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《Journal of System Simulation》 2008-14
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Research on Flexible Job Shop Dynamic Scheduling Problem

WU Xiu-li (Department of Logistics Engineering, School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China)  
A multi-objective immune genetic algorithm (MOIGA) based dynamic scheduling optimization algorithm was proposed to solve the flexible job shop dynamic scheduling problem. The problem was defined in which deviation degree criterion was proposed with the delivery date criterion to reflect the difference between the pre-scheduling and the rescheduling. A MOIGA-based interactive optimization model integrating periodic and event-driven dynamic scheduling strategy was proposed. An operation-extended based encoding way was put forward and a bidirectional scheduling algorithm aiming at the earliness/tardiness (E/T) criteria optimized was proposed in the decoding step. The factors related with MOIGA's time complexity was discussed. A simulation example result shows that the optimization model can solve the problem effectively and efficiently.
【CateGory Index】: TP18
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