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<p>The significance of multi-commodity sustainable supply chain issues is highlighted by various factors, such as environmental considerations, meeting customer expectations, and organizational financial reasons. This research presents a new multi-commodity supply chain network to decrease the network’s total costs and reduce emissions of environmental problems to reach to sustainability objective. The sectors in the presented approach include recovery and recycling units for parts as well as suppliers, producers, distribution centers, warehouses, and customers. A computational model is also developed for the suggested issue. Additionally, the implemented model is resolved on small and medium scales by utilizing weighted goal programming, and two metaheuristic algorithms are included in the non-dominated sorting genetic algorithm-II (NSGA-II). Particle swarm optimization (PSO) approaches can be utilized on large scales for research. Finally, numerical outcomes show that the suggested model is more accurate and can resolve the service level and shortage issues. The outcomes demonstrate the proposed PSO algorithm’s superiority over the NSGA-II method by 28% better performance.</p>

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Enhancing optimization models for multi-commodity sustainable supply chain networks and managing environmental challenges

  • 期刊论文

  • 发表日期：

    2026年1月16日

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  • Zongshan Wang,
  • Ali Ala,
  • Vladimir Simic,
  • Dragan Pamucar,
  • Nezir Aydin

摘要

The significance of multi-commodity sustainable supply chain issues is highlighted by various factors, such as environmental considerations, meeting customer expectations, and organizational financial reasons. This research presents a new multi-commodity supply chain network to decrease the network’s total costs and reduce emissions of environmental problems to reach to sustainability objective. The sectors in the presented approach include recovery and recycling units for parts as well as suppliers, producers, distribution centers, warehouses, and customers. A computational model is also developed for the suggested issue. Additionally, the implemented model is resolved on small and medium scales by utilizing weighted goal programming, and two metaheuristic algorithms are included in the non-dominated sorting genetic algorithm-II (NSGA-II). Particle swarm optimization (PSO) approaches can be utilized on large scales for research. Finally, numerical outcomes show that the suggested model is more accurate and can resolve the service level and shortage issues. The outcomes demonstrate the proposed PSO algorithm’s superiority over the NSGA-II method by 28% better performance.