{"title":"Heuristics for Multi-objective Service System Designing","authors":"Marek Kvet","doi":"10.1109/Informatics57926.2022.10083411","DOIUrl":null,"url":null,"abstract":"This paper brings a short overview of heuristics developed for solving the problem of multi-objective service system designing. Even though there are plenty of effective exact and approximate solving tools for different mathematical models, they are usually not able to manage more than one objective function simultaneously. Furthermore, multi-criteria optimization assumes producing a set of non-dominated solutions instead of one resulting center deployment. Since obtaining the complete Pareto front of non-dominated solutions is extremely demanding, the development of efficient heuristics is necessary from the practical point of view. In this paper, the quality of the resulting set of solutions is studied together with the dynamics of the heuristic itself. All suggested approaches are experimentally verified making use of a dataset from real world.","PeriodicalId":101488,"journal":{"name":"2022 IEEE 16th International Scientific Conference on Informatics (Informatics)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 16th International Scientific Conference on Informatics (Informatics)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Informatics57926.2022.10083411","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper brings a short overview of heuristics developed for solving the problem of multi-objective service system designing. Even though there are plenty of effective exact and approximate solving tools for different mathematical models, they are usually not able to manage more than one objective function simultaneously. Furthermore, multi-criteria optimization assumes producing a set of non-dominated solutions instead of one resulting center deployment. Since obtaining the complete Pareto front of non-dominated solutions is extremely demanding, the development of efficient heuristics is necessary from the practical point of view. In this paper, the quality of the resulting set of solutions is studied together with the dynamics of the heuristic itself. All suggested approaches are experimentally verified making use of a dataset from real world.