Dynamic Discrimination Pricing and Freelance Drivers to Rebalance Mixed-Fleet Carsharing Systems

This paper presents pricing strategies for the relocation problem in mixed carsharing systems with heterogeneous users. On the demand side, a dynamic discriminatory pricing approach is introduced, which can obviate the need for operation-based repositioning, partially or fully. Moreover, it’s assumed that there are earnings-sensitive freelance drivers who may decide to participate in relocation operations, and their decisions depend on the wages are offered by the operation manager. As the operational decisions (trip pricing, relocators hiring, and charging scheduling) are tightly connected, and their interplay can significantly affect the performance of the carsharing system, an integrated model is proposed to optimize trip price and staff wage problems along with vehicle allocation and charge scheduling. To cope with computation difficulties for solving the proposed model, this paper presents a solution framework based on a rolling horizon approach. Through extensive numerical experiments, the performance and effectiveness of the proposed model are investigated under different assumptions and parameter settings. The results confirm that the proposed pricing strategies outperform common strategies in the literature and can significantly improve the performances of carsharing systems. The sensitivity analysis of several key parameters provides managerial insights into the operations management of carsharing systems.