10 papers
Maintenance Optimization for Asset Networks with Unknown Degradation Parameters
Peter Verleijsdonk, Collin Drent, Stella Kapodistria +1
We consider the key practical challenge of multi-asset maintenance optimization in settings where degradation parameters are heterogeneous and unknown, and must be inferred from de…
Zero-shot Generalization in Inventory Management: Train, then Estimate and Decide
Tarkan Temizöz, Christina Imdahl, Remco Dijkman +2
Deploying deep reinforcement learning (DRL) in real-world inventory management presents challenges, including dynamic environments and uncertain problem parameters, e.g. demand and…
Same-day or next-day? Transparent time-dependent shipment pricing for e-fulfillment
Uta Mohring, Melvin Drent, Ivo Adan +1
We develop a parsimonious model of an e-commerce fulfillment center that offers time-dependent shipment options and corresponding fees to utility-maximizing customers arriving acco…
Improving After-sales Service: Deep Reinforcement Learning for Dynamic Time Slot Assignment with Commitments and Customer Preferences
Xiao Mao, Albert H. Schrotenboer, Guohua Wu +1
Problem definition: For original equipment manufacturers (OEMs), high-tech maintenance is a strategic component in after-sales services, involving close coordination between custom…
A Universal Approach to Feature Representation in Dynamic Task Assignment Problems
Riccardo Lo Bianco, Remco Dijkman, Wim Nuijten +1
Dynamic task assignment concerns the optimal assignment of resources to tasks in a business process. Recently, Deep Reinforcement Learning (DRL) has been proposed as the state of t…
GymPN: A Library for Decision-Making in Process Management Systems
Riccardo Lo Bianco, Willem van Jaarsveld, Remco Dijkman
Process management systems support key decisions about the way work is allocated in organizations. This includes decisions on which task to perform next, when to execute the task,…