Automatic Strategy Selection Based on Graph Neural Network for Constraint Programming on the Shop Scheduling

Mengya Zhang, Xinyu Li, Liang Gao, Chunjiang Zhang, Yiping Gao

Abstract: Due to the complexity of production scheduling and increasing demand, various methods, including solvers, are widely applied to the job shop scheduling problem. Among these, using machine learning techniques to enhance solver quality has attracted significant attention. However, beyond the model, the characteristics of problems greatly influence solver performance. This study focuses on the classic job shop scheduling problem and explores methods to improve constraint programming model efficiency through machine learning. An automatic branching strategy selection method based on machine learning is proposed, consisting of two components: the problem features extraction and strategy selection identification. For features extraction, three feature extraction approaches are designed. In the strategy selection phase, a classification method based on the graph neural network is used to incorporate the set of three types of features, and the problem-related loss function is designed. We conducted experiments on the proposed method on 3500 training sets and 70 test sets (benchmark), and compared the experiments with the automatic selection strategy that comes with OR-Tools. The results show that the proposed method can obtain equal or better solutions on 81.42% of the instances.

External IDs:dblp:conf/cscwd/ZhangLGZG25