Capacity Allocation For A Green Supply Chain Using Simulated Annealing
DOI:
https://doi.org/10.23055/ijietap.2025.32.5.11131Keywords:
Green supply chain management (GSCM), Bi-Objective Optimization, Simulated annealing, Pareto optimalAbstract
We consider a green supply chain that minimizes both economic cost and carbon dioxide emissions. The problem is considered a bi-objective optimization problem that requires a comprehensive approach to consider all stages of the supply chain, from design to end-of-life management. It aims to minimize environmental impact while maintaining economic viability. We present a simulated annealing-based optimization model that will be used to build a small Pareto set which is balancing the conflicting objectives of minimizing total economic cost and carbon dioxide emissions. The challenge here is how the decision-maker’s finds the best alternative solution from among the available alternative solutions in the Pareto set. The weighted sum method is used to rank the alternative solutions based on the decision maker experience and then to select an optimal solution. The model consists of three-echelon supply chain (sources, distribution center, retailers). The proposed method is implemented on a case study involving a supply chain model consists of three echelons in Jordan. The results demonstrate the algorithm's ability to identify a distribution model that closely approximates the global optimum.
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