{"title":"Integration of knowledge-based and algorithmic techniques for production scheduling","authors":"P. Brandimarte, C. Greco","doi":"10.1109/CIM.1988.5392","DOIUrl":null,"url":null,"abstract":"The integration of algorithmic and heuristic methods has been investigated in different manufacturing environments. The algorithmic procedures developed for production scheduling results were limited in their ability to cope with the complexity of real-world manufacturing. The scheduling problem, seen as a constraints-satisfaction problem, can be approached with knowledge based techniques. However, algorithmic techniques are more efficient and better able to deal with aggregated data. The authors suggest that the integration of knowledge-based techniques with algorithmic ones can increase the efficiency of searching in the space of possible solutions.<<ETX>>","PeriodicalId":334994,"journal":{"name":"[Proceedings] 1988 International Conference on Computer Integrated Manufacturing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1988-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[Proceedings] 1988 International Conference on Computer Integrated Manufacturing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIM.1988.5392","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
The integration of algorithmic and heuristic methods has been investigated in different manufacturing environments. The algorithmic procedures developed for production scheduling results were limited in their ability to cope with the complexity of real-world manufacturing. The scheduling problem, seen as a constraints-satisfaction problem, can be approached with knowledge based techniques. However, algorithmic techniques are more efficient and better able to deal with aggregated data. The authors suggest that the integration of knowledge-based techniques with algorithmic ones can increase the efficiency of searching in the space of possible solutions.<>