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论文类型：会议论文

发表时间：2016-10-29

收录刊物：EI

摘要：Flexible job shop scheduling, the core problem in manufacturing system aims to achieve flexibility and efficiency of production by efficient resource allocation under the constraint conditions. Flexible scheduling under uncertainty can not only increase flexibility but also reduce the impacts of uncertainty factors. So it can better meet production demands in practical applications, hence attracting more attentions. The existing algorithms aim to find the most compact structure on the left in the Gantt chart, however, this kind of methods have difficult in solving the stochastic scheduling problems. In this paper, we proposed coevolution hybrid evolution algorithm (ChEA), which chooses particle swarm optimization (PSO) as basic algorithm, then to use the set-based grouping methods to divide the 'overall-population' into several sub-populations, evaluated by the coevolution mechanism. Meanwhile, to self-adjust the parameters in the optimization process in order to satisfy the dynamic process. Through experiments, we get the conclusion that ChEA can get better solution of stochastic flexible job shop scheduling problems than original algorithms and improve the robustness of solution.