CONVJSSP: Convolutional Learning for Job-Shop Scheduling Problems

The Job-Shop Scheduling Problem (JSSP) is a well-known optimization problem with plenty of existing solutions. Although remarkable progress has been made in addressing the problem, most of the solutions require input from human experts. Deep Learning techniques, on the other hand, have proven successful in acquiring knowledge from data without using step-by-step instructions from humans. In this work, we propose a novel solution, called ConvJSSP, by applying Deep Learning to speed up the solving process of JSSPs and to reduce the need for human involvement. In ConvJSSP, we train a Convolutional Neural Network model for predicting the optimal makespan of JSSPs, and use the predicted makespan to accelerate the JSSP solving schema. Through the experiments, we compare several JSSP solving methods based on ConvJSSP approach with a state-of-the-art solution as a baseline, and show that ConvJSSP speeds up the problem solving up to 9% compared to the baseline method.