7 papers
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…
Optimizing a Worldwide-Scale Shipper Transportation Planning in a Carmaker Inbound Supply Chain
Mathis Brichet, Maximilian Schiffer, Axel Parmentier
We study the shipper-side design of large-scale inbound transportation networks, motivated by the global supply chain of the carmaker Renault. We formalize the Shipper Transportati…
Managing delay in tail assignment: from minimum turn time to stochastic routing at Air France
Léo Baty, Axel Parmentier
On-time performance is a critical challenge in the airline industry, leading to large operational and customer dissatisfaction costs. The tail assignment problem builds the sequenc…
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…
Operational route planning under uncertainty for Demand Adaptive Systems
Benedikt Lienkamp, Mike Hewitt, Axel Parmentier +1
With an increasing need for more flexible mobility services, we consider an operational problem arising in the planning of Demand Adaptive Systems (DAS). Motivated by the decision…