Hesam Adarang, A. Bozorgi-Amiri, K. Khalili-Damghani, R. Tavakkoli-Moghaddam
{"title":"一种用于提供紧急医疗服务的稳健双目标定位路由模型","authors":"Hesam Adarang, A. Bozorgi-Amiri, K. Khalili-Damghani, R. Tavakkoli-Moghaddam","doi":"10.1108/jhlscm-11-2018-0072","DOIUrl":null,"url":null,"abstract":"PurposeThis paper addresses a location-routing problem (LRP) under uncertainty for providing emergency medical services (EMS) during disasters, which is formulated using a robust optimization (RO) approach. The objectives consist of minimizing relief time and the total cost including location costs and the cost of route coverage by the vehicles (ambulances and helicopters).Design/methodology/approachA shuffled frog leaping algorithm (SFLA) is developed to solve the problem and the performance is assessed using both the ε-constraint method and NSGA-II algorithm. For a more accurate validation of the proposed algorithm, the four indicators of dispersion measure (DM), mean ideal distance (MID), space measure (SM), and the number of Pareto solutions (NPS) are used.FindingsThe results obtained indicate the efficiency of the proposed algorithm within a proper computation time compared to the CPLEX solver as an exact method.Research limitations/implicationsIn this study, the planning horizon is not considered in the model which can affect the value of parameters such as demand. Moreover, the uncertain nature of the other parameters such as traveling time is not incorporated into the model.Practical implicationsThe outcomes of this research are helpful for decision-makers for the planning and management of casualty transportation under uncertain environment. The proposed algorithm can obtain acceptable solutions for real-world cases.Originality/valueA novel robust mixed-integer linear programming (MILP) model is proposed to formulate the problem as a LRP. To solve the problem, two efficient metaheuristic algorithms were developed to determine the optimal values of objectives and decision variables.","PeriodicalId":46575,"journal":{"name":"Journal of Humanitarian Logistics and Supply Chain Management","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2020-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1108/jhlscm-11-2018-0072","citationCount":"18","resultStr":"{\"title\":\"A robust bi-objective location-routing model for providing emergency medical services\",\"authors\":\"Hesam Adarang, A. Bozorgi-Amiri, K. Khalili-Damghani, R. Tavakkoli-Moghaddam\",\"doi\":\"10.1108/jhlscm-11-2018-0072\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PurposeThis paper addresses a location-routing problem (LRP) under uncertainty for providing emergency medical services (EMS) during disasters, which is formulated using a robust optimization (RO) approach. The objectives consist of minimizing relief time and the total cost including location costs and the cost of route coverage by the vehicles (ambulances and helicopters).Design/methodology/approachA shuffled frog leaping algorithm (SFLA) is developed to solve the problem and the performance is assessed using both the ε-constraint method and NSGA-II algorithm. For a more accurate validation of the proposed algorithm, the four indicators of dispersion measure (DM), mean ideal distance (MID), space measure (SM), and the number of Pareto solutions (NPS) are used.FindingsThe results obtained indicate the efficiency of the proposed algorithm within a proper computation time compared to the CPLEX solver as an exact method.Research limitations/implicationsIn this study, the planning horizon is not considered in the model which can affect the value of parameters such as demand. Moreover, the uncertain nature of the other parameters such as traveling time is not incorporated into the model.Practical implicationsThe outcomes of this research are helpful for decision-makers for the planning and management of casualty transportation under uncertain environment. 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A robust bi-objective location-routing model for providing emergency medical services
PurposeThis paper addresses a location-routing problem (LRP) under uncertainty for providing emergency medical services (EMS) during disasters, which is formulated using a robust optimization (RO) approach. The objectives consist of minimizing relief time and the total cost including location costs and the cost of route coverage by the vehicles (ambulances and helicopters).Design/methodology/approachA shuffled frog leaping algorithm (SFLA) is developed to solve the problem and the performance is assessed using both the ε-constraint method and NSGA-II algorithm. For a more accurate validation of the proposed algorithm, the four indicators of dispersion measure (DM), mean ideal distance (MID), space measure (SM), and the number of Pareto solutions (NPS) are used.FindingsThe results obtained indicate the efficiency of the proposed algorithm within a proper computation time compared to the CPLEX solver as an exact method.Research limitations/implicationsIn this study, the planning horizon is not considered in the model which can affect the value of parameters such as demand. Moreover, the uncertain nature of the other parameters such as traveling time is not incorporated into the model.Practical implicationsThe outcomes of this research are helpful for decision-makers for the planning and management of casualty transportation under uncertain environment. The proposed algorithm can obtain acceptable solutions for real-world cases.Originality/valueA novel robust mixed-integer linear programming (MILP) model is proposed to formulate the problem as a LRP. To solve the problem, two efficient metaheuristic algorithms were developed to determine the optimal values of objectives and decision variables.
期刊介绍:
The Journal of Humanitarian Logistics and Supply Chain Management (JHLSCM) is targeted at academics and practitioners in humanitarian public and private sector organizations working on all aspects of humanitarian logistics and supply chain management. The journal promotes the exchange of knowledge, experience and new ideas between researchers and practitioners and encourages a multi-disciplinary and cross-functional approach to the resolution of problems and exploitations of opportunities within humanitarian supply chains. Contributions are encouraged from diverse disciplines (logistics, operations management, process engineering, health care, geography, management science, information technology, ethics, corporate social responsibility, disaster management, development aid, public policy) but need to have a logistics and/or supply chain focus. JHLSCM publishes state of the art research, utilizing both quantitative and qualitative approaches, in the field of humanitarian and development aid logistics and supply chain management.