{"title":"A hybrid genetic algorithm to size the hospital resources in the case of a massive influx of victims","authors":"Abderrahmane Ben Kacem, Oualid Kamach, S. Chafik","doi":"10.1109/LOGISTIQUA.2019.8907324","DOIUrl":null,"url":null,"abstract":"This paper describes a hybrid approach to size the hospital resources in the cases of a massive influx of victims generated by a disaster situation (natural or made man disaster). This suggested approach based on the genetic algorithm is a blending between the simulation (ARENA) and machine learning (Neural Networks). The first one produces a matrix of theoretical solutions and the second one contributes a solution based on the feedback on experiences. This method provided a reliable and efficient solution based on available resources and on a solutions applied in real cases. The result shows that the genetic algorithm provided a new solution that improves the solutions got by the simulation. Also we made an application as a decision support tool for hospital decision-makers to provide with the needs of resources in the same cases. This work is being carried out in collaboration with the Mohammed 5 hospital center in Casablanca (Morocco).","PeriodicalId":435919,"journal":{"name":"2019 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LOGISTIQUA.2019.8907324","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper describes a hybrid approach to size the hospital resources in the cases of a massive influx of victims generated by a disaster situation (natural or made man disaster). This suggested approach based on the genetic algorithm is a blending between the simulation (ARENA) and machine learning (Neural Networks). The first one produces a matrix of theoretical solutions and the second one contributes a solution based on the feedback on experiences. This method provided a reliable and efficient solution based on available resources and on a solutions applied in real cases. The result shows that the genetic algorithm provided a new solution that improves the solutions got by the simulation. Also we made an application as a decision support tool for hospital decision-makers to provide with the needs of resources in the same cases. This work is being carried out in collaboration with the Mohammed 5 hospital center in Casablanca (Morocco).