考虑工件分批的柔性作业车间调度及求解算法

Problem and solution of flexible job shop scheduling based on job batching

针对柔性作业车间分批调度问题,建立了考虑工件分批的柔性作业车间调度模型,并提出混合遗传算法用于模型求解。首先,采用改进试探法确定划分的具体批次、柔性批量划分方法确定各个批次的实际批量;其次,采用双层编码机制对工序排序及机器选择同时进行优化,利用GLR机器选择法生成初始解;最后,混合遗传算法利用GA鲁棒性强与ABC算法对初始解依赖性不高、适应性强的特点在解空间内充分搜索较优解,并结合SA出色的局部搜索能力快速收敛到全局最优解。分析表明,改进试探法批次划分与柔性批量划分方法可明显缩短生产周期,同时也证明了所提算法的有效性和可行性。

A flexible job shop scheduling model based on job batching was established,and a hybrid genetic algorithm was proposed to solve the model.Firstly,the improved heuristic method was used to determine the specific batch,and the flexible batching method was used to determine the actual amount of each batch.Secondly,the process sequencing and machine selection were optimized simultaneously by double-layer coding mechanism,and the initial solution was generated by GLR machine selection method.Finally,the hybrid genetic algorithm took advantage of the strong robustness of GA and the low dependence on the initial solution and strong adaptability of ABC algorithm to fully search for the optimal solution in the solution space,and the excellent local search ability of SA was used to quickly converge to the global optimal solution.The analysis showed that the improved heuristic batching method and the flexible batching method could significantly shorten the production cycle,and the effectiveness and feasibility of the proposed algorithm was proved.