Learning-Based Approaches for Job Shop Scheduling Problems: A Review

TL;DR

This paper reviews learning-based methods for Job Shop Scheduling, analyzing their effectiveness and limitations compared to traditional approaches, and suggests future research directions.

Contribution

It provides a comprehensive summary and evaluation of machine learning approaches for JSS, highlighting their benefits, limitations, and potential future developments.

Findings

Learning-based methods show promise but are less mature than traditional techniques.

Machine learning approaches can improve scheduling efficiency and adaptability.

Future research should focus on hybrid models and real-world applications.

Abstract

Job Shop Scheduling (JSS) is one of the most studied combinatorial optimization problems. It involves scheduling a set of jobs with predefined processing constraints on a set of machines to achieve a desired objective, such as minimizing makespan, tardiness, or flowtime. Since it introduction, JSS has become an attractive research area. Many approaches have been successfully used to address this problem, including exact methods, heuristics, and meta-heuristics. Furthermore, various learning-based approaches have been proposed to solve the JSS problem. However, these approaches are still limited when compared to the more established methods. This paper summarizes and evaluates the most important works in the literature on machine learning approaches for the JSSP. We present models, analyze their benefits and limitations, and propose future research directions.