Abstract: In the synergic production scheduling optimization problem of distributed multi-plants, it is necessary to consider the two stages of job allocation between factories and job scheduling in factories at the same time. This paper first establishes a distributed multi-plant scheduling model with total cost and advance/delay as the optimization objectives, and then proposes a nested optimization algorithm framework integrating ID3 decision tree with Gauss particle swarm optimization. In this framework, independent scheduling optimization within the factory is nested in the process of inter factory allocation optimization, and elite retention strategy is introduced to improve the algorithm optimization. In addition, ID3 decision tree technology is integrated into the process of outer layer optimization particle generation to reduce the randomness of outer layer optimization. Simulation results show that the algorithm has advantages in optimization, convergence and CPU time.

Key words: Distributed multi-plants, ID3 decision tree, Multi-objective particle swarm optimization, Elite retention strategy