{"title":"FMS的控制方法","authors":"D. Ben-Arieh, C. L. Moodie, C. Chu","doi":"10.1109/56.771","DOIUrl":null,"url":null,"abstract":"Intelligent real-time control of a flexible manufacturing system (FMS) is a complicated task. The scheduling of machines and routing of jobs are nonpolynomial problems that have to be solved fast and accurately to obtain the advantages of an FMS. Such control is done hierarchically: part routing is considered in the cell level and the equipment control strategy is reviewed in the workstation level. Two methods of controlling an FMS are described, one for each level. The one that controls the workstation equipment utilizes a decision tables in which the system states are correlated to the decision variables. The method that routes parts between the machines in the cell uses a knowledge base that stores facts about the facility and recognizes evidence to generate a decision. Both methods are presented, along with results from simulation experiments. >","PeriodicalId":370047,"journal":{"name":"IEEE J. Robotics Autom.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1988-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Control methodology for FMS\",\"authors\":\"D. Ben-Arieh, C. L. Moodie, C. Chu\",\"doi\":\"10.1109/56.771\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Intelligent real-time control of a flexible manufacturing system (FMS) is a complicated task. The scheduling of machines and routing of jobs are nonpolynomial problems that have to be solved fast and accurately to obtain the advantages of an FMS. Such control is done hierarchically: part routing is considered in the cell level and the equipment control strategy is reviewed in the workstation level. Two methods of controlling an FMS are described, one for each level. The one that controls the workstation equipment utilizes a decision tables in which the system states are correlated to the decision variables. The method that routes parts between the machines in the cell uses a knowledge base that stores facts about the facility and recognizes evidence to generate a decision. Both methods are presented, along with results from simulation experiments. >\",\"PeriodicalId\":370047,\"journal\":{\"name\":\"IEEE J. Robotics Autom.\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1988-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE J. Robotics Autom.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/56.771\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE J. Robotics Autom.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/56.771","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Intelligent real-time control of a flexible manufacturing system (FMS) is a complicated task. The scheduling of machines and routing of jobs are nonpolynomial problems that have to be solved fast and accurately to obtain the advantages of an FMS. Such control is done hierarchically: part routing is considered in the cell level and the equipment control strategy is reviewed in the workstation level. Two methods of controlling an FMS are described, one for each level. The one that controls the workstation equipment utilizes a decision tables in which the system states are correlated to the decision variables. The method that routes parts between the machines in the cell uses a knowledge base that stores facts about the facility and recognizes evidence to generate a decision. Both methods are presented, along with results from simulation experiments. >