9 papers
Scalable Dynamic Pricing of Substitutable Products through Structure-Guided Policy Learning
Yue Su, Antoine Désir, Axel Parmentier
Problem definition: We study dynamic pricing of substitutable products with finite, product-specific inventories. Customer substitution couples pricing decisions across products, w…
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…
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…
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…
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…
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…