锻造多属性有条件相容并行组批优化

Parallel batching optimization with multi-attribute &compatible job in forging

针对锻造行业存在的多阶段、工件有条件相容、不确定加工时间、以最小化拖期和完工时间为目标优化的组批和排序问题,提出了基于工件模糊聚类的两种算法。算法1的工件优先级由工件熵值、工件松弛时间重要度、市场意志共同决定,批的优先级由批内最高优先级工件决定;算法2为随机密钥代表混合遗传算法,该算法用随机密钥进行编码和解码,采用基于规则编码的方法来优化基因序列和基于规则的交叉操作,采用小生境技术来调整个体适应度,采用精英保留策略产生下一代。数值实验结果表明,在多品种、高负荷下,混合遗传算法好于算法1,算法1好于其他启发式算法。

Motivated by forge operation in a multi-layer production line whose jobs with uncertain process time in forge phase were processed simultaneously as a batch under certain condition,the objectives of minimizing the total tardy number of jobs and the total tardiness,two job-clustering-based algorithms were presented.In the first algo-rithm,the job priority was decided by process entropy of job,slacking importance of job and market will.And the batch priority was determined by the job with the highest priority in the batch.In the second algorithm,random keys were used to represent hybrid genetic algorithm,and random keys represent was adopted to perform coding and decoding.To solve practical big-size problems in a reasonable computational time,a rule coding method was adopted to optimize the gene sequence and crossover was also operated by anther rule.Niche technology was used to adjust the individual fitness,and elitist strategy was adopted to create next generation.Numerical experimental results showed that the hybrid genetic algorithm was better than the first algorithm,and the first algorithm was better than the other heuristic algorithms.