Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modeling

TL;DR

This paper presents a novel sequence modeling architecture using Mamba, a linear-complexity state-space model, to efficiently solve the Flexible Job Shop Scheduling Problem, outperforming existing methods in speed and accuracy.

Contribution

Introduces a Mamba-based architecture with dual encoding and cross-attention decoding for improved FJSP solving efficiency and performance.

Findings

Faster solving speed compared to state-of-the-art methods

Outperforms existing learning-based approaches on benchmarks

Efficient modeling of operation and machine dependencies

Abstract

The Flexible Job Shop Problem (FJSP) is a well-studied combinatorial optimization problem with extensive applications for manufacturing and production scheduling. It involves assigning jobs to various machines to optimize criteria, such as minimizing total completion time. Current learning-based methods in this domain often rely on localized feature extraction models, limiting their capacity to capture overarching dependencies spanning operations and machines. This paper introduces an innovative architecture that harnesses Mamba, a state-space model with linear computational complexity, to facilitate comprehensive sequence modeling tailored for FJSP. In contrast to prevalent graph-attention-based frameworks that are computationally intensive for FJSP, we show our model is more efficient. Specifically, the proposed model possesses an encoder and a decoder. The encoder incorporates a dual Mamba block to extract operation and machine features separately. Additionally, we introduce an efficient cross-attention decoder to learn interactive embeddings of operations and machines. Our experimental results demonstrate that our method achieves faster solving speed and surpasses the performance of state-of-the-art learning-based methods for FJSP across various benchmarks.