In this study, we examine the emergency medical transportation system in the Sri Lankan public health service and develop an efficient routing framework for inter-hospital patient transfers, where patients are moved between hospitals according to clinical requirements and hospital capability levels. In this transfer process, medical experts emphasize that the minimum risk time of each patient should not be exceeded. To address this problem, we propose a mixed-integer programming (MIP) model and test it on random instances with up to 25 patients. Computational results showed that the MIP approach fails to obtain a feasible solution within a 300-second time limit. To solve real-world cases with up to 132 patients, we propose three heuristic approaches. The first is a two-phase method that combines machine learning with a simplified MIP model that reduces the feasible region (ML-MIP). The second heuristic, TP-VRP, is also a two-phase approach in which patient–hospital assignments are determined in the first phase, and the remaining problem is solved as a vehicle routing problem with time windows (VRP-TW) in the second phase. The third heuristic, RCPSP-VRP, combines a resource-constrained project scheduling problem for hospital assignments with the VRP-TW solution approach used in the second method. On 15 tested real-world instances, TP-VRP and RCPSP-VRP obtained feasible solutions for all cases, whereas MIP and ML-MIP failed to obtain feasible solutions for 8 out of 15 instances within 300 seconds. Compared to MIP, RCPSP-VRP also reduced average runtime by approximately 98.3% on the seven instances where both methods yielded feasible solutions, and achieved equal or better objective values, improving two instances.
Citation: Sudheeraka Wickramarachchi, Kazuki Hasegawa, Wei Wu. Inter-hospital ambulance routing in Sri Lanka[J]. Journal of Industrial and Management Optimization, 2026, 22(6): 2843-2865. doi: 10.3934/jimo.2026104
In this study, we examine the emergency medical transportation system in the Sri Lankan public health service and develop an efficient routing framework for inter-hospital patient transfers, where patients are moved between hospitals according to clinical requirements and hospital capability levels. In this transfer process, medical experts emphasize that the minimum risk time of each patient should not be exceeded. To address this problem, we propose a mixed-integer programming (MIP) model and test it on random instances with up to 25 patients. Computational results showed that the MIP approach fails to obtain a feasible solution within a 300-second time limit. To solve real-world cases with up to 132 patients, we propose three heuristic approaches. The first is a two-phase method that combines machine learning with a simplified MIP model that reduces the feasible region (ML-MIP). The second heuristic, TP-VRP, is also a two-phase approach in which patient–hospital assignments are determined in the first phase, and the remaining problem is solved as a vehicle routing problem with time windows (VRP-TW) in the second phase. The third heuristic, RCPSP-VRP, combines a resource-constrained project scheduling problem for hospital assignments with the VRP-TW solution approach used in the second method. On 15 tested real-world instances, TP-VRP and RCPSP-VRP obtained feasible solutions for all cases, whereas MIP and ML-MIP failed to obtain feasible solutions for 8 out of 15 instances within 300 seconds. Compared to MIP, RCPSP-VRP also reduced average runtime by approximately 98.3% on the seven instances where both methods yielded feasible solutions, and achieved equal or better objective values, improving two instances.
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Sudheeraka Wickramarachchi, Kazuki Hasegawa, Wei Wu. Inter-hospital ambulance routing in Sri Lanka[J]. Journal of Industrial and Management Optimization, 2026, 22(6): 2843-2865. doi: 10.3934/jimo.2026104
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