KDD 2018滴滴派单算法论文。
We present a novel order dispatch algorithm in large-scale on-
demand ride-hailing platforms. While traditional order dispatch
approaches usually focus on immediate customer satisfaction, the
proposed algorithm is designed to provide a more efficient way to
optimize resource utilization and user experience in a global and
more farsighted view. In particular, we model order dispatch as a
large-scale sequential decision-making problem, where the decision
of assigning an order to a driver is determined by a centralized algo-
rithm in a coordinated way. The problem is solved in a learning and
planning manner: 1) based on historical data, we first summarize
demand and supply patterns into a spatiotemporal quantization,
each of which indicates the expected value of a driver being in a
particular state; 2) a planning step is conducted in real-time, where
each driver-order-pair is valued in consideration of both immedi-
ate rewards and future gains, and then dispatch is solved using
a combinatorial optimizing algorithm. Through extensive offline
experiments and online AB tests, the proposed approach delivers
remarkable improvement on the platform’s efficiency and has been
successfully deployed in the production system of Didi Chuxing.
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