Document Type

Article

Publication Title

arXiv

Abstract

This paper introduces a Reinforcement Learning approach to better generalize heuristic dispatching rules on the Job-shop Scheduling Problem (JSP). Current models on the JSP do not focus on generalization, although, as we show in this work, this is key to learning better heuristics on the problem. A well-known technique to improve generalization is to learn on increasingly complex instances using Curriculum Learning (CL). However, as many works in the literature indicate, this technique might suffer from catastrophic forgetting when transferring the learned skills between different problem sizes. To address this issue, we introduce a novel Adversarial Curriculum Learning (ACL) strategy, which dynamically adjusts the difficulty level during the learning process to revisit the worst-performing instances. This work also presents a deep learning model to solve the JSP, which is equivariant w.r.t. the job definition and size-agnostic. Conducted experiments on Taillard’s and Demirkol’s instances show that the presented approach significantly improves the current state-of-the-art models on the JSP. It reduces the average optimality gap from 19.35% to 10.46% on Taillard’s instances and from 38.43% to 18.85% on Demirkol’s instances. Our implementation is available online 2 © 2022, CC BY-NC-SA.

DOI

10.48550/arXiv.2206.04423

Publication Date

6-9-2022

Keywords

Deep learning, Job shop scheduling, Learning systems, Reinforcement learning, Catastrophic forgetting, Current modeling, Dispatching rules, Generalisation, Job shop scheduling problems, Job-Shop scheduling, Learn+, Learning strategy, Problem size, Reinforcement learning approach, Curricula, Artificial Intelligence (cs.AI), Machine Learning (cs.LG)

Comments

Preprint: arXiv

Archived with thanks to arXiv

Preprint License: CC by NC-SA 4.0

Uploaded 13 July 2022

Download

Share

COinS