The encyclopedia · Software & IT · Technical decision · 2017–2019
DiDi treated every ride as one move in a long optimization
DiDi frames order dispatching as a Markov decision process, so each match improves response, fulfilment and driver income across the whole day.
DiDi Chuxing
the move
DiDi, China's largest ride-hailing platform, dispatches orders to drivers in real time. Treating each request separately and taking the nearest driver was simple but could not see how a match would affect the next rider or the rest of the day.
The company modeled dispatching as a Markov decision process and used machine learning to optimize assignments across the whole network, so each match is a step toward a better long-run state rather than a locally cheap one.
The winning application improved response rates, fulfilment rates and driver income, and earned the 2019 INFORMS Daniel H. Wagner Prize for implemented operations research.
why it works
- Independent greedy matches cannot see the next request coming
- A Markov decision process captures the sequence of trips in a day
- Machine learning lets the model value a driver's future availability
- Improving the whole network raises response, fulfilment and income together
what transfers
Operate the fleet as a linked sequence of decisions, not independent requests; optimizing the long-run state beats greedily minimizing each trip.
what came after
DiDi's dispatching system won the 2019 INFORMS Daniel H. Wagner Prize. The work showed that ride-hailing dispatch could be solved as a large sequential optimization rather than a rule of nearest-first.
references
- Introduction: 2019 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research
- Ride-Hailing Order Dispatching at DiDi via Reinforcement Learning
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