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A Data-Driven Matching Algorithm For Ride Pooling Problem


3 avr. 2023   11h00 — 12h00

Ismail Sevim Université de Montréal, Canada

Ismail Sevim

Présentation sur YouTube.

This paper proposes a data-driven matching algorithm for the problem of ride pooling, which is a transportation mode enabling people to share a vehicle for a trip. The problem is considered as a variant of matching problem, since it aims to find a matching between drivers and riders. Proposed algorithm is a machine learning algorithm based on rank aggregation idea, where every feature in a multi-feature dataset provides a ranking of candidate drivers and weight for each feature is learned from past data through an optimization model. Once weight learning and candidate ranking problems are considered simultaneously, resulting optimization model becomes a nonlinear bilevel optimization model, which is reformulated as a single level mixed-integer nonlinear optimization model. To demonstrate the performance of the proposed algorithm, a real-life dataset from a mobile application of a ride pooling start-up company is used and company’s current approach is considered as benchmark.

Séminaire conjoint avec Quentin Bertrand.

Federico Bobbio responsable
Defeng Liu responsable
Léa Ricard responsable


Séminaire hybride au GERAD
Zoom et salle 4488
Pavillon André-Aisenstadt
Campus de l'Université de Montréal
2920, chemin de la Tour

Montréal Québec H3T 1J4

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