Revenue Management Meets Carsharing: Optimizing the Daily Business

Abstract

Carsharing is a transportation alternative that enables flexible use of a vehicle instead of owning it by paying trip-dependent fees. In recent years, this service denotes a considerable increase of new providers, which face an exponentially growing number of customers worldwide. As a consequence, rising vehicle utilization leads providers to contemplate revenue management elements. When focusing on station-based carsharing concepts, these are typically based on advance reservations. This makes them perfectly suitable for the application of demand-side management approaches. Demand-side management allows providers to optimize their revenues by accepting or rejecting certain trips. We respectively develop an optimization model for revenue management support. Based on an existing model of the hotel business, special consideration is drawn to carsharing related features. For instance, the implementation of a heterogeneously powered fleet allows providers to choose a certain limit of emissions to fulfill local requirements. We implement the mathematical model into the modeling environment GAMS using the solver Couenne. Conducted benchmarks show sensitivities under the variation of different input values, for example risk tolerances. In contrast to the often used first-come first-serve-principle, the results indicate the usefulness of the developed model in optimizing revenues of todays carsharing providers.