5 papers · 1 filter
Regularized Large Neighborhood Search
Germain Vivier-Ardisson, Laurent Demonet, Axel Parmentier +1
Operations research practitioners typically tackle NP-hard combinatorial problems using large neighborhood search (LNS), a scalable heuristic that iteratively refines a current sol…
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…
WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning
Kai Jungel, Dario Paccagnan, Axel Parmentier +1
When optimizing transportation systems, anticipating traffic flows is a central element. Yet, computing such traffic equilibria remains computationally expensive. Against this back…