{"title":"Distributed, knowledge-based, reactive scheduling of transportation tasks","authors":"K. Fischer, N. Kuhn, J. Muller","doi":"10.1109/CAIA.1994.323693","DOIUrl":null,"url":null,"abstract":"We demonstrate the use of DAI (distributed artificial intelligence) techniques to construct a distributed solution for a class of scheduling tasks within the transportation domain. We deal with the dynamic allocation of transportation orders to a set of resources (different shipping companies and their trucks), aiming to minimize transportation distance, time, and/or the number of resources (trucks). We provide a thorough description of the concepts and the implementation underlying our system. Important research work done in related fields such as operations research is reviewed. Finally, we discuss the results obtained by a series of experiments we carried through in order to compare the problem solving power of the DAI approach with standard operations research methods for solving (distributed) scheduling problems.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"121 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAIA.1994.323693","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
We demonstrate the use of DAI (distributed artificial intelligence) techniques to construct a distributed solution for a class of scheduling tasks within the transportation domain. We deal with the dynamic allocation of transportation orders to a set of resources (different shipping companies and their trucks), aiming to minimize transportation distance, time, and/or the number of resources (trucks). We provide a thorough description of the concepts and the implementation underlying our system. Important research work done in related fields such as operations research is reviewed. Finally, we discuss the results obtained by a series of experiments we carried through in order to compare the problem solving power of the DAI approach with standard operations research methods for solving (distributed) scheduling problems.<>
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