# Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods

arXiv: https://arxiv.org/abs/2308.12794 · HTML: https://mlsanity.com/paper/2308.12794/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 24 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12367/) (2020)
- [A MILP model for an extended version of the Flexible Job Shop Problem](https://mlsanity.com/paper/1401.3654/) (2014)
- [schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments](https://mlsanity.com/paper/2301.04182/) (2023)

## Built on by (most cited first, 12 of 12)

- [Large Language Models as End-to-end Combinatorial Optimization Solvers](https://mlsanity.com/paper/2509.16865/) (2025)
- [Graph Neural Networks for Job Shop Scheduling Problems: A Survey](https://mlsanity.com/paper/2406.14096/) (2024)
- [Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling](https://mlsanity.com/paper/2409.10589/) (2024)
- [A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints](https://mlsanity.com/paper/2506.13566/) (2025)
- [Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems](https://mlsanity.com/paper/2412.14052/) (2024)
- [Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization](https://mlsanity.com/paper/2505.16471/) (2025)
- [Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions](https://mlsanity.com/paper/2509.10303/) (2025)
- [Algorithms for dynamic scheduling in manufacturing, towards digital factories Improving Deadline Feasibility and Responsiveness via Temporal Networks](https://mlsanity.com/paper/2510.16047/) (2025)
- [Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework](https://mlsanity.com/paper/2605.18454/) (2026)
- [A Sliding-Window-Based Reinforcement Learning for Dynamic Assembly Flow Shop Scheduling with Multi-Product Delivery](https://mlsanity.com/paper/2607.02941/) (2026)
- [VLM Fine-Tuning for End-to-End Combinatorial Optimization](https://mlsanity.com/paper/2609.37175/) (2026)
- [Multi-Task Evolution for Zero-Shot Cross-Problem Generalization using LLMs](https://mlsanity.com/paper/2610.03316/) (2026)

## Abstract (arXiv)

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.