多目标柔性作业车间分批优化调度

Multi-objective flexible Job Shop scheduling with lot-splitting

为解决多目标柔性作业车间分批调度问题,提出了一种基于粒子群算法的多目标柔性分批调度算法。提出了一种基于"游标"的柔性批量分割方法,并采用一种批量分割与加工工序相融合的粒子编码方法,使得该算法不但可根据机床负荷将工件分割成具有柔性批量的多个子批,而且可使子批工艺路线选取及加工排序同时得到优化。算法引入了决策者的偏好信息,用于引导算法的搜索方向,使搜索结果集中于决策者感兴趣的Pareto边沿,避免了决策者在众多非劣解中做出困难选择。通过实例仿真,对算法性能进行了比较分析和评价,结果表明了算法的有效性和可行性。最后,从生产实际出发给出了算例,证明了算法的有效性和对生产实践的指导作用。

To solve the problem of multi-objective flexible Job Shop scheduling with lot-splitting,a novel multi-objective flexible size lot-splitting scheduling algorithm based on particle swarm optimization algorithm was proposed. In this algorithm,a flexible size lot-splitting approach based on ＂cursors＂ was put forward. Combined the lot-splitting with the sub-lot scheduling,a novel particle coding scheme was proposed. As a result,the algorithm could not only split lots into flexible size sub-lots according to machine workloads,but also optimize the sub-lots routing and sequencing simultaneously. The preference information of decision maker was incorporated in the algorithm and was used to guide the search direction of the algorithm. And the search results were concentrated in preferred region of the Pareto front and the difficulty in selecting a satisfying solution from numerous non-inferior solutions was eliminated. Performance of the proposed algorithm was evaluated through simulations,and the results demonstrated the feasibility and efficiency of the proposed algorithm. Finally,an example from the practical production was addressed. Experimental results could provide reference for production practice.