Simultaneous Assignment and Pricing for Multi-Objective Online Ride-Hailing Problem Model

Abstract

This paper proposed an assignment model for online ride-hailing (ORH). ORH platform acts as an intermediary and their main job is to match passenger requests to the driver. Drivers and passengers might accept or reject the assignment result based on their individual preferences. From the platform's standpoint, it is necessary to ensure that the acceptance likelihood is high. To do this, the platform needs to consider each party's interests, and it often involves a multi-objective characteristic. Drivers are income-sensitive, while passengers are interested in a safe trip, low trip price, and short pickup distance. Several researchers have considered these factors in their assignment model but overlooked the possibility of dependency among factors as well as the heterogeneity of drivers and passenger's behavior. In this paper, we propose an assignment model which considers the multi-objective characteristic, factors dependency, and customer behavior's heterogeneity. A weighted-sum multi-objective optimization model is used to find the optimal solution, which allows us to incorporate the interests of the ORH platform, drivers, and passengers into the model. We defined how the trip price could affect the driver's and passenger's pickup distance tolerance and solved simultaneously the assignment and trip price determination. We tested the model developed using hypothetical data and showed that the solution of the model is better compared to the output of the general assignment models which use a distance minimization policy.