Decision Transformer for Enhancing Neural Local Search on the Job Shop Scheduling Problem

TL;DR

This paper introduces a decision transformer trained on search trajectories from a neural local search agent to improve solution quality for the job shop scheduling problem, especially when longer computation times are acceptable.

Contribution

It develops a novel training method for a decision transformer on search trajectories, enhancing local search strategies beyond the original neural local search agent.

Findings

Decision transformer learns more effective local search strategies.

DT outperforms NLS in solution quality when longer inference times are acceptable.

Achieves state-of-the-art ML-based results for JSSP.

Abstract

The job shop scheduling problem (JSSP) and its solution algorithms have been of enduring interest in both academia and industry for decades. In recent years, machine learning (ML) is playing an increasingly important role in advancing existing and building new heuristic solutions for the JSSP, aiming to find better solutions in shorter computation times. In this paper we build on top of a state-of-the-art deep reinforcement learning (DRL) agent, called Neural Local Search (NLS), which can efficiently and effectively control a large local neighborhood search on the JSSP. In particular, we develop a method for training the decision transformer (DT) algorithm on search trajectories taken by a trained NLS agent to further improve upon the learned decision-making sequences. Our experiments show that the DT successfully learns local search strategies that are different and, in many cases, more effective than those of the NLS agent itself. In terms of the tradeoff between solution quality and acceptable computational time needed for the search, the DT is particularly superior in application scenarios where longer computational times are acceptable. In this case, it makes up for the longer inference times required per search step, which are caused by the larger neural network architecture, through better quality decisions per step. Thereby, the DT achieves state-of-the-art results for solving the JSSP with ML-enhanced search.