{"title":"基于多智能体的知识制造自适应调度系统","authors":"Hao-Xiang Wang, Hong-Sen Yan","doi":"10.1109/ICCA.2013.6564866","DOIUrl":null,"url":null,"abstract":"To address the uncertainty of production environment in knowledgeable manufacturing system, an interoperable knowledgeable dynamic scheduling system based on multi-agent is designed, in which a knowledge representation with a series of problem characteristics for various scheduling problems is adopted and problem-based function modules are constructed by using agent technology. An adaptive scheduling mechanism based on Q-learning (known as MA-Q policy) is proposed for guiding the equipment agent to select scheduling strategy in a dynamic environment. Simulation experiments show the scheduling system is of high intelligence and interoperability, and can constantly adapt to environmental changes by self-learning.","PeriodicalId":336534,"journal":{"name":"2013 10th IEEE International Conference on Control and Automation (ICCA)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An adaptive scheduling system in knowledgeable manufacturing based on multi-agent\",\"authors\":\"Hao-Xiang Wang, Hong-Sen Yan\",\"doi\":\"10.1109/ICCA.2013.6564866\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To address the uncertainty of production environment in knowledgeable manufacturing system, an interoperable knowledgeable dynamic scheduling system based on multi-agent is designed, in which a knowledge representation with a series of problem characteristics for various scheduling problems is adopted and problem-based function modules are constructed by using agent technology. An adaptive scheduling mechanism based on Q-learning (known as MA-Q policy) is proposed for guiding the equipment agent to select scheduling strategy in a dynamic environment. Simulation experiments show the scheduling system is of high intelligence and interoperability, and can constantly adapt to environmental changes by self-learning.\",\"PeriodicalId\":336534,\"journal\":{\"name\":\"2013 10th IEEE International Conference on Control and Automation (ICCA)\",\"volume\":\"89 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 10th IEEE International Conference on Control and Automation (ICCA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCA.2013.6564866\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 10th IEEE International Conference on Control and Automation (ICCA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCA.2013.6564866","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An adaptive scheduling system in knowledgeable manufacturing based on multi-agent
To address the uncertainty of production environment in knowledgeable manufacturing system, an interoperable knowledgeable dynamic scheduling system based on multi-agent is designed, in which a knowledge representation with a series of problem characteristics for various scheduling problems is adopted and problem-based function modules are constructed by using agent technology. An adaptive scheduling mechanism based on Q-learning (known as MA-Q policy) is proposed for guiding the equipment agent to select scheduling strategy in a dynamic environment. Simulation experiments show the scheduling system is of high intelligence and interoperability, and can constantly adapt to environmental changes by self-learning.