Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem

TL;DR

This paper presents a hybrid approach combining constraint programming and deep learning to improve solutions for the flexible job-shop scheduling problem, outperforming existing deep reinforcement learning methods.

Contribution

It introduces a novel method integrating CP with DL, training models on optimal solutions and combining both techniques for enhanced problem-solving performance.

Findings

Outperforms five state-of-the-art DRL approaches on benchmark FJSSP instances.

Demonstrates superior solution quality compared to a standard CP solver.

Shows promising preliminary results on applying the hybrid method to the traveling salesman problem.

Abstract

Recent advancements in the flexible job-shop scheduling problem (FJSSP) are primarily based on deep reinforcement learning (DRL) due to its ability to generate high-quality, real-time solutions. However, DRL approaches often fail to fully harness the strengths of existing techniques such as exact methods or constraint programming (CP), which can excel at finding optimal or near-optimal solutions for smaller instances. This paper aims to integrate CP within a deep learning (DL) based methodology, leveraging the benefits of both. In this paper, we introduce a method that involves training a DL model using optimal solutions generated by CP, ensuring the model learns from high-quality data, thereby eliminating the need for the extensive exploration typical in DRL and enhancing overall performance. Further, we integrate CP into our DL framework to jointly construct solutions, utilizing DL for the initial complex stages and transitioning to CP for optimal resolution as the problem is simplified. Our hybrid approach has been extensively tested on three public FJSSP benchmarks, demonstrating superior performance over five state-of-the-art DRL approaches and a widely-used CP solver. Additionally, with the objective of exploring the application to other combinatorial optimization problems, promising preliminary results are presented on applying our hybrid approach to the traveling salesman problem, combining an exact method with a well-known DRL method.