8 papers
Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions
Jesse van Remmerden, Zaharah Bukhsh, Yingqian Zhang
Online reinforcement learning (RL) approaches have demonstrated strong performance on Job Shop Scheduling (JSP) and Flexible JSP (FJSP) problems by learning scheduling policies thr…
A Rollout-Based Algorithm and Reward Function for Resource Allocation in Business Processes
Jeroen Middelhuis, Zaharah Bukhsh, Ivo Adan +1
Resource allocation plays a critical role in minimizing cycle time and improving the efficiency of business processes. Recently, Deep Reinforcement Learning (DRL) has emerged as a…
Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling
Jesse van Remmerden, Zaharah Bukhsh, Yingqian Zhang
The Job Shop Scheduling Problem (JSSP) is a complex combinatorial optimization problem. While online Reinforcement Learning (RL) has shown promise by quickly finding acceptable sol…
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…
Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization
Jesse van Remmerden, Maurice Kenter, Diederik M. Roijers +3
In this paper, we introduce Multi-Objective Deep Centralized Multi-Agent Actor-Critic (MO- DCMAC), a multi-objective reinforcement learning (MORL) method for infrastructural mainte…