ABSTRACT

This paper studies the production scheduling problem in an assembly manufacturing system with uncertain processing time and random machine breakdown. The objectives of minimizing makespan and the performance deviation of the actual schedule from the baseline schedule are simultaneously considered. Specifically, a boosting radial basis function network constructed using the data generated by Monte Carlo method, is used as the surrogate model to approximate the performance deviation. After that, a modified master-apprentice evolutionary algorithm (MAE) is developed for robust scheduling. In the design of MAE, we employ an extended adjacency matrix of subassemblies to cope with the sequential constraints of operations in AJSSP. Based on this, effective neighbourhood structures and distance metric of solutions are designed for tabu search and path relinking operators to generate feasible schedules. To evaluate the effectiveness of the proposed method, a series of computational experiments are conducted. The results indicate that, compared with several commonly used algorithms, the suggested method shows good performance in dealing with AJSSP under uncertainty.

摘要

研究了加工时间不确定、机器故障随机的装配制造系统中的生产调度问题。同时考虑最小化完工时间和实际进度与基线进度的性能偏差的目标。具体来说，使用蒙特卡洛方法生成的数据构建的增强径向基函数网络作为代理模型来逼近性能偏差。之后，改进的师徒进化算法（MAE）被开发用于鲁棒调度。在 MAE 的设计中，我们采用扩展的子组件邻接矩阵来应对 AJSSP 中操作的顺序约束。基于此，有效的邻域结构和解决方案的距离度量是为禁忌搜索和路径重新链接算子设计的，以生成可行的时间表。为了评估所提出方法的有效性，进行了一系列计算实验。结果表明，与几种常用算法相比，所提出的方法在处理不确定性下的AJSSP方面表现出良好的性能。