{"title":"最小化总完工时间的三代理流车间问题","authors":"Wen-Chiung Lee, Jen-Ya Wang","doi":"10.1109/IIAI-AAI.2017.87","DOIUrl":null,"url":null,"abstract":"In the field of job scheduling, two-agent scheduling problems have been widely studied for a dozen years. Nevertheless, there are more than two agents competing for limited resources in practice. In light of the above observation, we aim to build a scheduling model for a three-agent problem. In this model, we aim to minimize the total completion time for agent 1 with the restriction that the total tardiness concerned with agent 2 cannot exceed a threshold and two maintenance activities requested by agent 3 must be conducted within two specific time windows. Due to the NP-hardness, we employ a genetic algorithm to solve this problem. Experimental results show that the genetic algorithm takes a little run time and generates near-optimal schedules.","PeriodicalId":281712,"journal":{"name":"2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"361 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Three-Agent Flow Shop Problem for Minimizing the Total Completion Time\",\"authors\":\"Wen-Chiung Lee, Jen-Ya Wang\",\"doi\":\"10.1109/IIAI-AAI.2017.87\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the field of job scheduling, two-agent scheduling problems have been widely studied for a dozen years. Nevertheless, there are more than two agents competing for limited resources in practice. In light of the above observation, we aim to build a scheduling model for a three-agent problem. In this model, we aim to minimize the total completion time for agent 1 with the restriction that the total tardiness concerned with agent 2 cannot exceed a threshold and two maintenance activities requested by agent 3 must be conducted within two specific time windows. Due to the NP-hardness, we employ a genetic algorithm to solve this problem. Experimental results show that the genetic algorithm takes a little run time and generates near-optimal schedules.\",\"PeriodicalId\":281712,\"journal\":{\"name\":\"2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)\",\"volume\":\"361 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IIAI-AAI.2017.87\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IIAI-AAI.2017.87","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Three-Agent Flow Shop Problem for Minimizing the Total Completion Time
In the field of job scheduling, two-agent scheduling problems have been widely studied for a dozen years. Nevertheless, there are more than two agents competing for limited resources in practice. In light of the above observation, we aim to build a scheduling model for a three-agent problem. In this model, we aim to minimize the total completion time for agent 1 with the restriction that the total tardiness concerned with agent 2 cannot exceed a threshold and two maintenance activities requested by agent 3 must be conducted within two specific time windows. Due to the NP-hardness, we employ a genetic algorithm to solve this problem. Experimental results show that the genetic algorithm takes a little run time and generates near-optimal schedules.