Approximate dynamic programming for planning a ride-hailing system using autonomous fleets of electric vehicles

•A comprehensive mathematical model for an autonomous fleet of electric vehicles.•Optimizing over time by capturing the characteristics of the trips demand using value functions.•Developing an approximate dynamic programming framework for the value functions.•Developing a dispatch problem that captu...

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Veröffentlicht in:European journal of operational research Jg. 284; H. 3; S. 1088 - 1106
Hauptverfasser: Al-Kanj, Lina, Nascimento, Juliana, Powell, Warren B.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.08.2020
Schlagworte:
Adaptive learning
Approximate dynamic programming
Autonomous vehicles
Ride-Hailing
Surge pricing
Adaptive learning
Approximate dynamic programming
Surge pricing
Ride-Hailing
Autonomous vehicles
ISSN:0377-2217, 1872-6860
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Zusammenfassung:•A comprehensive mathematical model for an autonomous fleet of electric vehicles.•Optimizing over time by capturing the characteristics of the trips demand using value functions.•Developing an approximate dynamic programming framework for the value functions.•Developing a dispatch problem that captures many decisions to operate the fleet.•Developing a surge pricing framework to decide on the trip price using adaptive learning. We address a comprehensive ride-hailing system taking into account many of the decisions required to operate it in reality. The ride-hailing system is formed of a centrally managed fleet of autonomous electric vehicles which is creating a transformative new technology with significant cost savings. This problem involves a dispatch problem for assigning riders to cars, a surge pricing problem for deciding on the price per trip and a planning problem for deciding on the fleet size. We use approximate dynamic programming to develop high-quality operational dispatch strategies to determine which car is best for a particular trip, when a car should be recharged, when it should be re-positioned to a different zone which offers a higher density of trips and when it should be parked. These decisions have to be made in the presence of a highly dynamic call-in process, and assignments have to take into consideration the spatial and temporal patterns in trip demand which are captured using value functions. We prove that the value functions are monotone in the battery and time dimensions and use hierarchical aggregation to get better estimates of the value functions with a small number of observations. Then, surge pricing is discussed using an adaptive learning approach to decide on the price for each trip. Finally, we discuss the fleet size problem.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2020.01.033