Introduction: Ride-Hailing Order Dispatching at DiDi via Reinforcement Learning

Zhiwei (Tony) Qin (), Xiaocheng Tang (), Yan Jiao (), Fan Zhang (), Zhe Xu (), Hongtu Zhu () and Jieping Ye ()
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Zhiwei (Tony) Qin: DiDi Labs, Mountain View, California 94043
Xiaocheng Tang: DiDi Labs, Mountain View, California 94043
Yan Jiao: DiDi Labs, Mountain View, California 94043
Fan Zhang: Didi Chuxing, Beijing 100193, China
Zhe Xu: Didi Chuxing, Beijing 100193, China
Hongtu Zhu: Didi Chuxing, Beijing 100193, China
Jieping Ye: Didi Chuxing, Beijing 100193, China

Interfaces, 2020, vol. 50, issue 5, 272-286

Abstract: Order dispatching is instrumental to the marketplace engine of a large-scale ride-hailing platform, such as the DiDi platform, which continuously matches passenger trip requests to drivers at a scale of tens of millions per day. Because of the dynamic and stochastic nature of supply and demand in this context, the ride-hailing order-dispatching problem is challenging to solve for an optimal solution. Added to the complexity are considerations of system response time, reliability, and multiple objectives. In this paper, we describe how our approach to this optimization problem has evolved from a combinatorial optimization approach to one that encompasses a semi-Markov decision-process model and deep reinforcement learning. We discuss the various practical considerations of our solution development and real-world impact to the business.

Keywords: ride-hailing marketplace; order dispatching; reinforcement learning; data-driven decision making (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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