arXiv:2308.12794
공식 코드 · Papers with Code
Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods
원문 초록
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 (FJSP-SDST), 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.
함께 살펴볼 논문
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A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints
Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning Environment
Unraveling the Rainbow: can value-based methods schedule?
REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models
ALAS: A Stateful Multi-LLM Agent Framework for Disruption-Aware Planning
Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem
토론
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