19 citations · 19 across the 5 of their papers we have counts for
6 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…
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…
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…
Learning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems
Remco Coppens, Robbert Reijnen, Yingqian Zhang +2
Multi-objective evolutionary algorithms (MOEAs) are widely used to solve multi-objective optimization problems. The algorithms rely on setting appropriate parameters to find good s…
Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning
Robbert Reijnen, Yingqian Zhang, Hoong Chuin Lau +1
The Adaptive Large Neighborhood Search (ALNS) algorithm has shown considerable success in solving combinatorial optimization problems (COPs). Nonetheless, the performance of ALNS r…