{"title":"新的基于奖励的运动，以改善全球发展的BCO护士名册问题","authors":"Vebby Clarissa, S. Suyanto","doi":"10.1109/ISRITI48646.2019.9034669","DOIUrl":null,"url":null,"abstract":"Nurse Rostering Problem (NRP) is a crucial problem in hospital industry with combinatorial complex problem. NRP is one of the NP-Hard problems, which means that today there is no definite algorithm that is capable of solving the problem. In this paper, a metaheuristic approach called Reward-Based Movement for Bee Colony Optimization (RBMBCO) is proposed to solve the NRP. It is evaluated using an NRP instance of 30 nurses for 4 weeks of assignment from The Second International Nurse Rostering Competition (INRC-II) dataset. The experimental results show that RBMBCO is capable of generating a better solution than the standard Globally-Evolved Bee Colony Optimization.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"332 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"New Reward-Based Movement to Improve Globally-Evolved BCO in Nurse Rostering Problem\",\"authors\":\"Vebby Clarissa, S. Suyanto\",\"doi\":\"10.1109/ISRITI48646.2019.9034669\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nurse Rostering Problem (NRP) is a crucial problem in hospital industry with combinatorial complex problem. NRP is one of the NP-Hard problems, which means that today there is no definite algorithm that is capable of solving the problem. In this paper, a metaheuristic approach called Reward-Based Movement for Bee Colony Optimization (RBMBCO) is proposed to solve the NRP. It is evaluated using an NRP instance of 30 nurses for 4 weeks of assignment from The Second International Nurse Rostering Competition (INRC-II) dataset. The experimental results show that RBMBCO is capable of generating a better solution than the standard Globally-Evolved Bee Colony Optimization.\",\"PeriodicalId\":367363,\"journal\":{\"name\":\"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)\",\"volume\":\"332 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISRITI48646.2019.9034669\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISRITI48646.2019.9034669","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
New Reward-Based Movement to Improve Globally-Evolved BCO in Nurse Rostering Problem
Nurse Rostering Problem (NRP) is a crucial problem in hospital industry with combinatorial complex problem. NRP is one of the NP-Hard problems, which means that today there is no definite algorithm that is capable of solving the problem. In this paper, a metaheuristic approach called Reward-Based Movement for Bee Colony Optimization (RBMBCO) is proposed to solve the NRP. It is evaluated using an NRP instance of 30 nurses for 4 weeks of assignment from The Second International Nurse Rostering Competition (INRC-II) dataset. The experimental results show that RBMBCO is capable of generating a better solution than the standard Globally-Evolved Bee Colony Optimization.
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