Multi-Product, Multi-Period Sustainable Perishable Supply Chain Optimization with Uncertainty Navigation

This study creates a resilient and sustainable supply chain framework for perishable goods that harmonizes economic, environmental, and social goals. By tackling the intricacies associated with multi-tiered, multi-product, and multi-temporal systems, the study enhances supply chain efficiency in the face of uncertainty. The primary goal is to reduce costs, minimize environmental impacts, and improve service levels, while incorporating the unique characteristics of the dairy and pharmaceutical industries. The study utilizes a multi-objective mixed-integer linear programming framework to enhance the sustainability of a supply chain dedicated to perishable goods in the face of uncertainty. The model is validated using experimental data, solved with GAMS software and CPLEX solver, and further analyzed through the NSGA-II meta-heuristic algorithm and a modified epsilon constraint method. Comparative evaluations assess the performance and efficiency of these optimization techniques, highlighting their applicability in diverse supply chain scenarios. The study demonstrates that the NSGA-II algorithm outperforms the modified epsilon constraint algorithm in handling large-scale supply chain optimization problems, offering faster computation and more diverse Pareto-optimal solutions. Conversely, the epsilon constraint method provides greater precision and accuracy for smaller, less complex problems. The proposed models effectively balance economic, environmental, and social objectives, showcasing their applicability in designing sustainable and robust supply chains for perishable products under uncertainty.