Optimization of carsharing fleet size to maximize the number of clients served

Abstract

Carsharing services aim at offering short-term car rentals. Those rentals can be offered in different modalities, such as round-trip and one-way. This work simulates the dynamics of possible carsharing clients searching for resources availability and using the carsharing service. Clients can rent a vehicle if there is an available car at the client's origin station and an available parking slot at the client's destination station. If there is no car or parking slot available, the possible clients look for these resources in other stations nearby their origin and destination. Together with this Agent-Based simulation, the fleet of vehicles is optimized to serve as many clients as possible and to avoid wasting resources. This work proposes a Mixed-Integer Linear Programming Model to optimize the fleet size of a carsharing service for the one-way and round-trip modes while simulating the clients interaction. Different scenarios are analyzed using real parameters and spatial data from the city of São Paulo, Brazil. Those scenarios are composed for round-trip and one-way, varying the number of possible clients, the maximum number of available vehicles, different sets of carsharing stations and the corresponding number of parking slots. We also explore the distance walked by clients to find an available vehicle to rent or a parking slot to deliver the rented vehicle. The Agent-Based simulation showed that clients' flexibility to walk was well aligned with a higher allocation of vehicles in the same station, since it increased the possibility of sharing cars among nearby clients. Also, results show that round-trip services can scale-up better than one-way services, and that distances walked by the clients to be served are essential to make the most of the one-way mode's vehicles and parking slots.