强化学习
计算机科学
调度(生产过程)
符号
匹配(统计)
二部图
组合优化
比例(比率)
订单(交换)
运筹学
人工智能
数学优化
理论计算机科学
算法
图形
物理
经济
统计
算术
量子力学
数学
财务
作者
Yongxin Tong,Dingyuan Shi,Yi Xu,Weifeng Lv,Zhiwei Qin,Xiaocheng Tang
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2021-11-10
卷期号:35 (10): 9812-9823
被引量:20
标识
DOI:10.1109/tkde.2021.3127077
摘要
Ride hailing has become prevailing. Central in ride hailing platforms is taxi order dispatching which involves recommending a suitable driver for each order. Previous works use pure combinatorial optimization solutions for taxi dispatching, which suffer in practice due to complex dynamics of demand and supply and temporal dependency among dispatching decisions. Recent studies try to adopt data-driven method into combinatorial optimization hoping knowledge from history data would help overcome these challenges. Among these attempts, adoption of reinforcement learning shows great promise but current adoptions are a unidirectional integration which restricts the potential performance gains. In this work, we propose L earning T o D ispatch(LTD), a systematic solution that allows synergic integration of reinforcement learning and combinatorial optimization for large-scale taxi order dispatching. We demonstrate the necessity of online learning and taxi scheduling for reinforcement learning to work in synergy with combinatorial optimization, and devise corresponding algorithms. We also devise many tricks for more efficient calculation of the bipartite matching. Experiments show our methods can improve $36.4\%$ and $42.0\%$ on utility and efficiency at most, respectively. Especially, it achieves state-of-the-art performance in terms of utility.
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