Q-learning driven multi-population memetic algorithm for distributed three-stage assembly hybrid flow shop scheduling with flexible preventive maintenance

Abstract

The distributed assembly flow shop scheduling (DAFS) problem has received much attention in the last decade, and a variety of metaheuristic algorithms have been developed to achieve the high-quality solution. However, there are still some limitations. On the one hand, these studies usually ignore the machine deterioration, maintenance, transportation as well as the flexibility of flow shops. On the other hand, metaheuristic algorithms are prone to fall into local optimality and are unstable in solving complex combinatorial optimization problems. Therefore, a multi-population memetic algorithm (MPMA) with Q-learning (MPMA-QL) is developed to address a distributed assembly hybrid flow shop scheduling problem with flexible preventive maintenance (DAHFSP-FPM). Specifically, a mixed integer linear programming (MILP) model targeted at the minimal makespan is first established, followed by an effective flexible maintenance strategy to simplify the model. To efficiently solve the model, MPMA is developed and Q-learning is used to achieve an adaptive individual assignment for each subpopulation to improve the performance of MPMA. Finally, two state-of-the-art metaheuristics and their Q-learning-based improvements are selected as rivals of the developed MPMA and MPMA-QL. A series of numerical studies are carried out along with a real-life case of a furniture manufacturing company, to demonstrate that MPMA-QL can provide better solutions on the studied DAHFSP-FPM..