A Q-learning-Based Meta-heuristic Algorithm for Open Shop Scheduling Considering Group and Transportation Activities

Open shop scheduling problems have received considerable attention in recent years, and they are extensively implemented in manufacturing and service systems. Group and transportation activities play pivotal roles in reality, but they attract less concern. This article studies an open shop scheduling problem considering group and transportation operations via addressing group allocation of jobs, job sequence in groups and group processing route on machines. Firstly, a mixed integer programming model is developed to define the problem for minimizing makespan. Secondly, a Q-learning-based meta-heuristic algorithm is devised by integrating a Q-learning method and three meta-heuristic methods, i.e., genetic algorithm, variable neighborhood search approach and Jaya algorithm. The Q-learning method is employed to choose an appropriate meta-heuristic as the search method during iterations. Finally, through comparison experiments with two state-of-the-art meta-heuristics, the competitiveness of the proposed model and method is identified in solving the concerned problems.