论文部分内容阅读
为提升企业快速响应单件、小批量、个性化定制等市场需求的能力,该文提出了1种面向智能制造的作业车间调度优化的改进遗传算法。在多工件加工工艺约束条件下,对工序和机器分别进行矩阵编码。设计了与编码方式相对应的选择、交叉和变异操作,并增加保留算子,保留每一代种群中的最优个体。在求得全局近似最优解后,采用插入式贪婪解码算法对染色体进行解码。可动态优化基于加工时间最短或提前/拖期惩罚代价最小的多工件作业规划和机器分配方案。仿真结果证明了算法的有效性。
In order to enhance the ability of enterprises to respond quickly to the market demand of single parts, small quantities and individual customization, an improved genetic algorithm for job shop scheduling optimization based on intelligent manufacturing is proposed. In the multi-workpiece processing technology constraints, the process and machine were matrix-coded. The selection, crossover and mutation operations corresponding to the encoding are designed and the retention operator is added to retain the best individual in each generation. After obtaining the global optimal solution, the greedy decoding algorithm is adopted to decode the chromosomes. Can be dynamically optimized for multi-part job planning and machine allocation based on minimum machining time or early / delayed penalty. Simulation results prove the effectiveness of the algorithm.