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<p>The rising global demand for perishable products—coupled with their inherent time sensitivity and stringent storage requirements—poses significant challenges for supply chain management. In response, this research develops an innovative multi-objective optimization framework that integrates economic, environmental, and social dimensions into a resilient supply chain model tailored for perishable goods. The proposed model addresses critical issues such as transportation delays, disruptions due to adverse traffic and weather conditions, and the necessity for coordinated interactions among distribution centers to minimize wastage and ensure product integrity. Using advanced mixed-integer linear program alongside state-of-the-art metaheuristic algorithms—such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and multi-objective particle swarm optimization—the framework optimizes key decision variables including transportation routes, delivery schedules, and inventory management under real-world constraints. The performance metrics used are: MID (mean ideal distance), SNS (spread of non-dominated solutions), RAS (rate of achievement simultaneously), and runtime. The computational results, averaged over the experimental runs, summarize algorithmic performance for small/medium and large problem sizes as follows. For small/medium instances (baseline: CPLEX), MOPSO achieves a 6.81% reduction in MID, a 0.88% reduction in SNS, a 23.56% reduction in RAS, and a 42.69% reduction in CPU time relative to CPLEX; NSGA‑II yields a 2.46% MID reduction, a 21.98% SNS reduction, a 25.04% RAS reduction, and a 1.04% decrease in CPU time relative to CPLEX. For large instances (baseline: MOPSO), NSGA‑II attains 5.12% lower SNS and 1.18% lower RAS than MOPSO but requires 37.33% more CPU time. Lower values of MID, RAS, and CPU time indicate better performance; higher SNS values indicate better performance. By incorporating risk management and equitable resource allocation in the proposed model, the framework is expected to enhance supply‑chain responsiveness, leading to improved customer satisfaction and a more balanced distribution of operational risk. This study contributes to both the theoretical development of sustainable supply‑chain management and its practical application in industries dominated by perishable products.</p>

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Optimizing the Unstable: A New Paradigm in Risk Management and Equitable Supply Chains for Perishables

  • 期刊论文

  • 发表日期：

    2025年11月27日

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  • Javad Behnamian,
  • Shiva Momeni

摘要

The rising global demand for perishable products—coupled with their inherent time sensitivity and stringent storage requirements—poses significant challenges for supply chain management. In response, this research develops an innovative multi-objective optimization framework that integrates economic, environmental, and social dimensions into a resilient supply chain model tailored for perishable goods. The proposed model addresses critical issues such as transportation delays, disruptions due to adverse traffic and weather conditions, and the necessity for coordinated interactions among distribution centers to minimize wastage and ensure product integrity. Using advanced mixed-integer linear program alongside state-of-the-art metaheuristic algorithms—such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and multi-objective particle swarm optimization—the framework optimizes key decision variables including transportation routes, delivery schedules, and inventory management under real-world constraints. The performance metrics used are: MID (mean ideal distance), SNS (spread of non-dominated solutions), RAS (rate of achievement simultaneously), and runtime. The computational results, averaged over the experimental runs, summarize algorithmic performance for small/medium and large problem sizes as follows. For small/medium instances (baseline: CPLEX), MOPSO achieves a 6.81% reduction in MID, a 0.88% reduction in SNS, a 23.56% reduction in RAS, and a 42.69% reduction in CPU time relative to CPLEX; NSGA‑II yields a 2.46% MID reduction, a 21.98% SNS reduction, a 25.04% RAS reduction, and a 1.04% decrease in CPU time relative to CPLEX. For large instances (baseline: MOPSO), NSGA‑II attains 5.12% lower SNS and 1.18% lower RAS than MOPSO but requires 37.33% more CPU time. Lower values of MID, RAS, and CPU time indicate better performance; higher SNS values indicate better performance. By incorporating risk management and equitable resource allocation in the proposed model, the framework is expected to enhance supply‑chain responsiveness, leading to improved customer satisfaction and a more balanced distribution of operational risk. This study contributes to both the theoretical development of sustainable supply‑chain management and its practical application in industries dominated by perishable products.