A Reinforcement Learning Environment For Job-Shop Scheduling

TL;DR

This paper introduces a new Deep Reinforcement Learning environment for job-shop scheduling, featuring a compact state representation and a reward function, achieving near state-of-the-art results on benchmark instances.

Contribution

The paper presents a novel DRL environment with a meaningful state representation and reward function tailored for job-shop scheduling, improving performance over existing DRL methods.

Findings

Significantly outperforms existing DRL methods on benchmark instances

Achieves results close to state-of-the-art combinatorial optimization approaches

Provides an efficient environment for applying DRL to job-shop scheduling

Abstract

Scheduling is a fundamental task occurring in various automated systems applications, e.g., optimal schedules for machines on a job shop allow for a reduction of production costs and waste. Nevertheless, finding such schedules is often intractable and cannot be achieved by Combinatorial Optimization Problem (COP) methods within a given time limit. Recent advances of Deep Reinforcement Learning (DRL) in learning complex behavior enable new COP application possibilities. This paper presents an efficient DRL environment for Job-Shop Scheduling -- an important problem in the field. Furthermore, we design a meaningful and compact state representation as well as a novel, simple dense reward function, closely related to the sparse make-span minimization criteria used by COP methods. We demonstrate that our approach significantly outperforms existing DRL methods on classic benchmark instances, coming close to state-of-the-art COP approaches.