A multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling.

To improve the scheduling efficiency of flexible job shops, this paper proposes a multi-objective collaborative intelligent agent reinforcement learning algorithm based on weight distribution. First, a mathematical model for flexible job shop scheduling optimization is established, with the makespan and total energy consumption of the shop as optimization objectives, and a disjunctive-graph is introduced to represent state features. Second, two intelligent agents are designed to address the simultaneous decision making problems of jobs and machines. Encoder-decoder components are implemented to facilitate state recognition and action output by the agents. Reward parameters are computed based on temporal differences at various moments, constructing a multi-objective Markov decision-process training model. Using hypervolume, set coverage and inverted generational distance as evaluation metrics, the algorithm is compared with those proposed in other studies on standard instances. The results demonstrate that the proposed method significantly outperforms other algorithms in solving the flexible job shop scheduling problem. Finally, a real-world case study further validates the effectiveness and practicality of the proposed algorithm.