland integration effectively resolved the ranking ambiguities observed in mid-tier fibers like Flax (A\u003csub\u003e6\u003c/sub\u003e) and Bamboo (A\u003csub\u003e8\u003c/sub\u003e), providing a definitive roadmap for their application-specific utilization.\u003c/li\u003e\n \u003cli\u003eStrategic Valorization Roadmap:\u0026nbsp;The finalized hierarchical order (from Coir to Babassu) provides a quantitative basis for the valorization of agricultural residues. This allows material scientists to align fiber selection with specific engineering constraints, ensuring that the selection process is driven by objective performance metrics rather than heuristic estimations.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn summary, the integration of the Copeland method provided a mathematically rigorous bridge between divergent MCDM outcomes, establishing a defensible consensus for sustainable material design. Future research could extend this framework by incorporating life cycle assessment data and chemical compatibility factors, further advancing the transition toward a circular bio-economy in the composite industry.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e The conceptual framework and design of the study were prepared by A, A.. The idea for the article belongs to the author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e No funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003eIn the course of preparing the present work, the author employed GEMINI with a view to enhancing the readability and linguistic quality of the manuscript. Following the utilization of the provided tool/service, the author conducted a thorough review and editing process of the content, subsequently assuming full responsibility for the publication\u0026apos;s content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003eThe author declares no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHe A, Xing T, Liang Z, et al (2024) Advanced Aramid Fibrous Materials: Fundamentals, Advances, and Beyond. Adv Fiber Mater 6:3\u0026ndash;35. https://doi.org/10.1007/s42765-023-00332-1\u003c/li\u003e\n\u003cli\u003eAvcu A, Kuş H, Sug\u0026ouml;z\u0026uuml; İ (2024) Application of the MULTIMOORA Method to Evaluate Performance Results of Red Mud Reinforced Bronze Matrix Brake Pads. 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Omega 59:146\u0026ndash;156. https://doi.org/10.1016/j.omega.2015.05.013\u003c/li\u003e\n\u003cli\u003eAktaş N, Demirel N (2021) A hybrid framework for evaluating corporate sustainability using multi-criteria decision making. Environ Dev Sustain 23:15591\u0026ndash;15618. https://doi.org/10.1007/s10668-021-01311-5\u003c/li\u003e\n\u003cli\u003eYadav R, Singh M, Meena A, et al (2023) Selection and ranking of dental restorative composite materials using hybrid Entropy-VIKOR method: An application of MCDM technique. Journal of the Mechanical Behavior of Biomedical Materials 147:106103. https://doi.org/10.1016/j.jmbbm.2023.106103\u003c/li\u003e\n\u003cli\u003eBirkocak DT, Acar E, Bakadur A\u0026Ccedil;, et al (2023) An Application of the MARCOS Method Within the Framework of Sustainability to Determine the Optimum Recycled Fibre-Containing Fabric. Fibers Polym 24:2595\u0026ndash;2608. https://doi.org/10.1007/s12221-023-00197-6\u003c/li\u003e\n\u003cli\u003eAcar E Evaluating sustainability in the textile sector using WASPAS and TOPSIS: A multicriteria decision-making approach\u003c/li\u003e\n\u003cli\u003eTerzioglu T, Polat G (2022) Formwork System Selection in Building Construction Projects Using an Integrated Rough AHP-EDAS Approach: A Case Study. Buildings 12:. https://doi.org/10.3390/buildings12081084\u003c/li\u003e\n\u003cli\u003eŞahin M (2020) Hybrid Multiattribute Decision Method for Material Selection. International Journal of Pure and Applied Sciences 6:107\u0026ndash;117. https://doi.org/10.29132/ijpas.811402\u003c/li\u003e\n\u003cli\u003eDepartment of Electrical and Electronics Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, India, Manoj V (2024) Towards Efficient Energy Solutions: MCDA-Driven Selection of Hybrid Renewable Energy Systems. IJEETC 13:98\u0026ndash;111. https://doi.org/10.18178/ijeetc.13.2.98-111\u003c/li\u003e\n\u003cli\u003eOpricovic S, Tzeng G-H (2004) Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research 156:445\u0026ndash;455. https://doi.org/10.1016/S0377-2217(03)00020-1\u003c/li\u003e\n\u003cli\u003eKizielewicz B, Sałabun W (2024) The pymcdm-reidentify tool: Advanced methods for MCDA model re-identification. SoftwareX 28:101960. https://doi.org/10.1016/j.softx.2024.101960\u003c/li\u003e\n\u003cli\u003eKizielewicz B, Shekhovtsov A, Sałabun W (2023) pymcdm\u0026mdash;The universal library for solving multi-criteria