Ensemble Artificial Bee Colony Algorithm and Q-Learning for Multi-Objective Distributed Heterogeneous Flowshop Scheduling Problems with Sequence-Dependent Setup Time

As the global economy develops and people's awareness of environmental protection increases, the efficient scheduling of production lines in workshops has received more and more attention. However, there is very little research focusing on distributed scheduling for heterogeneous factories. This study addresses a multi-objective distributed heterogeneous permutation flow shop scheduling problem with sequence-dependent setup times (DHPFSP-SDST). The objective is to optimize the trade-off between the maximum completion time (Makespan) and total energy consumption. First, to describe the concerned problems, we establish a mathematical model. Second, we use the artificial bee colony (ABC) algorithm to optimize the two objectives, incorporating five local search strategies tailored to the problem characteristics to enhance the algorithm's performance. Third, to improve the convergence speed of the algorithm, a Q-learning based strategy is designed to select the appropriated local search operator during iterations. Finally, based on experiments conducted on 72 instances, statistical analysis and discussions show that the Q-learning-based ABC algorithm can effectively solve the problems better than its peers.