5 papers
Fair Online Resource Allocation
Christopher En, Yuri Faenza, Andrea Lodi +1
We study the problem of fair online resource allocation, motivated by applications such as refugee resettlement and airline scheduling, where agents arrive sequentially and must be…
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…
Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming
Lara Scavuzzo, Karen Aardal, Andrea Lodi +1
Mixed Integer Linear Programming (MILP) is a pillar of mathematical optimization that offers a powerful modeling language for a wide range of applications. During the past decades,…
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…
Revisiting local branching with a machine learning lens
Defeng Liu, Matteo Fischetti, Andrea Lodi
Finding high-quality solutions to mixed-integer linear programming problems (MILPs) is of great importance for many practical applications. In this respect, the refinement heuristi…