2 citations · 2 across the 9 of their papers we have counts for
12 papers
Annealed Sinkhorn with Momentum: Certified Unregularized Optimal Transport in Linear Memory
Samuel J. K. Chin, Maximilian Schiffer
We characterize Bregman Douglas-Rachford splitting (BDRS) for unregularized discrete optimal transport and develop an anytime primal-dual certificate in linear memory. We first est…
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…
Linear Decision Tree Policies for Integer Linear Programs
Théo Guyard, Cleber Oliveira, Maximilian Schiffer +2
We study optimal decision policies, represented as linear decision trees, for integer linear programs with a fixed feasible set and varying cost vectors. Once synthesized for a giv…
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…
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…
Dynamic capacity allocation of hybrid transportation units for cargo-hitching in urban public transportation systems
Paul Bischoff, Benedikt Lienkamp, Tarun Rambha +1
To improve the utilization of public transportation systems (PTSs) during off-peak hours, we present an algorithmic framework that designs PTSs with hybrid transportation units (HT…
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…
Branch-and-Price for the Stochastic TSP with Generalized Latency
Benedikt Lienkamp, Mike Hewitt, Maximilian Schiffer
Motivated by the tactical planning level of demand adaptive public transportation systems, we present the stochastic symmetric traveling salesman problem with generalized latency (…