Maximizing Carsharing Profits: An Optimization Model to Support the Carsharing Planning

Abstract

Carsharing services aim to offer short-term car rentals, including round-trip and one-way alternatives. Round-trip clients must deliver the rented car at the same station where the rental has started. One-way clients can return the vehicle in a different station. This work proposes a Mixed-Integer Linear Programming Model to optimize the fleet-sizing of a carsharing service for the one-way and round-trip alternatives, seen as utilization scenarios. The proposed model aims to maximize the company’s profit, finding the best number of vehicles to be allocated to each carsharing station. Different scenarios were analyzed for the one-way and round-trip settings, varying service costs, rental prices, number of clients, rental duration and driven distance. Simulations were performed using real spatial data from the city of São Paulo, Brazil. Results showed that round-trip profits can benefit from rentals with higher durations, and that one-way profits can overcome the profits from round-trip if user demand and number of available vehicles are enough.