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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zhiwei (Tony) Qin & Xiaocheng Tang & Yan Jiao & Fan Zhang & Zhe Xu & Hongtu Zhu & Jieping Ye, 2020. "Introduction: Ride-Hailing Order Dispatching at DiDi via Reinforcement Learning," Interfaces, INFORMS, vol. 50(5), pages 272-286, September.
    • Handle: RePEc:inm:orinte:v:50:y:2020:i:5:p:272-286
    DOI: 10.1287/inte.2020.1047
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    References listed on IDEAS

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    2. H. W. Kuhn, 1955. "The Hungarian method for the assignment problem," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 2(1‐2), pages 83-97, March.
    3. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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    Cited by:

    1. Long He & Sheng Liu & Zuo‐Jun Max Shen, 2022. "Smart urban transport and logistics: A business analytics perspective," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3771-3787, October.

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