A multi-objective stochastic programming model for post-disaster management

This paper develops a mathematical model for post-disaster planning with human casualties, which can be considered as operational guidance for the proper use of emergency resources. For this purpose, a stochastic mixed-integer programming model is provided to formulate the problem. The objective fun...

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Veröffentlicht in:Transportmetrica (Abingdon, Oxfordshire, UK) Jg. 18; H. 3; S. 1103 - 1126
Hauptverfasser: Gharib, Mehrdad, Fatemi Ghomi, Seyyed Mohammad Taghi, Jolai, Fariborz
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 02.12.2022
Taylor & Francis Ltd
Schlagworte:
Algorithms
Casualties
Completion time
Disaster management
Earthquakes
Emergency medical services
Emergency procedures
Genetic algorithms
Heuristic methods
Integer programming
Mathematical analysis
Mathematical programming
Mixed integer
multi-objective
multi-objective simulated annealing
non-dominated sorting genetic algorithm
Simulated annealing
Sorting algorithms
Stochastic models
Stochastic programming
ϵ-constraint method
Iran
ISSN:2324-9935, 2324-9943
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Zusammenfassung:This paper develops a mathematical model for post-disaster planning with human casualties, which can be considered as operational guidance for the proper use of emergency resources. For this purpose, a stochastic mixed-integer programming model is provided to formulate the problem. The objective functions of the model are (1) maximizing the survival probability of patients, (2) minimizing the maximum of completion time of treatment of all patients, and (3) minimizing the total cost of operations. The model is solved with the ϵ-constraint method. Due to the NP-hardness of the problem which is a significant challenge in the literature, two innovative meta-heuristic algorithms are proposed, i.e. a non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective simulated annealing (MOSA). Finally, a comprehensive computational analysis is performed for evaluation purposes. Also, a case study is made on the earthquake in Iran, which illustrates the real-world application of the model. Highlights A stochastic multi-objective mathematical programming model to allocate patients to hospitals and treat them. NSGA-II and MOSA are proposed, in addition to ϵ-constraint method as solution methods. Performance of two meta-heuristic algorithms is measured with five evaluation metrics. The study showed that NSGA-II is more effective than MOSA. The model was implemented on a real-world case, Tabriz earthquake in Iran.
Bibliographie:ObjectType-Article-1
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ISSN:2324-9935
2324-9943
DOI:10.1080/23249935.2021.1928790