期刊
DECISION SCIENCES
卷 51, 期 6, 页码 1490-1520出版社
WILEY
DOI: 10.1111/deci.12433
关键词
Data-Driven Analytics; Driver Dispatching System; Operational Risk Management; and Ride-Sharing
类别
资金
- Ministry of Science and Technology of China [2016YFC0503606]
- National Natural Science Foundation of China [71825007]
- Chinese Academy of Sciences Frontier Scientific Research Key Project [QYZDB-SSWSYS021]
- Marianne and MarcusWallenberg Foundation [MMW2015.0007]
- Strategic Priority Research Program of CAS [XDA23020203]
- International Partnership Program of Chinese Academy of Sciences [211211KYSB20180042]
In this article, we develop and analyze a driver dispatching system for a control center that aims to minimize passengers' waiting time. The system imposes allocation constraints that ensure a minimum number of drivers in different regions to manage operational risk. The data-driven system is based on Rolling Time Horizon approach and utilizes knowledge learned from historical data. It incorporates a hybrid forecasting model and a heuristic algorithm to solve the off-line problem in each iteration. We show that the NP-hardness of the off-line problem lies in allocation constraints. We test the performance of the system with a simulation study based on actual past taxi order data. The result suggests that the system markedly decreases the average waiting time and saves planning time in comparison with the request-driven dispatching mode. The result also demonstrates that in nonextreme cases, the dispatching system finds an acceptable solution which approximately satisfies allocation constraints while guaranteeing a short increase in waiting time.
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