Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modeling
arXiv:2602.21546v1 Announce Type: new 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 featur
arXiv:2602.21546v1 Announce Type: new 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.
Executive Summary
This article introduces a novel architecture for solving the Flexible Job Shop Problem (FJSP) using a state-space model called Mamba. The proposed model combines an encoder with a dual Mamba block and a cross-attention decoder to learn interactive embeddings of operations and machines. Experimental results demonstrate that the method achieves faster solving speed and surpasses state-of-the-art learning-based methods for FJSP across various benchmarks. The approach has significant implications for manufacturing and production scheduling, offering a more efficient and effective solution for optimizing criteria such as minimizing total completion time.
Key Points
- ▸ Introduction of Mamba, a state-space model with linear computational complexity, for FJSP
- ▸ Proposal of a novel architecture combining an encoder and a cross-attention decoder
- ▸ Experimental results demonstrating faster solving speed and improved performance compared to state-of-the-art methods
Merits
Efficient Sequence Modeling
The proposed model's ability to capture overarching dependencies spanning operations and machines using Mamba enables more efficient sequence modeling for FJSP.
Demerits
Limited Generalizability
The approach may be limited in its generalizability to other combinatorial optimization problems, requiring further research to adapt the model to different domains.
Expert Commentary
The introduction of Mamba and the proposed architecture represents a significant advancement in the field of FJSP, offering a more efficient and effective solution for optimizing complex manufacturing and production scheduling problems. The approach's ability to capture overarching dependencies and learn interactive embeddings of operations and machines is particularly noteworthy. However, further research is necessary to fully explore the model's potential and adapt it to other domains. The implications of this work are far-reaching, with potential applications in various industries and fields, including supply chain management, logistics, and beyond.
Recommendations
- ✓ Further research to adapt the model to other combinatorial optimization problems
- ✓ Investigation of the approach's potential applications in various industries and fields
