Multi-Objective Vehicle Rebalancing for Ridehailing System using a Reinforcement Learning Approach
Abstract
The problem of designing a rebalancing algorithm for a large-scale ridehailing system with asymmetric demand is considered here. We pose the rebalancing problem within a semi Markov decision problem (SMDP) framework with closed queues of vehicles serving stationary, but asymmetric demand, over a large city with multiple nodes (representing neighborhoods). We assume that the passengers queue up at every node until they are matched with a vehicle. The goal of the SMDP is to minimize a convex combination of the waiting time of the passengers and the total empty vehicle miles traveled. The resulting SMDP appears to be difficult to solve for closed-form expression for the rebalancing strategy. As a result, we use a deep reinforcement learning algorithm to determine the approximately optimal solution to the SMDP. The trained policy is compared with other well-known algorithms for rebalancing, which are designed to address other objectives (such as to minimize demand drop probability) for the ridehailing problem.
Cite
@article{arxiv.2007.06801,
title = {Multi-Objective Vehicle Rebalancing for Ridehailing System using a Reinforcement Learning Approach},
author = {Yuntian Deng and Hao Chen and Shiping Shao and Jiacheng Tang and Jianzong Pi and Abhishek Gupta},
journal= {arXiv preprint arXiv:2007.06801},
year = {2020}
}