Séminaire en format hybride à HEC Montréal (CSC) salle Florence ou Zoom

This talk will explore three facility location problems for smart and sustainable mobility. The first problem focuses on locating and sizing charging stations to support long-distance travel with electric vehicles. A bilevel optimization model is developed where the network planner (leader) minimizes infrastructure costs while meeting probabilistic service requirements for waiting times to charge. Electric vehicle users (followers) minimize their route lengths, affecting charging demand and wait times.

The bilevel problem is reduced to a single-level mixed-integer model when stations operate as M/M/c queues and users cooperate. A decomposition-based solution method is developed using a logic-based Benders algorithm. Computational experiments on benchmark and real-world highway networks analyze the impact of route choice, service requirements, and deviation tolerance on decisions. The second problem involves designing hub networks for the strategic deployment of autonomous shuttles. Given a set of passenger trips in an urban area, the goal is to determine the origins and destinations of a fixed number of hub arcs representing shuttle connections to maximize users of the system. This problem is formulated as a maximal covering hub arc location model and solved to optimality using Benders decomposition. Two data-driven clustering-based methodologies are also implemented and compared with the optimization model. Computational experiments using the New York City taxi data compare optimization and data-driven approaches on key performance metrics.

The final problem addresses infrastructure design for shared autonomous transportation by locating staging facilities and deploying autonomous lanes. A bi-objective model is developed that minimizes total travel distance as well as non-autonomous lane travel, given a lane deployment budget and the number of staging facilities to locate. Trade-offs are evaluated on benchmark instances.