Developing a robust multi-objective model for pre/post disaster times under uncertainty in demand and resource

Studies show that by the course of time, the number of natural disasters such as earthquakes is increasing. Therefore, developing a model for locating distribution centers and relief goods distribution systems in disaster times, along with appropriately locating health centers with the ease of acces...

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Veröffentlicht in:	Journal of cleaner production Jg. 154; S. 188 - 202
Hauptverfasser:	Haghi, Mahbubeh, Fatemi Ghomi, Seyed Mohammad Taghi, Jolai, Fariborz
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 15.06.2017
Schlagworte:	algorithms disasters earthquakes hospitals Humanitarian relief logistics Non-dominant sorting genetic algorithm Robust stochastic optimization Simulated annealing algorithm uncertainty ε – constraint exact method Simulated annealing algorithm Robust stochastic optimization Humanitarian relief logistics Non-dominant sorting genetic algorithm ε – constraint exact method
ISSN:	0959-6526, 1879-1786
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Abstract	Studies show that by the course of time, the number of natural disasters such as earthquakes is increasing. Therefore, developing a model for locating distribution centers and relief goods distribution systems in disaster times, along with appropriately locating health centers with the ease of access for transferring the casualties and saving their lives, is among the most essential concerns in relief logistics. Considering these two subjects, simultaneously, results in an increase in the quality of service in disaster zones. In this study, a multi-objective programming model is developed for locating relief goods distribution centers and health centers along with distributing relief goods and transferring the casualties to health centers, with pre/post-disaster budget constraints for goods and casualties logistics. For a better modelling of the reality, the uncertainties in demand, supply, and cost parameters are included in the model. Also, facility failure (e.g. relief distribution centers, health centers, hospitals and supply points failure) due to earthquakes is considered. The proposed model maximizes the response level to medical needs of the casualties, while targeting the justly distribution of relief goods and minimizing the total costs of preparedness and response phases. In order to handle the uncertainties, the robust optimization approach is utilized. The model is solved with ε – constraint method. For the large sized form, the MOGASA algorithm is proposed, and the results are compared to those of the NSGAII algorithm. Then the validity and efficiency of the proposed algorithm is explored based on the results of both the proposed and exact methods. •A relief logistics model for simultaneous planning of relief distribution and casualty transfer.•The uncertainty is considered in demand, resources, relief distribution and casualty transfer costs.•The failure of the facilities due to the earthquakes is considered.•The robust approach is utilized and the ε-constraint method is applied to pareto solutions.•For the case of large sized instances the MOGASA and NSGAII metaheuristics are proposed.
AbstractList	Studies show that by the course of time, the number of natural disasters such as earthquakes is increasing. Therefore, developing a model for locating distribution centers and relief goods distribution systems in disaster times, along with appropriately locating health centers with the ease of access for transferring the casualties and saving their lives, is among the most essential concerns in relief logistics. Considering these two subjects, simultaneously, results in an increase in the quality of service in disaster zones. In this study, a multi-objective programming model is developed for locating relief goods distribution centers and health centers along with distributing relief goods and transferring the casualties to health centers, with pre/post-disaster budget constraints for goods and casualties logistics. For a better modelling of the reality, the uncertainties in demand, supply, and cost parameters are included in the model. Also, facility failure (e.g. relief distribution centers, health centers, hospitals and supply points failure) due to earthquakes is considered. The proposed model maximizes the response level to medical needs of the casualties, while targeting the justly distribution of relief goods and minimizing the total costs of preparedness and response phases. In order to handle the uncertainties, the robust optimization approach is utilized. The model is solved with ε – constraint method. For the large sized form, the MOGASA algorithm is proposed, and the results are compared to those of the NSGAII algorithm. Then the validity and efficiency of the proposed algorithm is explored based on the results of both the proposed and exact methods. •A relief logistics model for simultaneous planning of relief distribution and casualty transfer.•The uncertainty is considered in demand, resources, relief distribution and casualty transfer costs.•The failure of the facilities due to the earthquakes is considered.•The robust approach is utilized and the ε-constraint method is applied to pareto solutions.•For the case of large sized instances the MOGASA and NSGAII metaheuristics are proposed. Studies show that by the course of time, the number of natural disasters such as earthquakes is increasing. Therefore, developing a model for locating distribution centers and relief goods distribution systems in disaster times, along with appropriately locating health centers with the ease of access for transferring the casualties and saving their lives, is among the most essential concerns in relief logistics. Considering these two subjects, simultaneously, results in an increase in the quality of service in disaster zones. In this study, a multi-objective programming model is developed for locating relief goods distribution centers and health centers along with distributing relief goods and transferring the casualties to health centers, with pre/post-disaster budget constraints for goods and casualties logistics. For a better modelling of the reality, the uncertainties in demand, supply, and cost parameters are included in the model. Also, facility failure (e.g. relief distribution centers, health centers, hospitals and supply points failure) due to earthquakes is considered. The proposed model maximizes the response level to medical needs of the casualties, while targeting the justly distribution of relief goods and minimizing the total costs of preparedness and response phases. In order to handle the uncertainties, the robust optimization approach is utilized. The model is solved with ε – constraint method. For the large sized form, the MOGASA algorithm is proposed, and the results are compared to those of the NSGAII algorithm. Then the validity and efficiency of the proposed algorithm is explored based on the results of both the proposed and exact methods.
Author	Fatemi Ghomi, Seyed Mohammad Taghi Haghi, Mahbubeh Jolai, Fariborz
Author_xml	– sequence: 1 givenname: Mahbubeh surname: Haghi fullname: Haghi, Mahbubeh organization: Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, Tehran, Iran – sequence: 2 givenname: Seyed Mohammad Taghi surname: Fatemi Ghomi fullname: Fatemi Ghomi, Seyed Mohammad Taghi email: fatemi@aut.ac.ir organization: Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, Tehran, Iran – sequence: 3 givenname: Fariborz surname: Jolai fullname: Jolai, Fariborz organization: Department of Industrial Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran
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Keywords	Simulated annealing algorithm Robust stochastic optimization Humanitarian relief logistics Non-dominant sorting genetic algorithm ε – constraint exact method
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Snippet	Studies show that by the course of time, the number of natural disasters such as earthquakes is increasing. Therefore, developing a model for locating...
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SubjectTerms	algorithms disasters earthquakes hospitals Humanitarian relief logistics Non-dominant sorting genetic algorithm Robust stochastic optimization Simulated annealing algorithm uncertainty ε – constraint exact method
Title	Developing a robust multi-objective model for pre/post disaster times under uncertainty in demand and resource
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