Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem

TL;DR

This paper introduces residual scheduling, a reinforcement learning-based heuristic that effectively solves large-scale job shop scheduling problems by focusing on relevant jobs and machines, achieving state-of-the-art results.

Contribution

It proposes a novel residual scheduling method that improves upon existing heuristics by removing irrelevant elements, enhancing performance on large and complex JSP/FJSP instances.

Findings

Achieves state-of-the-art results on benchmark JSP/FJSP problems.

Performs well on large-scale problems even when trained on smaller instances.

Reaches zero gap on 49 out of 50 large JSP instances.

Abstract

Job-shop scheduling problem (JSP) is a mathematical optimization problem widely used in industries like manufacturing, and flexible JSP (FJSP) is also a common variant. Since they are NP-hard, it is intractable to find the optimal solution for all cases within reasonable times. Thus, it becomes important to develop efficient heuristics to solve JSP/FJSP. A kind of method of solving scheduling problems is construction heuristics, which constructs scheduling solutions via heuristics. Recently, many methods for construction heuristics leverage deep reinforcement learning (DRL) with graph neural networks (GNN). In this paper, we propose a new approach, named residual scheduling, to solving JSP/FJSP. In this new approach, we remove irrelevant machines and jobs such as those finished, such that the states include the remaining (or relevant) machines and jobs only. Our experiments show that our approach reaches state-of-the-art (SOTA) among all known construction heuristics on most well-known open JSP and FJSP benchmarks. In addition, we also observe that even though our model is trained for scheduling problems of smaller sizes, our method still performs well for scheduling problems of large sizes. Interestingly in our experiments, our approach even reaches zero gap for 49 among 50 JSP instances whose job numbers are more than 150 on 20 machines.