An effective coding approach for multiobjective integrated resource selection and operation sequences problem
DC Field | Value | Language |
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dc.contributor.author | Zhang, Haipeng | - |
dc.contributor.author | Gen, Mitsuo | - |
dc.contributor.author | Seo, Yoonho | - |
dc.date.accessioned | 2021-09-09T06:30:27Z | - |
dc.date.available | 2021-09-09T06:30:27Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2006-08 | - |
dc.identifier.issn | 0956-5515 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/123121 | - |
dc.description.abstract | In this paper, we consider an integrated Resource Selection and Operation Sequences (iRS/OS) problem in Intelligent Manufacturing System (IMS). Several kinds of objectives are taken into account, in which the makespan for orders should be minimized; workloads among machine tools should be balanced; the total transition times between machines in a local plant should also be minimized. To solve this multiobjective iRS/OS model, a new two vectors-based coding approach has been proposed to improve the efficiency by designing a chromosome containing two kinds of information, i.e., operation sequences and machine selection. Using such kind of chromosome, we adapt multistage operation-based Genetic Algorithm (moGA) to find the Pareto optimal solutions. Moreover a special technique called left-shift hillclimber has been used as one kind of local search to improve the efficiency of our algorithm. Finally, the experimental results of several iRS/OS problems indicate that our proposed approach can obtain best solutions. Further more comparing with previous approaches, moGA performs better for finding Pareto solutions. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | CONSTRAINTS | - |
dc.subject | PRECEDENCE | - |
dc.subject | ALGORITHM | - |
dc.subject | SYSTEM | - |
dc.title | An effective coding approach for multiobjective integrated resource selection and operation sequences problem | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Seo, Yoonho | - |
dc.identifier.doi | 10.1007/s10845-005-0012-y | - |
dc.identifier.scopusid | 2-s2.0-33747676002 | - |
dc.identifier.wosid | 000240935400002 | - |
dc.identifier.bibliographicCitation | JOURNAL OF INTELLIGENT MANUFACTURING, v.17, no.4, pp.385 - 397 | - |
dc.relation.isPartOf | JOURNAL OF INTELLIGENT MANUFACTURING | - |
dc.citation.title | JOURNAL OF INTELLIGENT MANUFACTURING | - |
dc.citation.volume | 17 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 385 | - |
dc.citation.endPage | 397 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Manufacturing | - |
dc.subject.keywordPlus | CONSTRAINTS | - |
dc.subject.keywordPlus | PRECEDENCE | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordAuthor | intelligent manufacturing system | - |
dc.subject.keywordAuthor | integrated resource selection and operation sequences | - |
dc.subject.keywordAuthor | multistage operation-based genetic algorithm | - |
dc.subject.keywordAuthor | left-shift hillclimber | - |
dc.subject.keywordAuthor | multiple criteria model | - |
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