2 citations · 2 across the 9 of their papers we have counts for
6 papers · 1 filter
Neural Cluster First, Route Second: Capacitated Vehicle Routing via Differentiable Optimal Transport
Samuel J. K. Chin, Maximilian Schiffer
The Capacitated Vehicle Routing Problem (CVRP) underpins modern last-mile logistics, where routing decisions recur over the same fixed service area, like a city. In this setting, r…
Breaking the Grid: Distance-Guided Reinforcement Learning in Large Discrete Action Spaces
Heiko Hoppe, Fabian Akkerman, Wouter van Heeswijk +1
Reinforcement Learning (RL) is increasingly applied to large-scale decision-making problems like logistics, scheduling, and recommender systems, but existing algorithms struggle wi…
Combinatorial Optimization Augmented Machine Learning
Maximilian Schiffer, Heiko Hoppe, Yue Su +2
Combinatorial optimization augmented machine learning (COAML) has recently emerged as a powerful paradigm for integrating predictive models with combinatorial decision-making. By e…
Structured Reinforcement Learning for Combinatorial Decision-Making
Heiko Hoppe, Léo Baty, Louis Bouvier +2
Reinforcement learning (RL) is increasingly applied to real-world problems involving complex and structured decisions, such as routing, scheduling, and assortment planning. These s…
Preference-aware compensation policies for crowdsourced on-demand services
Georgina Nouli, Axel Parmentier, Maximilian Schiffer
Crowdsourced on-demand services offer benefits such as reduced costs, faster service fulfillment times, greater adaptability, and contributions to sustainable urban transportation…
Contextual Stochastic Vehicle Routing with Time Windows
Breno Serrano, Alexandre M. Florio, Stefan Minner +2
We study the vehicle routing problem with time windows (VRPTW) and stochastic travel times, in which the decision-maker observes related contextual information, represented as feat…