An Adaptive Job Shop Scheduler Using Multilevel Convolutional Neural Network and Iterative Local Search

Abstract

This manuscript proposed an effective hybrid method based on multi-level convolutional neural network (ML-CNN) and iterative local search (ILS) to solve job shop scheduling problems (JSSP) with small-scale training samples and less time. In the proposed method, ML-CNN is proposed to find the global path of JSSP instance from the genetic algorithm (GA); ML-CNN learned global path is fed into ILS to search for the best local path, it is the key to attach excellent adaptivity. In order to find the global path from optimal solutions, the proposed ML-CNN treated the JSSP as some sub-classification tasks first. Each subclass corresponding to one sub-operation prioritizes a particular machine according to the production environment. Significantly, the proposed ML-CNN designed two-level inputs to represent production statutes. The detailed-level input channel records fourteen detailed statutes, while the system-level input channel records four systematic statutes. Two-level inputs are fed into one dimensional (1-D) CNN to extract rich hidden features to predict the priority of each suboperation using a support vector machine (SVM) classifier. At last, the global path (priority sequences) could be obtained, which is encoded as the input of ILS to find the best local path. The authors trained and tested the proposed method on 82 public JSSP instances. The results indicated that the proposed method could obtain optimal solutions for small-scale instances and outperform others regarding makespan and computation time for large-scale JSSP instances.