Solving machine overload for re-scheduling of dynamic flexible job shop by adaptive tripartite game theory-based genetic algorithm

In the production process of a flexible job shop, the dynamic events could disrupt the original production scheduling plan. The existing methods typically use rescheduling, but they only ensure the resumption of normal production without considering the machine load, which would lead to a machine overload vicious cycle. This paper studies the dynamic flexible job shop scheduling problem (DFJSP) considering machine load under the constraint of machine breakdown as a dynamic event, and proposes an adaptive tripartite game theory-based genetic algorithm (ATGA). Firstly, a population initialization strategy based on a pre-scheduling scheme is designed to obtain a better initial population. Then, in order to better balance multiple objectives, a machine selection strategy based on tripartite game is designed. Finally, for improving the search ability and convergence performance of the algorithm, the adaptive probability selection strategy of binary tournament is designed. The experimental results show that the algorithm surpasses other advanced algorithms in scheduling effectiveness. • The machine overload vicious cycle in dynamic scheduling is discovered. • A machine selection strategy is proposed to balance multiple objectives. • Adaptive probability selection strategy to enhance the search capability. • Pre-scheduling-based initialization method generates better initial solutions.