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《Industrial Engineering Journal》 2010-06
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An Improved Genetic Algorithm for Partial Flexible Job-Shop Scheduling

Su Chun,Wang Da-xia (School of Mechanical Engineering,Southeast University,Nanjing 211189,China)  
Partial flexible job-shop scheduling(PFJS) problem is considered and a genetic algorithm is presented for it.In the algorithm,job sequence and machine assignment are coded by job string and machine string,respectively.Thus,it is called a two-string encoding method.Based on the chromosome structure,it applies a selection strategy that uses elite reserving,roulette,and set-partition crossover operator.Meanwhile,for mutation,based on random experiment with uniform distribution,a strategy is proposed by selecting one among the reverse,circular interchange,inserting,and machine interchange operators.Further,greedy decoding procedure is adopted.A case study is presented to verify the effectiveness of the method.
【Fund】: 国家自然科学基金资助项目(70671022)
【CateGory Index】: TP18
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【Secondary Citations】
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1 Zhang Chaoyong Rao Yunqing Liu Xiangjun Li Peigen Huazhong University of Science & Technology, Wuhan,430074;An Improved Genetic Algorithm for the Job Shop Scheduling Problem[J];China Mechanical Engineering;2004-23
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