An adaptive artificial bee colony with reinforcement learning for distributed three-stage assembly scheduling with maintenance

Abstract

Distributed three-stage assembly scheduling problem extensively exists in the real-life assembly production process and is seldom considered. The integration of reinforcement learning with meta-heuristic can effectively improve the performance of meta-heuristic and effectively solve the problem; however, the integration is seldom used to cope with the problem. In this study, distributed three-stage assembly scheduling problem with DPm→1 layout and maintenance at three stages is considered and a mathematical model is provided. A new artificial bee colony with Q-learning (QABC) is proposed to minimize maximum tardiness. An effective Q-learning algorithm is implemented to dynamically select search operator, which consists of 12 states based on population quality evaluation, 8 actions defined by global search and neighborhood search, a new reward and an effective action selection. Two employed bee swarms are formed, an adaptive communication and an adaptive competition process between them are adopted to intensify exploration ability and improve search efficiency. QABC and its four comparative algorithms are tested on 80 instances. The computational results demonstrate that the new strategies of QABC really improve its search performance and QABC is a competitive algorithm for the considered problem.