Research on multi-objective Green Permutation Flow Shop Scheduling Problem Method based on Deep Reinforcement Learning

Abstract This paper proposes an effective algorithm framework based on deep reinforcement learning (DRL) to solve the multi-objective permutation flow shop scheduling problem (MOPFSP) with optimization objectives of maximum completion time and energy consumption, named DRL-MOPFSP. Firstly, the PFSP is modeled as a pointer network using the DRL-PFSP method and trained using Actor-Critic reinforcement learning to minimize the makespan. Subsequently, a neighborhood search method based on critical path is employed to further enhance the quality of solutions obtained by the DRL-PFSP algorithm. Additionally, an energy-saving strategy based on job setback is introduced to optimize the energy consumption objective. Finally, simulation and comparative experiments with classical multi-objective algorithms are conducted on 24 different-scale instances. The results demonstrate that the proposed DRL-MOPFSP algorithm exhibits fast solution speed, scalability without size limitations, and strong generalization ability.