The dynamic ride-hailing sharing problem with multiple vehicle types and user classes

This paper proposes a dynamic ride-hailing sharing problem with multiple vehicle types and user classes. Ride-hailing vehicles (RHVs) can be classified into express ride-hailing vehicles (ERHVs) and premier ride-hailing vehicles (PRHVs) according to service levels. PRHVs can provide the high-quality ride-hailing service with upmarket vehicles and ERHVs provide the normal ride-hailing service with normal vehicles. The fare of PRHVs is higher. PRHVs can be temporarily used as ERHVs to serve the customers who order ERHVs with or without ride-sharing, which is referred to as the substitution of ERHVs with PRHVs. A lexicographic multi-objective function with three-level objectives is proposed to formulate the problem, in which the first-level objective is to maximize the profit of the platform, the second-level objective is to minimize the number of requests of customers who involve ordering ERHVs matched to PRHVs, and the third-level objective is to minimize the total driving distance of all RHVs. The dynamic problem is divided into a set of continuous and small ride-hailing sharing subproblems based on equal time intervals. Each subproblem is formulated as a mixed integer nonlinear program for matching RHVs to the requests collected in the last time interval or unmatched in previous time intervals and re-scheduling the vehicle routes. To solve the subproblems, a new solution method is proposed based on the modified artificial bee colony algorithm developed by Zhan et al. (2021). Numerical examples using real request data from Didi are given to explore the problem properties, and the results gain insights into the ride-hailing market. For example, the profit of the platform and the number of matched requests are higher when the substitution of ERHVs with PRHVs is allowed while the matching percentage of requests of customers who select a mixed choice is higher when there is no substitution. Different ratios of vehicle types and user classes influence the performance of the ride-hailing sharing market (e.g., the profit of the platform, the number of matched requests, matching percentage, etc.). The value of the fare discount multiplier for the passengers who successfully share RHVs with others can affect the number of shared requests, the number of matched requests, and platform profitability.