| Abstrakt: |
In recent years, the establishment of competitive markets has led researchers to pay more attention to the subject of supply chain design and competition in their studies. In this research, a multi-objective mathematical model is proposed for the design of a dynamic, integrated network in a competitive, sustainable, and resilient closed-loop supply chain for perishable goods under disruptions. In this model, competition between two chains is examined with a focus on economic, environmental, social, and resilience considerations. To solve this competitive model, a two-stage approach is used. In the first stage, game theory is employed to determine equilibrium values in competitive decisions, and considering the complexities of the model, the Multi Objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm (NSGA-II) metaheuristic algorithms are used to solve the supply chain design problem. To evaluate the efficiency of the model and the proposed solution approach, the performance of each algorithm is analyzed based on five criteria: computation time, distance, average distance from the ideal solution, diversity, and the number of solutions examined, using random numerical examples. The results are analyzed graphically and statistically. In comparison to the NSGA-II algorithm, the MOPSO algorithm demonstrates better performance in terms of all criteria, with average improvements of 36.5% in distance, 33.9% in average distance from the ideal solution, 20.8% in diversity, and 79.6% in the number of solutions examined. The results indicate the effectiveness of the proposed approach and model in designing sustainable and resilient supply chain networks under competition for perishable goods. [ABSTRACT FROM AUTHOR] |
