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针对工业4.0智能制造模式下产品客户化、个性化定制带来的多品种小批量柔性作业车间调度新需求,提出一种改进的工序基因与机器基因相结合的遗传算法,以最大完工时间、生产加工成本、生产负载平衡以及能源消耗量为优化目标,通过两级染色体交叉变异操作实现种群遗传进化解决此调度问题。经过与现有相关算法对比分析,验证了该算法的可用性与有效性。
Aiming at the new demand of small batch flexible job shop scheduling in multi-varieties and small batch jobs under the mode of industrial 4.0 intelligent manufacturing, this paper proposes an improved genetic algorithm combining process gene and machine gene with maximum completion time to produce Processing cost, production load balance and energy consumption as optimization objectives, population genetic evolution through two-level cross-mutation operation to solve this scheduling problem. After comparing with the existing algorithms, the availability and validity of the algorithm are verified.