4 papers
When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling
Andrea Mencaroni, Robbert Reijnen, Yingqian Zhang +1
Deep reinforcement learning (DRL) has recently emerged as a promising tool for Dynamic Algorithm Configuration (DAC), enabling evolutionary algorithms to adapt their parameters onl…
Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization
Robbert Reijnen, Yaoxin Wu, Zaharah Bukhsh +1
Deep reinforcement learning (DRL) has been widely used for dynamic algorithm configuration, particularly in evolutionary computation, which benefits from the adaptive update of par…
Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods
Robbert Reijnen, Igor G. Smit, Hongxiang Zhang +3
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 communiti…
Graph Neural Networks for Job Shop Scheduling Problems: A Survey
Igor G. Smit, Jianan Zhou, Robbert Reijnen +6
Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial optimization problems. Recent years have witnessed a rapid increase in the applicat…