نشریه بین المللی مهندسی صنایع و تحقیقات تولید
دانشگاه علم و صنعت ایران
چهارشنبه 13 آبان 1405
[Archive]
چکیده: (46 مشاهده)
Fresh agri-food e-commerce operates through complex multi-echelon supply chains, where perishability and transportation increase both operational costs and carbon emissions. Although previous studies have relied on mixed-integer linear programming (MILP) and exact optimization methods, the performance of metaheuristics under carbon-constrained conditions remains largely unexplored. This study extends a carbon-constrained MILP model by incorporating single-sourcing decisions and a planning-horizon emission cap. A feasibility-preserving random-key decoder with a shadow carbon price gene is embedded into Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), both calibrated using a Taguchi L9 design. In a real case study in Java, Indonesia, PSO achieved IDR 605.67 billion, only 0.44% below the exact optimum of IDR 608.33 billion, whereas GA recorded a 5.19% gap. PSO significantly outperformed GA (Mann-Whitney U test, p = 0.0002). The proposed framework provides a solver-independent approach for carbon-constrained agri-food supply chain optimization.
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