A Two-stage Cooperative Discrete Differential Evolution With Q-Learning for Multi-Objective Energy-Efficient Distributed Blocking Flow-Shop Scheduling

In the context of green manufacturing, energy consumption issues have attracted widespread attention from all walks of life, especially in the manufacturing industry. In actual industrial production, the distributed blocking flow shop scheduling problem is a typical manufacturing production scenario, but its energy consumption problem has not been effectively solved. In this study, a two-stage cooperative discrete differential evolution with Q-learning (QTCDDE) is proposed to solve the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP) with total energy consumption (TEC) and total tardiness (TTD). An initialization strategy that considers both TEC and TTD is proposed to obtain an initial population with rich search space. In the first stage, a discrete differential evolution is designed to improve the quality of the solution. In the second stage, three types of local search operators are designed, and they are adaptively selected based on historical information and Q-learning. In addition, during the iterative process, the two stages cooperate and complement each other. Finally, each strategy of QTCDDE is effectively verified. Furthermore, QTCDDE is compared with state-of-the-art algorithms in the benchmark suite. Experimental results show that QTCDDE significantly outperforms state-of-the-art algorithms at the 95% confidence interval and effectively solves EEDBFSP.