5 papers
Deep RL Dual Sourcing Inventory Management with Supply and Capacity Risk Awareness
Defeng Liu, Ying Liu, Carson Eisenach
In this work, we study how to efficiently apply reinforcement learning (RL) for solving large-scale stochastic optimization problems by leveraging intervention models. The key of t…
Learning Large Neighborhood Search for Maritime Inventory Routing Optimization
Rui Chen, Defeng Liu, Nan Jiang +3
Maritime inventory routing optimization is an important yet challenging combinatorial optimization problem. We propose a machine learning-based local search approach for finding fe…
Dual Policy Reinforcement Learning for Real-time Rebalancing in Bike-sharing Systems
Jiaqi Liang, Defeng Liu, Sanjay Dominik Jena +2
Bike-sharing systems play a crucial role in easing traffic congestion and promoting healthier lifestyles. However, ensuring their reliability and user acceptance requires effective…
Single- vs. Dual-Policy Reinforcement Learning for Dynamic Bike Rebalancing
Jiaqi Liang, Defeng Liu, Sanjay Dominik Jena +2
Bike-sharing systems (BSS) provide a sustainable urban mobility solution, but ensuring their reliability requires effective rebalancing strategies to address stochastic demand and…
Learning chordal extensions
Defeng Liu, Andrea Lodi, Mathieu Tanneau
A highly influential ingredient of many techniques designed to exploit sparsity in numerical optimization is the so-called chordal extension of a graph representation of the optimi…