{"title":"Multi-period emergency facility location-routing problems under uncertainty and risk aversion","authors":"Qing-Mi Hu, Yan Hu, Xiaoping Li","doi":"10.1016/j.seps.2024.102093","DOIUrl":null,"url":null,"abstract":"<div><div>This paper addresses a multi-period emergency facility location-routing problem, in which the uncertainties in material demands and transportation time, as well as dynamic inventory replenishment and carryover are incorporated in the design of multi-level emergency logistics networks. To measure the risks stemming from uncertain transportation time, mean-CVaR method is used. Then, a risk-averse stochastic programming model for the presented problem is formulated to minimize the total rescue time of the network. Moreover, a genetic algorithm is developed to solve the proposed model. Extensive numerical experiments including the randomly generated instances and a case study on the Wenchuan earthquake in China are conducted to verify the effectiveness of the presented model and algorithm. Experimental results show that the genetic algorithm significantly performs better than the Gurobi solver in terms of both solution quality and solution time.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"96 ","pages":"Article 102093"},"PeriodicalIF":6.2000,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Socio-economic Planning Sciences","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0038012124002933","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper addresses a multi-period emergency facility location-routing problem, in which the uncertainties in material demands and transportation time, as well as dynamic inventory replenishment and carryover are incorporated in the design of multi-level emergency logistics networks. To measure the risks stemming from uncertain transportation time, mean-CVaR method is used. Then, a risk-averse stochastic programming model for the presented problem is formulated to minimize the total rescue time of the network. Moreover, a genetic algorithm is developed to solve the proposed model. Extensive numerical experiments including the randomly generated instances and a case study on the Wenchuan earthquake in China are conducted to verify the effectiveness of the presented model and algorithm. Experimental results show that the genetic algorithm significantly performs better than the Gurobi solver in terms of both solution quality and solution time.
期刊介绍:
Studies directed toward the more effective utilization of existing resources, e.g. mathematical programming models of health care delivery systems with relevance to more effective program design; systems analysis of fire outbreaks and its relevance to the location of fire stations; statistical analysis of the efficiency of a developing country economy or industry.
Studies relating to the interaction of various segments of society and technology, e.g. the effects of government health policies on the utilization and design of hospital facilities; the relationship between housing density and the demands on public transportation or other service facilities: patterns and implications of urban development and air or water pollution.
Studies devoted to the anticipations of and response to future needs for social, health and other human services, e.g. the relationship between industrial growth and the development of educational resources in affected areas; investigation of future demands for material and child health resources in a developing country; design of effective recycling in an urban setting.