{"title":"救灾网络设计问题:一种分布鲁棒优化方法","authors":"A. Hasani","doi":"10.52547/jimp.11.4.85","DOIUrl":null,"url":null,"abstract":"In this study, a robust two-stage risk-aversion optimization model is proposed for the multi-product relief network design problem. The comprehensive set of decisions for locating and reinforcing relief facilities, inventory planning, and distributing healthcare items has been considered in an integrated manner. Uncertainties of relief facility capacity, relief demand, and the node linkage capacity are considered. Moreover, the weighted average expected loss is considered in the proposed robust planning model. The efficiency of the proposed model has been evaluated by examining numerical instances. The obtained results indicate the efficiency of the distributionally robust model compared to the traditional two-stage stochastic model. In addition, the type of ambiguous set and levels of confidence, risk aversion, and adjustment parameters will affect network performance.","PeriodicalId":303885,"journal":{"name":"Journal of Industrial Management Perspective","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Relief Network Design Problem: A Distributionally Robust Optimization Approach\",\"authors\":\"A. Hasani\",\"doi\":\"10.52547/jimp.11.4.85\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, a robust two-stage risk-aversion optimization model is proposed for the multi-product relief network design problem. The comprehensive set of decisions for locating and reinforcing relief facilities, inventory planning, and distributing healthcare items has been considered in an integrated manner. Uncertainties of relief facility capacity, relief demand, and the node linkage capacity are considered. Moreover, the weighted average expected loss is considered in the proposed robust planning model. The efficiency of the proposed model has been evaluated by examining numerical instances. The obtained results indicate the efficiency of the distributionally robust model compared to the traditional two-stage stochastic model. In addition, the type of ambiguous set and levels of confidence, risk aversion, and adjustment parameters will affect network performance.\",\"PeriodicalId\":303885,\"journal\":{\"name\":\"Journal of Industrial Management Perspective\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Industrial Management Perspective\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.52547/jimp.11.4.85\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Industrial Management Perspective","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52547/jimp.11.4.85","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Relief Network Design Problem: A Distributionally Robust Optimization Approach
In this study, a robust two-stage risk-aversion optimization model is proposed for the multi-product relief network design problem. The comprehensive set of decisions for locating and reinforcing relief facilities, inventory planning, and distributing healthcare items has been considered in an integrated manner. Uncertainties of relief facility capacity, relief demand, and the node linkage capacity are considered. Moreover, the weighted average expected loss is considered in the proposed robust planning model. The efficiency of the proposed model has been evaluated by examining numerical instances. The obtained results indicate the efficiency of the distributionally robust model compared to the traditional two-stage stochastic model. In addition, the type of ambiguous set and levels of confidence, risk aversion, and adjustment parameters will affect network performance.