A Novel Hybrid Algorithm for Designing a Sustainable Supply Chain of CAR-T Therapy in a Multi-Objective Mode Considering Disease Relapse

Document Type : Original Article

Authors

1 Department of Industrial Engineering, AK.C., Islamic Azad University, Aliabad Katoul, Iran. Email: sadeghisabzevary@iau.ac.ir

2 Department of Industrial Engineering, Sar.C., Islamic Azad University, Sari, Iran. Email: ho.amoozad@iau.ac.ir

3 Department of Industrial Engineering, AK.C., Islamic Azad University, Aliabad Katoul, Iran. Email: m.amirkhan@iau.ac.ir

4 Department of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran. Email: sh.hosseini@shahroodut.ac.ir

Abstract

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.

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References

Ala, A., Simic, V., Bacanin, N., & Tirkolaee, E. B. (2024). Blood supply chain network design with lateral freight: A robust possibilistic optimization model. Engineering Applications of Artificial Intelligence, 133, 108053. https://doi.org/10.1016/j.engappai.2024.108053
Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R. L., Soerjomataram, I., & Jemal, A. (2024). Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74(3), 229-263. https://doi.org/10.3322/caac.21492.
Camacho-Villalón, C., Dorigo, M., & Stützle, T. (2025). METAFOR: A hybrid metaheuristics software framework for single-objective continuous optimization problems. arXiv preprint arXiv:2502.11225. https://doi.org/10.48550/arXiv.2502.11225
Dada, S. A., Azai, J. S., Umoren, J., Utomi, E., & Akonor, B. G. (2025). Strengthening US healthcare supply chain resilience through data-driven strategies to ensure consistent access to essential medicines. International Journal of Research Publications, 164(1), 10-10. https://doi.org/10.47119/IJRP1001641120257438
Fallahi, A., Mousavian Anaraki, S. A., Mokhtari, H., & Niaki, S. T. A. (2024). Blood plasma supply chain planning to respond COVID-19 pandemic: A case study. Environment, Development and Sustainability, 26(1), 1965-2016. https://doi.org/10.1007/s10668-022-02793-7
Goodarzian, F., Taleizadeh, A. A., Ghasemi, P., & Abraham, A. (2021). An integrated sustainable medical supply chain network during COVID-19. Engineering Applications of Artificial Intelligence, 100, 104188. https://doi.org/10.1016/j.engappai.2021.104188
Hayden, P. J., Roddie, C., Bader, P., Basak, G. W., Bonig, H., Bonini, C., Chabannon, C., Ciceri, F., Corbacioglu, S., Ellard, R., Sanchez-Guijo, F., Jäger, U., Hildebrandt, M., Hudecek, M., Kersten, M. J., Köhl, U., Kuball, J., … & Yakoub-Agha, I. (2022). Management of adults and children receiving CAR T-cell therapy: 2021 best practice recommendations of the European Society for Blood and Marrow Transplantation (EBMT) and the Joint Accreditation Committee of ISCT and EBMT (JACIE) and the European Haematology Association (EHA). Annals of Oncology, 33(3), 259-275. https://doi.org/10.1016/j.annonc.2021.12.003
Herdianto, B., Billot, R., Lucas, F., Sevaux, M., & Vigo, D. (2025). Hybrid node-destroyer model with large neighborhood search for solving the capacitated vehicle routing problem. arXiv preprint arXiv:2508.08659. https://doi.org/10.48550/arXiv.2508.08659
Jacoby, E. (2019). The role of allogeneic HSCT after CAR T cells for acute lymphoblastic leukemia. Bone Marrow Transplantation, 54(2), 810-814. https://doi.org/10.1038/s41409-019-0604-3.
Javadi Gargari, F., Sayad, M., Posht Mashhadi, S. A., Sadrnia, A., Nedjati, A., & Yousefi Golafshani, T. (2021). Five‐Echelon multiobjective health services supply chain modeling under disruption. Mathematical Problems in Engineering, 2021(1), 5587392. https://doi.org/10.1155/2021/5587392
