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4142. Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods
Invited abstract in session TA-3: (Deep) Reinforcement Learning for Combinatorial Optimization 3, stream Data Science Meets Optimization.
Tuesday, 8:30-10:00Room: 1005 (building: 202)
Authors (first author is the speaker)
| 1. | Yingqian Zhang
|
| Industrial Engineering, TU Eindhoven | |
| 2. | Robbert Reijnen
|
| IE&IS, TU Eindhoven | |
| 3. | Yaoxin Wu
|
| Eindhoven University of Technology | |
| 4. | Zaharah Bukhsh
|
| Industrial Engineering, TU Eindhoven |
Abstract
We introduce an open-source GitHub repository containing comprehensive benchmarks for a wide range of machine scheduling problems, including Job Shop Scheduling (JSP), Flow Shop Scheduling
(FSP), Flexible Job Shop Scheduling (FJSP), FJSP with Assembly constraints (FAJSP), FJSP with Sequence-Dependent Setup Times (FJSPSDST), and the online FJSP (with online job arrivals). Our primary goal is to provide a centralized hub for researchers, practitioners, and
enthusiasts interested in tackling machine scheduling challenges.
Keywords
- Combinatorial Optimization
- Artificial Intelligence
- Manufacturing
Status: accepted
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