{"title":"The genetic algorithm with two point crossover to solve the resource-constrained project scheduling problems","authors":"Hela Ouerfelli, A. Dammak","doi":"10.1109/ICMSAO.2013.6552686","DOIUrl":null,"url":null,"abstract":"In the last few decades, the resource-constrained project-scheduling problem has become the key of the success of researching project in the enterprises and has become a popular problem type in operations research. However, due to its strongly NP-hard status, the effectiveness of exact optimization procedures is restricted to relatively small instances. In this paper, we present a genetic algorithm (GA), the so called genetic algorithm with two-point crossover (GA2P), for this problem that is able to provide near-optimal heuristic solutions. A full factorial computational experiment was set up using the well-known standard instances in PSPLIB, and the results reveal that the algorithm is effective for the RCPSP.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMSAO.2013.6552686","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
In the last few decades, the resource-constrained project-scheduling problem has become the key of the success of researching project in the enterprises and has become a popular problem type in operations research. However, due to its strongly NP-hard status, the effectiveness of exact optimization procedures is restricted to relatively small instances. In this paper, we present a genetic algorithm (GA), the so called genetic algorithm with two-point crossover (GA2P), for this problem that is able to provide near-optimal heuristic solutions. A full factorial computational experiment was set up using the well-known standard instances in PSPLIB, and the results reveal that the algorithm is effective for the RCPSP.