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Data‐Driven Driver Dispatching System with Allocation Constraints and Operational Risk Management for a Ride‐Sharing Platform
Author(s) -
Wang Tianyu,
Wu Desheng
Publication year - 2020
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/deci.12433
Subject(s) - computer science , time horizon , operations research , heuristic , control (management) , mathematical optimization , mode (computer interface) , time allocation , real time computing , engineering , economics , mathematics , management , artificial intelligence , operating system
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.