Integrated Production–Distribution Planning Using a MILP Optimization Model and CPLEX Solver

Integrated Production–Distribution Planning Using a MILP Optimization Model and CPLEX Solver | 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 Integrated Production–Distribution Planning Using a MILP Optimization Model and CPLEX Solver Mohammed Machkour, Abdellah ELBARKANY, Bilal HARRAS This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7785095/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Feb, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract This paper presents an integrated mathematical model for optimizing production, inventory, and transportation planning in a multi-product, multi-period supply chain. The model aims to minimize total variable costs—including transportation, storage, backlog, overtime production, and vehicle usage—while satisfying customer demand and adhering to real-world operational constraints such as pallet-based shipping, trailer capacity, warehouse limits, and backlog penalties. Formulated as a Mixed-Integer Linear Programming (MILP) problem and solved using IBM ILOG CPLEX, the model considers a single factory, two warehouses, and a single customer across four planning periods and six finished products. Key logistical parameters include pallet sizes, trailer capacities, and a maximum number of vehicles available per week. The model integrates tactical and operational decisions such as the number of products transported (in pallets), vehicle use, extra vehicle penalties, warehouse inventory dynamics, and controlled backlog. The numerical results highlight the critical role of transportation capacity and backlog management in driving overall system cost. While production costs remain constant, backlog and transportation expenses, suggesting that limited vehicle capacity significantly affects service levels and cost-effectiveness, dominate variable costs. The results also demonstrate efficient warehouse utilization and minimal overtime, reflecting a lean operational strategy. This work contributes to the literature by addressing integrated supply chain decision-making with detailed and realistic transport-unit constraints. It offers a flexible decision-support tool that can be extended to multi-factory or multi-client contexts, and enhanced with stochastic demand or sustainability metrics for real-world deployment. Integrated supply chain optimization planning MILP optimization Full Text Cite Share Download PDF Status: Published Journal Publication published 13 Feb, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Minor Revisions Needed 01 Jan, 2026 Reviewers agreed at journal 09 Oct, 2025 Reviewers invited by journal 08 Oct, 2025 Editor assigned by journal 07 Oct, 2025 First submitted to journal 05 Oct, 2025 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. We do this by developing innovative software and high quality services for the global research community. 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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-7785095","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526525805,"identity":"35e2ac94-ed43-4d12-900d-e65531e168d2","order_by":0,"name":"Mohammed 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