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dc.contributor.authorLu, Po-Hsiangen_US
dc.contributor.authorWu, Muh-Cherngen_US
dc.contributor.authorTan, Haoen_US
dc.contributor.authorPeng, Yong-Hanen_US
dc.contributor.authorChen, Chen-Fuen_US
dc.date.accessioned2018-08-21T05:53:08Z-
dc.date.available2018-08-21T05:53:08Z-
dc.date.issued2018-01-01en_US
dc.identifier.issn0956-5515en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s10845-015-1083-zen_US
dc.identifier.urihttp://hdl.handle.net/11536/144317-
dc.description.abstractThis paper proposes a genetic algorithm for solving distributed and flexible job-shop scheduling (DFJS) problems. A DFJS problem involves three scheduling decisions: (1) job-to-cell assignment, (2) operation-sequencing, and (3) operation-to-machine assignment. Therefore, solving a DFJS problem is essentially a 3-dimensional solution space search problem; each dimension represents a type of decision. The algorithm is developed by proposing a new and concise chromosome representation , which models a 3-dimensional scheduling solution by a 1-dimensional scheme (i.e., a sequence of all jobs to be scheduled). That is, the chromosome space is 1-dimensional (1D) and the solution space is 3-dimensional (3D). In , we develop a 1D-to-3D decoding method to convert a 1D chromosome into a 3D solution. In addition, given a 3D solution, we use a refinement method to improve the scheduling performance and subsequently use a 3D-to-1D encoding method to convert the refined 3D solution into a 1D chromosome. The 1D-to-3D decoding method is designed to obtain a "good" 3D solution which tends to be load-balanced. In contrast, the refinement and 3D-to-1D encoding methods of a 3D solution provides a novel way (rather than by genetic operators) to generate new chromosomes, which are herein called shadow chromosomes. Numerical experiments indicate that outperforms the IGA developed by De Giovanni and Pezzella (Eur J Oper Res 200:395-408, 2010), which is the up-to-date best-performing genetic algorithm in solving DFJS problems.en_US
dc.language.isoen_USen_US
dc.subjectGenetic algorithmen_US
dc.subjectDistributed flexible job-shopen_US
dc.subjectChromosome representationen_US
dc.subjectChromosome spaceen_US
dc.subjectSolution spaceen_US
dc.subjectShadow chromosomesen_US
dc.titleA genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problemsen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s10845-015-1083-zen_US
dc.identifier.journalJOURNAL OF INTELLIGENT MANUFACTURINGen_US
dc.citation.volume29en_US
dc.citation.spage19en_US
dc.citation.epage34en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000419167400002en_US
Appears in Collections:Articles