Conference Agenda

Shared mobility-on-demand (SMOD) systems are expected to make public transport more sustainable, especially in rural areas. From the social perspective, this means that an SMOD system should contribute to providing area-wide, reliable, and non-discriminatory access to mobility. In ecological terms, the main aim is to decrease emissions per passenger kilometer and to prevent that the SMOD service cannibalizes scheduled public transport services. Finally, economic efficiency must also be considered to limit the subsidy requirements while avoiding that the provider increases prices purely for capitalizing on the consumer surplus. Since providers typically offer customers multiple fulfillment options, i.e., rides with alternative pick-up times, dynamic pricing can be applied to actively steer demand, and thereby, improve the system’s sustainability. In this talk, we discuss how the resulting integrated demand management and vehicle routing problem can be modeled. Thereby, we consider sustainability-based objectives in addition to the usual profit maximization. We focus particularly on the dynamic pricing sub-problem, which can be cast as a multi-objective, constrained assortment optimization problem. Finally, we present a decomposition-based solution concept, which we evaluate based on real-world data from a rural SMOD provider.

Free-floating vehicle sharing systems such as car sharing systems offer customers the flexibility to pick up and drop off vehicles at any location within the business area. However, this flexibility comes with the drawback that vehicles tend to accumulate at locations with low demand. To counter these imbalances, pricing has proven to be an effective and cost-efficient means. The fact that modern systems rely on mobile applications for their communication with customers, combined with the fact that providers know the exact location of each vehicle in real-time, offers new opportunities for pricing. We develop a profit-maximizing dynamic pricing approach which is customer-centric, meaning that, whenever a customer opens the mobile application, the price optimization incorporates the customer’s location as well as the customer’s choice behavior. In particular, it considers the effects of prices and walking distances to available vehicles on the customer’s rental decision. Further, the approach anticipates future vehicle locations, rentals, and profits. More specifically, we propose an approximate dynamic programming-based solution approach with nonparametric value function approximation. It allows direct application in practice, because historical data can readily be used and key parameters can be precomputed such that the online pricing problem becomes tractable.