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Found 3 papers, 2 papers with code
Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods
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).
Learning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems
The DRL policy is trained to adaptively set the values that dictate the intensity and probability of mutation for solutions during optimization.
Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning
To address this, we introduce a Deep Reinforcement Learning (DRL) based approach called DR-ALNS that selects operators, adjusts parameters, and controls the acceptance criterion throughout the search.
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