Abstract: For the Green Distributed Heterogeneous Flexible Job Shop Scheduling Problem (GDHFJSP),a Cooperative Evolution algorithm based on Dueling-DQN (Dueling-DQNCE) is proposed. The objective is to minimize the maximum completion time and total energy consumption,selecting Pareto frontier solutions to achieve high-quality solutions. Initially,two initialization methods are introduced to effectively enhance the quality of the initial solution population.Subsequently,an activity scheduling scheme is utilized in the decoding stage to comprehensively explore the solution space and obtain high-quality solutions. Addressing the multi-objective problem,a rapid comparison method is proposed to efficiently obtain Pareto frontier solutions. Furthermore,ten knowledge-driven neighborhood search strategies are introduced,and Dueling-DQN is employed to intelligently learn and select appropriate local search strategies for each solution,accelerating population convergence. To validate the effectiveness of Dueling-DQNCE,it is compared with the state-of-the-art DQCE algorithm in 20 instances. The computational results demonstrate that Dueling-DQNCE outperforms Co-Evolution with Deep-Q-network (DQCE) in terms of computational resources and solution quality,confirming its effectiveness and superiority.

Key words: green heterogeneous distributed job shop scheduling, collaborative evolution, Pareto frontier solutions, Dueling-DQN