59 citations · 165 across the 16 of their papers we have counts for
5 papers · 1 filter
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…
Maintenance Strategies for Sewer Pipes with Multi-State Degradation and Deep Reinforcement Learning
Lisandro A. Jimenez-Roa, Thiago D. Simão, Zaharah Bukhsh +4
Large-scale infrastructure systems are crucial for societal welfare, and their effective management requires strategic forecasting and intervention methods that account for various…
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…
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…
Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking
Igor G. Smit, Zaharah Bukhsh, Mykola Pechenizkiy +3
In collaborative human-robot order picking systems, human pickers and Autonomous Mobile Robots (AMRs) travel independently through a warehouse and meet at pick locations where pick…