{"title":"A decomposition strategy for multicriteria optimization with application to machine tool design","authors":"J. Montusiewicz, A. Osyczka","doi":"10.1016/0167-188X(90)90102-N","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper a novel decomposition strategy for multicriteria optimization of large-scale systems is presented. The strategy has a heuristic character and contains four stages. The first stage is to optimize the overall system with respect to basic decision variables. The second stage is to optimize all subsystems which are considered separately. Interaction between subsystems and between the first and second stages are treated as coordination variables. The third stage is to optimize the overall system with respect to coordination variables. The final stage is to select the Pareto optimal set of solutions and to make final decision. An application of the strategy for designing machine tool spindle systems with hydrostatic bearings is presented.</p></div>","PeriodicalId":100476,"journal":{"name":"Engineering Costs and Production Economics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1990-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0167-188X(90)90102-N","citationCount":"19","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Costs and Production Economics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/0167188X9090102N","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19
Abstract
In this paper a novel decomposition strategy for multicriteria optimization of large-scale systems is presented. The strategy has a heuristic character and contains four stages. The first stage is to optimize the overall system with respect to basic decision variables. The second stage is to optimize all subsystems which are considered separately. Interaction between subsystems and between the first and second stages are treated as coordination variables. The third stage is to optimize the overall system with respect to coordination variables. The final stage is to select the Pareto optimal set of solutions and to make final decision. An application of the strategy for designing machine tool spindle systems with hydrostatic bearings is presented.
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