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针对以两阶段装配作业方式的制造型企业,将加工与配送调度相结合,研究生产配送协同调度问题。由于问题的强NP难性,提出了一种基于遗传算法和反向变邻域搜索的混合智能优化算法。该混合算法融合反向学习思想,构造反向邻域结构,增大搜索范围,提高遗传变邻域搜索算法的局部搜索能力,使生产和配送之间的时间衔接更精确,实现整体最优。通过多组实例仿真将该算法与其他算法进行比较,验证算法的有效性。
Aiming at the manufacturing enterprises with two-phase assembly operation mode, the processing and delivery scheduling are combined to study the problem of production and distribution coordination scheduling. Due to the strong NP difficulty of the problem, a hybrid intelligent optimization algorithm based on genetic algorithm and reverse variable neighborhood search is proposed. The hybrid algorithm integrates the idea of reverse learning, constructs the reverse neighborhood structure, increases the search range, and improves the local search ability of the genetic variable neighborhood search algorithm, which makes the time convergence between production and delivery more accurate and achieves the overall optimal. The algorithm is compared with other algorithms by multi-instance simulation to verify the validity of the algorithm.