فهرست نشریات علمی دسترسی آزاد ایران

This study investigates the sustainable supply chain network design problem in the healthcare sector, where patients with cancer are treated using CAR-T cell therapy. To better reflect real-world conditions, the possibility of disease relapse is incorporated into the problem formulation. The problem is modeled as a multi-objective mixed-integer programming (MIP) problem, aiming to minimize total costs, reduce environmental impacts, and maximize social satisfaction and accessibility. Given the NP-hard nature of the problem, a novel hybrid metaheuristic algorithm is developed to solve large-scale instances. The proposed algorithm is a structured integration of three evolutionary methods: Non-dominated Sorting Genetic Algorithm IV (NSGA-IV) for preserving diversity and Pareto front coverage, S-Metric Selection Evolutionary Multi-Objective Algorithm (SMS-EMOA) for enhancing precision and hypervolume expansion, and the Epsilon-dominance Evolutionary Multi-objective Algorithm (ε-MOEA) for rapid initial convergence. These three were selected as their combined strengths ensure a balanced trade-off between exploration, convergence speed, and final accuracy, which cannot be achieved by any of them individually. The proposed hybrid algorithm employs a two-stage selection mechanism, an adaptive mutation strategy, and a dynamic external archive to generate high-quality solutions across the Pareto front. Numerical experiments across different problem scales confirm its superiority, yielding on average 30% more non-dominated solutions, a 3% reduction in costs, and a 2 to 3% decrease in environmental impacts compared to single algorithms. The findings demonstrate this hybrid approach potential to enhance both strategic and operational decision-making in resilient healthcare delivery networks.