基于混合差分进化算法的并行机批处理调度问题研究

考虑到实际生产中产品多、批量小的特点，建立了一种带工艺约束的并行机批处理调度优化模型。为解决调度中的分批问题，提出了一种新的基于产品需求量的批量划分方案及批量染色体编码方式，采用两级差分进化算法来解决批量划分和批次调度问题；针对标准差分进化算法收敛速度慢、易出现早熟现象等问题，引入动态随机搜索和随机变异的局部搜索策略，以增强标准差分进化算法的局部搜索能力。测试算例及调度实例的仿真结果表明，该算法能有效地提高算法收敛速度，平衡其全局搜索和局部探索能力。

New hybrid differential evolution for parallel machines batch scheduling

Considering the characteristics of more pl＇oducts,small batches in practical production,a parallel machines hatch scheduling model with process constraint was established. In order to solve the problem of splitting, a new batch splitting method based on demand and new chromosome representation was put forward. A new parallel encoding was brought forward to solve both the batch splitting problem and batch scheduling problem. To the problem of low searching speed and premature convergence appeared in standard differential evolution, a new hybrid differential evolution （DE）,based on dynamic random search and Chaos optimization,was proposed to enhance local search ability of standard DE. Performance of the proposed algorithm on classic benchmark function and shop scheduling demonstrate that, the proposed algorithm can effectively improve searching speed, balancing the abilities of global search and local search.