Multi-Objective Online Ride-Matching

41 Pages Posted: 17 Apr 2019 Last revised: 2 Sep 2019

See all articles by Guodong Lyu

Guodong Lyu

National University of Singapore (NUS) - NUS Business School

Wang Chi Cheung

Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR)

Chung-Piaw Teo

NUS Business School - Department of Decision Sciences

Hai Wang

Carnegie Mellon University - Heinz College of Information Systems and Public Policy; Singapore Management University - School of Information Systems

Date Written: March 20, 2019

Abstract

We study the following multi-period multi-objective online ride-matching problem. A ride-sourcing platform needs to match passengers and drivers in real time without observing future information, considering multiple objectives such as platform revenue, pick-up distance, and service quality. We develop an efficient online matching policy that adaptively balances the trade-offs among multiple objectives in a dynamic setting, and provide theoretical performance guarantee for the policy. We prove that the proposed adaptive matching policy can achieve a “target-based optimal solution”, i.e., a solution that minimizes the Euclidean distance to any pre-determined multi-objective target. Specifically, the outcome under our policy converges to the “compromise solution” if we set the utopia point as the target. Through numerical experiments and industrial testing using real data from a ride-sourcing platform, we demonstrate that our approach indeed obtains solutions that are closest to the pre-determined targets under various settings, in comparison to existing approaches. The policy presents solutions with delicate balance among multiple objectives and brings value to all the stakeholders in the ride-sourcing ecosystem comparing to benchmark policies: (1) drivers with higher service scores are dispatched with more orders and receive higher incomes; (2) passengers are more likely to be served by drivers with higher service scores, and passengers with higher order revenues are served with higher answer rates, at the expense of a small increase in pick-up distance; (3) the platform obtains a higher total revenue.

Keywords: Multi-Objective Optimization, Ride-Matching, Online Algorithm, Target-Based Optimal Solution

Suggested Citation

Lyu, Guodong and Cheung, Wang Chi and Teo, Chung-Piaw and Wang, Hai, Multi-Objective Online Ride-Matching (March 20, 2019). Available at SSRN: https://ssrn.com/abstract=3356823 or http://dx.doi.org/10.2139/ssrn.3356823

Guodong Lyu

National University of Singapore (NUS) - NUS Business School ( email )

15 Kent Ridge Drive
Singapore, 119245
Singapore

Wang Chi Cheung

Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR) ( email )

Singapore

Chung-Piaw Teo

NUS Business School - Department of Decision Sciences ( email )

15 Kent Ridge Drive
Mochtar Riady Building, BIZ 1 8-69
119245
Singapore

Hai Wang (Contact Author)

Carnegie Mellon University - Heinz College of Information Systems and Public Policy ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Singapore Management University - School of Information Systems ( email )

School of Information Systems
80 Stamford Road
Singapore 178902, 178899
Singapore

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