A Q-Learning-Based Artificial Bee Colony Algorithm for Distributed Three-Stage Assembly Scheduling with Factory Eligibility and Setup Times
Jing Wang, Deming Lei, Mingbo Li
Abstract
The assembly scheduling problem (ASP) and distributed assembly scheduling problem (DASP) have attracted much attention in recent years; however, the transportation stage is often neglected in previous works. Factory eligibility means that some products cannot be manufactured in all factories. Although it extensively exists in many real-life manufacturing processes, it is hardly considered. In this study, a distributed three-stage ASP with a DPm→1 layout, factory eligibility and setup times is studied, and a Q-learning-based artificial bee colony algorithm (QABC) is proposed to minimize total tardiness. To obtain high quality solutions, a Q-learning algorithm is implemented by using eight states based on population quality evaluation, eight actions defined by global search and neighborhood search, a new reward and an adaptive ε−greedy selection and applied to dynamically select the search operator; two employed bee swarms are obtained by population division, and an employed bee phase with an adaptive migration between them is added; a new scout phase based on a modified restart strategy is also presented. Extensive experiments are conducted. The computational results demonstrate that the new strategies of QABC are effective, and QABC is a competitive algorithm for the considered problem.