A multiobjective memetic algorithm with particle swarm optimization and Q-learning-based local search for energy-efficient distributed heterogeneous hybrid flow-shop scheduling problem

Most existing distributed hybrid flow-shop scheduling problems (DHFSPs) assume identical shops and lack consideration of heterogeneous shops. This study focuses on energy-efficient heterogeneous DHFSP. A multi -objective memetic algorithm with particle swarm optimization and Q-learning-based local search is proposed in order to optimize both makespan and total energy consumption. Particle swarm optimization with multi-group is specifically designed as a global search strategy to improve the fast convergence performance of solutions in multi-direction of Pareto front. To improve the problem-specific knowledge search, two local search strategies are designed to further improve the quality and diversity of solutions. In addition, Q-learning is utilized to guide variable neighborhood search to better balance the exploration and exploitation of algorithms. This study investigates the effect of parameter setting and conducts extensive numerical tests. The comparative results and statistical analysis demonstrate the superior convergence and distribution performance of the proposed algorithm.