decision-making problems. SoftwareX 22:101368. https://doi.org/10.1016/j.softx.2023.101368\u003c/li\u003e\n\u003cli\u003eWang J-J, Jing Y-Y, Zhang C-F, Zhao J-H (2009) Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews 13:2263\u0026ndash;2278. https://doi.org/10.1016/j.rser.2009.06.021\u003c/li\u003e\n\u003cli\u003eMi X, Liao H (2019) An integrated approach to multiple criteria decision making based on the average solution and normalized weights of criteria deduced by the hesitant fuzzy best worst method. Computers \u0026amp; Industrial Engineering 133:83\u0026ndash;94. https://doi.org/10.1016/j.cie.2019.05.004\u003c/li\u003e\n\u003cli\u003eSałabun W, Wątr\u0026oacute;bski J, Shekhovtsov A (2020) Are MCDA Methods Benchmarkable? A Comparative Study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II Methods. Symmetry 12:. https://doi.org/10.3390/sym12091549\u003c/li\u003e\n\u003cli\u003eEvans JD (1996) Straightforward statistics for the behavioral sciences. Thomson Brooks/Cole Publishing Co, Belmont, CA, US\u003c/li\u003e\n\u003cli\u003eJahan, A., et al. (2012) Comparative Study of Physical and Elastic Properties of Jute and Glass Fiber Reinforced LDPE Composites. International Journal of Scientific \u0026amp; Technology Research, 1, 68-72. - References - Scientific Research Publishing. https://www.scirp.org/reference/referencespapers?referenceid=2827158. Accessed 27 Apr 2026\u003c/li\u003e\n\u003cli\u003eAl-Sharrah G (2010) Ranking using the Copeland score: a comparison with the Hasse diagram. J Chem Inf Model 50:785\u0026ndash;791. https://doi.org/10.1021/ci100064q\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Natural Fiber, Hybrid MCDM, Material Selection, Copeland Method, Rank Reversal","lastPublishedDoi":"10.21203/rs.3.rs-9611463/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9611463/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The strategic imperative of the circular economy has catalyzed a paradigm shift toward leveraging agricultural waste as high-performance, sustainable alternatives to conventional synthetic reinforcements. However, the inherent heterogeneity of natural fibers necessitates a systematic evaluative approach to ensure engineering reliability. This study presents a robust hybrid Multi-Criteria Decision-Making (MCDM) framework designed to evaluate eight prominent natural fibers—Babassu, Jute, Sisal, Kenaf, Coir, Flax, Hemp, and Bamboo—against a multidimensional matrix of physical and mechanical constraints. The methodology integrates IDOCRIW objective weighting with six baseline ranking algorithms (TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO), which are further synthesized via the Copeland method to establish a definitive, consensus-based ranking. To ensure the mathematical defensibility of the results, statistical validation using Spearman’s rank correlation was performed, revealing exceptionally high consistency (ρ: 0.91–1.00) across the algorithmic ensemble. Results unequivocally identify Coir (A5) as the superior reinforcement candidate, demonstrating an optimal equilibrium between mechanical integrity, low density, and sustainability descriptors, followed by Kenaf (A4) and Hemp (A7). By resolving the ranking ambiguities and model-specific biases inherent in standalone algorithms, this study provides a mathematically rigorous bridge between divergent MCDM outcomes. Ultimately, the proposed framework offers a validated roadmap for agro-waste valorization and the strategic selection of bio-based reinforcements, thereby facilitating the transition toward advanced green material science and circular composite manufacturing.","manuscriptTitle":"Application of an Integrated IDOCRIW-Copeland MCDM Framework for Ranking Agro-Based Natural Fibers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 11:00:41","doi":"10.21203/rs.3.rs-9611463/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d307df7-5237-47d2-bbd1-0200f59ee83f","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":{"display":true,"email":"[email protected]","identity":"advanced-composites-and-hybrid-materials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"achm","sideBox":"Learn more about [Advanced Composites and Hybrid Materials](https://link.springer.com/journal/42114)","snPcode":"42114","submissionUrl":"https://submission.nature.com/new-submission/42114/3","title":"Advanced Composites and Hybrid Materials","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T11:00:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 11:00:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9611463","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9611463","identity":"rs-9611463","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}