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Lei Deming
Wu Zhiming
Institute of Automation,
Shanghai Jiaotong University,
Shanghai 200030, China |
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EFFICIENT MULTI-OBJECTIVE EVOLU-
TIONARY ALGORITHM FOR JOB
SHOP SCHEDULING*
Abstract:
A new representation method is first presented based on priority rules. According to this method, each entry in the chromosome indicates that in the procedure of the Giffler and Thompson (GT) algorithm, the conflict occurring in the corresponding machine is resolved by the corresponding priority rule. Then crowding-measure multi-objective evolutionary algorithm (CMOEA) is designed, in which both archive maintenance and fitness assignment use crowding measure. Finally the comparisons between CMOEA and SPEA in solving 15 scheduling problems demonstrate that CMOEA is suitable to job shop scheduling.
Key words:
Job shop Crowding measure Archive maintenance Fitness assignment Multi-objective evolutionary algorithm
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