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Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods
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Job shop scheduling problems address the routing and sequencing of tasks in a job shop setting. Despite significant interest from operations research and machine learning communities over the years, a comprehensive platform for testing and comparing solution methods has been notably lacking. To fill this gap, we introduce a unified implementation of job shop scheduling problems and their solution methods, addressing the long-standing need for a standardized benchmarking platform in this domain. Our platform supports classic Job Shop (JSP), Flow Shop (FSP), Flexible Job Shop (FJSP), and Assembly Job Shop (AJSP), as well as variants featuring Sequence-Dependent Setup Times (SDST), variants with online arrivals of jobs, and combinations of these problems (e.g., FJSP-SDST and FAJSP). The platfrom provides a wide range of scheduling solution methods, from heuristics, metaheuristics, and exact optimization to deep reinforcement learning. The implementation is available as an open-source GitHub repository, serving as a collaborative hub for researchers, practitioners, and those new to the field. Beyond enabling direct comparisons with existing methods on widely studied benchmark problems, this resource serves as a robust starting point for addressing constrained and complex problem variants. By establishing a comprehensive and unified foundation, this platform is designed to consolidate existing knowledge and to inspire the development of next-generation algorithms in job shop scheduling research.
Forward citations
Cited by 4 Pith papers
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A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints
An open-source RL scheduling framework extends job shop problems with transport, buffer, setup, breakdown, stochasticity, and multi-objective features; a PPO proof of concept beats SPT and MWKR heuristics on small benchmarks.
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Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization
The paper shows that a GNN-based RL controller that reads a graph of the current Pareto front can outperform static tuning and prior RL tuners on multi-objective scheduling problems.
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Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
An attention module that aggregates sampled processing-time scenarios gives learned schedulers a consistent edge on stochastic flexible job shop problems.
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Investigating the Monte-Carlo Tree Search Approach for the Job Shop Scheduling Problem
On 20 synthetic large job shop instances, the best MCTS configurations achieved lower mean weighted completion times than the authors' OR-Tools constraint programming model.
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