Order Dispatching for Electric Vehicles by Deep Multi-Objective Reinforcement Learning

MPhil Thesis Defence


Title: "Order Dispatching for Electric Vehicles by Deep Multi-Objective 
Reinforcement Learning"

By

Mr. Jinliang DENG


Abstract

The electrification of fleets is intensifying in on demand ride-hailing 
platforms (e.g. Uber, Lyft, and Didi Chuxing), but a major technical 
barrier for deploying electric vehicles is that an electric vehicle has a 
long charging time and relatively lower mileage when fully charged than 
fully filled conventional fuel vehicles.  Subsequently, a major challenge 
for the ride-hailing platform is how to adopt the unique characteristics 
of electric vehicles into their order dispatching system. The aim of this 
work is to develop a formal dispatching framework that targets at 
improving the incoming of electric vehicle drivers on the platform, while 
considering attainability to charging stations to each vehicle.

Specifically, we formulate the dispatching problem as a Mutli-Objective 
Markov Decision Process (MOMDP) and solve it with a novel reinforcement 
learning approach.  Our approach that generates the candidate driver-order 
matching pairs consists of two steps: (i) the estimation of potential gain 
for each possible matching;  and (ii) the elimination of the matching 
pairs that lead the electric vehicles to an out-of-power situation. The 
candidate matching pairs with the corresponding gain are then passed 
through the Maxflow algorithm to extract a consistent combination of pairs 
with the highest aggregated gain. The experiments show that our algorithm 
can improve the driver income, while preventing electric vehicles from 
running out of power on the road.


Date:			Monday, 29 April 2019

Time:			4:00pm - 6:00pm

Venue:			Room 4621
 			Lifts 31/32

Committee Members:	Prof. Qiang Yang (Supervisor)
 			Dr. Xiaojuan Ma (Supervisor)
 			Prof. Fangzhen Lin (Chairperson)
 			Dr. Yangqiu Song


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