ProRL learns interpretable programmatic scheduling policies via local search and Bayesian optimization on a custom DSL, matching or exceeding deep RL and heuristic baselines on benchmarks while using few training episodes.
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The paper creates a new collection of ready-to-use FJSP instances with worker flexibility and uncertainty simulation, plus metrics and baselines, to support reproducible solver comparisons in production scheduling.
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Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework
ProRL learns interpretable programmatic scheduling policies via local search and Bayesian optimization on a custom DSL, matching or exceeding deep RL and heuristic baselines on benchmarks while using few training episodes.
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A Benchmarking Suite for Flexible Job Shop Scheduling Problems with Worker Flexibility under Uncertainty
The paper creates a new collection of ready-to-use FJSP instances with worker flexibility and uncertainty simulation, plus metrics and baselines, to support reproducible solver comparisons in production scheduling.