基于多目标粒子群算法的柔性作业车间调度优化方法

摘要

针对柔性作业车间的多目标调度问题,构建了以制造工期、加工成本及提前/拖期惩罚值为目标函数的柔性作业车间调度模型,提出基于密集距离排序的自适应多目标粒子群算法.采用精英策略保留进化过程中的优势个体,基于个体密集距离降序排列进行外部种群的缩减和全局最优值的更新,并引入小概率的变异机制以增强解的多样性和算法的全局寻优能力.最后,将该方法应用于某机械公司的柔性作业车间多目标调度中,仿真结果证明了该方法的有效性和适应性.%To solve flexible job-shop multiobjective scheduling problem, the optimization model was set up. Considering of the makespan, manufacturing cost and earliness/tardiness penalties, a crowding distance sorting based on multiobjective particle swarm optimization algorithm was proposed. With the elitism strategy, dominant individuals were preserved in evolution process. The shrink of the external population and update of the global best were achieved by the individuals' crowding distance sorting in descending order. A small ratio mutation was introduced to enhance the diversity of solutions and the global searching capacity of the algorithm. Finally, the feasibility and validity of the method was proved by the simulation results of a flexible job-shop multiobjective scheduling in a workshop.