Summary of Neural Combinatorial Optimization For Stochastic Flexible Job Shop Scheduling Problems, by Igor G. Smit et al.
Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
by Igor G. Smit, Yaoxin Wu, Pavel Troubil, Yingqian Zhang, Wim P.M. Nuijten
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Neural combinatorial optimization (NCO) has shown promise in solving complex problems efficiently using deep learning. Traditionally, NCO has focused on deterministic job shop scheduling problems (JSPs). This paper proposes a novel attention-based scenario processing module (SPM) to extend NCO methods for stochastic JSPs. The SPM incorporates stochastic information through an attention mechanism that captures the embedding of sampled scenarios. This approach learns an effective policy under stochasticity and is trained using either the expected makespan or Value-at-Risk objective. Results demonstrate that our approach outperforms existing learning and non-learning methods for the flexible JSP problem with stochastic processing times on various instances. Our method also exhibits generalizability to different numbers of scenarios and distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to solve complex scheduling problems more efficiently. Usually, these problems are predictable, but sometimes they can be unpredictable. The authors propose a new way to make computers better at solving these unpredictable problems by looking at many possible scenarios. They use a special technique called attention-based scenario processing module (SPM) that helps the computer learn from all these scenarios and make good decisions even when things are uncertain. They tested their method on various scheduling problems and found it was better than other methods. This new approach can be used in different situations with varying levels of uncertainty. |
Keywords
» Artificial intelligence » Attention » Deep learning » Embedding » Optimization