Jemai, J., Do Chung, B., & Sarkar, B. (2020). Environmental effect for a complex green supply-chain management to control waste: A sustainable approach. Journal of Cleaner Production, 277, 122919. https://doi.org/10.1016/j.jclepro.2020.122919
Karakostas, P., Panoskaltsis, N., Mantalaris, A., & Georgiadis, M. C. (2020). Optimization of CAR T-cell therapies supply chains. Computers & Chemical Engineering, 139, 106913. https://doi.org/10.1016/j.compchemeng.2020.106913
Kargar, B., MohajerAnsari, P., Büyüktahtakın, İ. E., Jahani, H., & Talluri, S. (2024). Data-driven modeling for designing a sustainable and efficient vaccine supply chain: A COVID-19 case study. Transportation Research Part E: Logistics and Transportation Review, 184, 103494. https://doi.org/10.1016/j.tre.2024.103494
KhajavandSany, T., AmoozadKhalil, H., Rezaeian, R., & Nemati, K. (2024). Optimization of multi-objective simulation of excavator-truck loading system for mining minerals. Journal of Engineering Management & Soft Computing, 10(2), 182-203. https://doi.org/10.22091/jemsc.2025.11807.1229
Lam, C., Meinert, E., Yang, A., & Cui, Z. (2021). Comparison between centralized and decentralized supply chains of autologous chimeric antigen receptor T-cell therapies: A UK case study based on discrete event simulation. Cytotherapy, 23(5), 433-451. https://doi.org/10.1016/j.jcyt.2020.08.007
Lam, C., Meinert, E., Yang, A., & Cui, Z. (2022). Impact of fast-track regulatory designations on strategic commercialization decisions for autologous cell therapies. Regenerative Medicine, 17(3), 155-174. https://doi.org/10.2217/rme-2021-0061
Mansur, A., Handayani, D. I., Wangsa, I. D., Utama, D. M., & Jauhari, W. A. (2023). A mixed-integer linear programming model for sustainable blood supply chain problems with shelf-life time and multiple blood types. Decision Analytics Journal, 8, 100279.  https://doi.org/10.1016/j.dajour.2023.100279.
Nazemi, H., Yousefinejad-Atari, M., & Ghaffari, A. (2022). Development and solution of a three-tier supply chain model to improve quality and reduce probabilistic delivery time. Engineering Management & Soft Computing, 7(2), 145–177‎.  https://doi.org/10.22091/jemsc.2018.1544.1046
Papathanasiou, M. M., Stamatis, C., Lakelin, M., Farid, S., Titchener-Hooker, N., & Shah, N. (2020). Autologous CAR T-cell therapies supply chain: Challenges and opportunities? Cancer Gene Therapy, 27(10), 799-809. https:/doi.org/10.1038/s41417-019-0157-z
Rekabi, S., Garjan, H. S., Goodarzian, F., Pamucar, D., & Kumar, A. (2024). Designing a responsive-sustainable-resilient blood supply chain network considering congestion by linear regression method. Expert Systems with Applications, 245, 122976. https://doi.org/10.1016/j.eswa.2023.122976
Rodriguez, D., Sued, M., & Valdora, M. (2025). A Kruskal-Wallis type test for functional data. Communications in Statistics-Simulation and Computation, 1-15. https://doi.org/10.1080/03610918.2025.2455418
Sajjadi, S. J., Sajjadi, M., & Sabzevari, M. (2022). Solving the index-tracking problem using a hybrid firefly metaheuristic algorithm. Engineering Management & Soft Computing, 7(1), 127–146. https://doi.org/10.22091/jemsc.2016.585
Shayannia, S. A. (2023). Presenting an agile supply chain mathematical model for COVID-19 (Corona) drugs using metaheuristic algorithms (Case study: Pharmaceutical industry). Environmental Science and Pollution Research, 30(3), 6559-6572. https://doi.org/10.1007/s11356-022-22608-6.
Torrado, A., & Barbosa-Póvoa, A. (2022). Towards an optimized and sustainable blood supply chain network under uncertainty: A literature review. Cleaner Logistics and Supply Chain, 3, 100028. https://doi.org/10.1016/j.clscn.2022.100028.
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Engineering Management and Soft Computing
Volume 12, Issue 1 - Serial Number 22
January 2026
Pages 179-204

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