Distributed assembly scheduling problem with three stages and maintenance: a new artificial bee colony

In this study, distributed assembly scheduling problem (DASP) with three stages named fabrication, transportation and assembly, and maintenance is considered. 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. 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.