A Deep Reinforcement Learning (DRL)-Based Transformer Method for Solving the Open Shop Scheduling Problem

📰 ArXiv cs.AI

Learn to solve the Open Shop Scheduling Problem using a Deep Reinforcement Learning (DRL)-Based Transformer Method, improving solution quality at large scales

advanced Published 15 Jun 2026

Action Steps

Implement a Transformer-based encoder-decoder architecture to learn scheduling policies
Train the model using DRL algorithms to optimize scheduling decisions
Apply the trained model to solve OSSP instances of varying sizes
Compare the performance of the DRL-based method with classical dispatching rules and metaheuristics
Evaluate the solution quality and computational efficiency of the proposed method

Who Needs to Know This

This method benefits operations research and scheduling teams, as well as machine learning engineers, by providing a novel approach to solving complex scheduling problems

Key Insight

💡 DRL-based Transformer methods can effectively solve complex scheduling problems like OSSP, offering improved solution quality and scalability

Key Takeaways

Learn to solve the Open Shop Scheduling Problem using a Deep Reinforcement Learning (DRL)-Based Transformer Method, improving solution quality at large scales

Full Article

Title: A Deep Reinforcement Learning (DRL)-Based Transformer Method for Solving the Open Shop Scheduling Problem

Abstract:
arXiv:2606.13682v1 Announce Type: new Abstract: The open shop scheduling problem (OSSP) arises in many industrial and service settings but remains computationally challenging as the number of jobs and machines increases. While exact methods quickly become intractable, classical dispatching rules and metaheuristics may require substantial tuning to maintain solution quality at large scales. This study develops a Transformer-based scheduling policy for OSSP using an encoder-decoder architecture wi

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