A Reinforcement Learning Environment For Job-Shop Scheduling

2021/04/08 by Pierre Tassel, Martin Gebser, Tassel, Pierre +3 · 5 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Optimization and Search Problems #Reinforcement Learning in Robotics #Scheduling and Optimization Algorithms #cs.AI #cs.LG #cs.NE

paper · pdf · doi:10.48550/arxiv.2104.03760

7 pages, 4 figures, 1 table

arxiv created 2021/04/08 · openalex publication_date 2021/04/08 · arxiv updated 2021/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Scheduling is a fundamental task occurring in various automated systems applications, e.g., optimal schedules for machines on a job shop allow for a reduction of production costs and waste. Nevertheless, finding such schedules is often intractable and cannot be achieved by Combinatorial Optimization Problem (COP) methods within a given time limit. Recent advances of Deep Reinforcement Learning (DRL) in learning complex behavior enable new COP application possibilities. This paper presents an efficient DRL environment for Job-Shop Scheduling -- an important problem in the field. Furthermore, we design a meaningful and compact state representation as well as a novel, simple dense reward function, closely related to the sparse make-span minimization criteria used by COP methods. We demonstrate that our approach significantly outperforms existing DRL methods on classic benchmark instances, coming close to state-of-the-art COP approaches.

Citations

• Playing Atari with Deep Reinforcement Learning
• Trust Region Policy Optimization
• Machine learning for combinatorial optimization: A methodological tour d’horizon
• Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

Cited by

• Machine Learning for Scheduling: A Paradigm Shift from Solver-Centric to Data-Centric Approaches
• Deep Learning for Unrelated-Machines Scheduling: Handling Variable Dimensions
• A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints
• Unraveling the Rainbow: can value-based methods schedule?
• Scalability of Reinforcement Learning Methods for Dispatching in Semiconductor Frontend Fabs: A Comparison of Open-Source Models with Real Industry Datasets

Related

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• Offline reinforcement learning for job-shop scheduling problems
• Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem
• Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
• Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling