A Novel Hybrid Algorithm for Designing a Sustainable Supply Chain of CAR-T Therapy in a Multi-Objective Mode Considering Disease Relapse | ||
| Journal of Engineering Management and Soft Computing | ||
| دوره 12، شماره 1 - شماره پیاپی 22، فروردین 2026، صفحه 179-204 اصل مقاله (940.86 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22091/jemsc.2026.12874.1275 | ||
| نویسندگان | ||
| Arman Sadeghi Sabzevary1؛ Hossein Amoozad Khalili* 2؛ Mohammad Amirkhan3؛ Seyed Mohammad Hassan Hosseini4 | ||
| 1Department of Industrial Engineering, AK.C., Islamic Azad University, Aliabad Katoul, Iran. Email: sadeghisabzevary@iau.ac.ir | ||
| 2Department of Industrial Engineering, Sar.C., Islamic Azad University, Sari, Iran. Email: ho.amoozad@iau.ac.ir | ||
| 3Department of Industrial Engineering, AK.C., Islamic Azad University, Aliabad Katoul, Iran. Email: m.amirkhan@iau.ac.ir | ||
| 4Department of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran. Email: sh.hosseini@shahroodut.ac.ir | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Therapeutic Sustainable Supply chain؛ CAR-T cell therapy؛ Multi-Objective Optimization؛ Hybrid Metaheuristic Algorithm؛ Disease Relapse | ||
| مراجع | ||
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