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<p>This study addresses a significant challenge in healthcare logistics for optimization of organ transplant transportation networks. This study is especially critical for patients with life-threatening conditions such as end-stage liver and heart diseases, where the timely transfer of organs and patients between origin and destination&#xa0;hospitals is paramount. The quality of the organ and the success of the transplant can hinge on mere seconds. To tackle this challenge, we develop an optimization model that integrates routing and scheduling for organ transplantation, encompassing the delivery logistics and routing of ambulances carrying organs. Key contributions of this study include the incorporation of real-world constraints such as cold ischemia time, urban traffic conditions, and limited fleet capacity. We validate the model’s applicability through a case study in Tehran, Iran. To solve the optimization problem, we propose a hybrid metaheuristic that integrates a constructive heuristic, simulated annealing with a randomized neighborhood search strategy, and the social engineering concept. The superior performance of our algorithm is rigorously validated against the CPLEX solver and benchmarked against state-of-the-art metaheuristic algorithms from the existing literature. Finally, sensitivity analyses confirm the model’s feasibility and applicability in real-world scenarios for concluding some managerial insights for the organ transplant transportation networks.</p>

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An efficient metaheuristic algorithm for the organ transplant logistics network considering urban traffic and cold ischemia constraints

  • 期刊论文

  • 发表日期：

    2025年7月12日

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  • Amir M. Fathollahi-Fard,
  • H. Amoozad Khalili,
  • S. M. J. Mirzapour Al-e-hashem

摘要

This study addresses a significant challenge in healthcare logistics for optimization of organ transplant transportation networks. This study is especially critical for patients with life-threatening conditions such as end-stage liver and heart diseases, where the timely transfer of organs and patients between origin and destination hospitals is paramount. The quality of the organ and the success of the transplant can hinge on mere seconds. To tackle this challenge, we develop an optimization model that integrates routing and scheduling for organ transplantation, encompassing the delivery logistics and routing of ambulances carrying organs. Key contributions of this study include the incorporation of real-world constraints such as cold ischemia time, urban traffic conditions, and limited fleet capacity. We validate the model’s applicability through a case study in Tehran, Iran. To solve the optimization problem, we propose a hybrid metaheuristic that integrates a constructive heuristic, simulated annealing with a randomized neighborhood search strategy, and the social engineering concept. The superior performance of our algorithm is rigorously validated against the CPLEX solver and benchmarked against state-of-the-art metaheuristic algorithms from the existing literature. Finally, sensitivity analyses confirm the model’s feasibility and applicability in real-world scenarios for concluding some managerial insights for the organ transplant transportation networks.