Seminar: Lina Al-Kanj, Princeton University

Lina Al-Kanj, associate research scholar at the Operations Research and Financial Engineering Department at Princeton University, presents a seminar on “Approximate Dynamic Programming for Planning Driverless Fleets of Electric Vehicles” at the C2SMART Center.

Abstract

Within a decade, almost every major auto company, along with fleet operators such as Uber and Lyft, have announced plans to put driverless vehicles on the road. At the same time, electric vehicles are quickly emerging as a next-generation technology that is cost effective, in addition to offering the benefits of reducing the carbon footprint. The combination of a centrally managed fleet of driverless vehicles, along with the operating characteristics of electric vehicles, is creating a transformative new technology that offers significant cost savingswith high service levels.

This problem involves a control problem for assigning requesters to cars and a planning problem for deciding on the fleet size, which are high dimensional stochastic dynamic programs. In this work, we propose to use approximate dynamic programming to develop high-quality operational control strategies to determine which car (given the battery level) is best for a particular trip (considering its length and destination), when a car should be recharged, andwhen it should be re-positioned to a different zone that offers a higher density of trips. We then propose to use outputs (in the form of value functions) from the operational planning model to optimize the distribution of battery capacities in the fleet. We then determine the number of cars required to provide a high level of service, and from this to understand the economics of a driverless fleet of electric vehicles.

Bio

Lina Al-Kanj is an Associate Research Scholar at the Operations Research and Financial Engineering Department at Princeton University. She received her PhD in Electrical and Computer Engineering from the American University of Beirut. Her research interests include optimal resource allocation and scheduling, stochastic optimization, dynamic programming and optimal learning with applications to transportation, communication and energy systems.

Event Details:

Wednesday, April 25 at 1 p.m.

6 Metrotech Center, 4th Floor