大规模柔性作业车间组批调度及求解方法研究

Study on Batch Scheduling and Solution of Large-Scale Flexible Job Shop

针对大规模柔性作业车间排程调度问题,采用基于工件组批调度方法降解问题规模,并利用自适应遗传算法优化求解.该方法首先将加工工艺类似、管径尺寸在同一范围内且毛坯材质相同的零件进行组批,从而减小问题求解规模.其次在优化过程中,采用OBX(Order-Based Crossover)交叉方法,根据最优交叉点个数与染色体长度的关系,改进自适应遗传算法,提高其优化速度及求解能力.最后经实例验证该方法可以有效地缩减工件完工时间、减少订单拖延期和寻优时间.

Aiming at the problem of large-scale flexible job-shop scheduling,the scale of the problem is degraded by the method based on the workpiece batch grouping scheduling,and the adaptive genetic algorithm is adopted to solve the problem.Firstly,the method take the same process,the size of the pipe diameter in the same range,and the same material blank parts as several batches,thereby reducing the size of the problem solving.Secondly,the OBX(Order-Based Crossover)cross method is used to improve the genetic algorithm and improve the speed and solution ability according to the relationship between the number of optimal intersections and chromosome length.Finally,an example is given to demonstrate that the method can effectively reduce the completion time of the workpiece and shorten the delay and optimization time of the order.