基于改进NSGA2算法的多目标柔性作业车间调度

Multi-objective Flexible Job Shop Scheduling based on Improved NSGA2 Algorithm

柔性作业车间调度问题(FJSP)是经典作业车间调度问题的重要扩展,其中每个操作可以在多台机器上处理,反之亦然。结合实际生产过程中加工时间、机器负载、运行成本等情况,建立了多目标调度模型。针对NSGA2算法收敛性不足的缺陷,引入免疫平衡原理改进NSGA2算法的选择策略和精英保留策略,成功避免了局部收敛问题,提高了算法的优化性能。通过与启发式规则以及多种智能算法进行比对仿真实验,改进的NASA2算法能获得更好的解。用改进的NAGA2算法求解实例,不仅有效地克服多目标间数量级和量纲的障碍,而且得到了满意的pareto解集,进一步验证了该算法和模型的可行性。

Flexible Job Shop Scheduling Problem （FJSP） is an important extension of the classic job shop schedulingproblem where each operation can be handled on multiple machines and vice versa. Combined with the actual productionprocess of processing time, machine load, operating costs and other conditions, a multi-objective scheduling model isestablished. Aiming at the defect of insufficient convergence of NSGA2 algorithm, the immune balance principle isintroduced to improve the selection strategy and elite retention strategy of NSGA2 algorithm, avoiding the localconvergence problem and improving the optimization performance of the algorithm. By comparing with heuristic rules andvarious intelligent algorithms, the improved NASA2 algorithm can get a better solution. Using improved NAGA2 algorithmto solve the case not only effectively overcomes the barriers in order of magnitude and dimension of the objectives, but alsoobtains a satisfactory pareto solution set, further verifying the feasibility of the algorithm and the model.