Customer-Oriented Optimal Vehicle Assignment in Mobility-on-Demand Systems

In this paper, we introduce a novel optimization framework for a station-to-door mobility-on-demand system that aims at ensuring an efficient transportation service for the daily mobility of passengers in densely populated urban areas. We propose a mixed integer linear programming approach that maximizes both the customers' satisfaction and the provider's revenues, keeping at the same time the number of vehicles in each station within given bounds towards the improvement of system balancing. The proposed customer-oriented approach aims at meeting as many customer requests as possible, while maximizing the provider revenues and reducing the customers' impatience, thus increasing their satisfaction. This implies, in turn, a better reputation for the service provider. The performance of the proposed approach is assessed through an extensive Monte Carlo simulation campaign. In particular, through the analysis of different performance indices, we compare the optimal solution of the proposed approach with the optimal solution achieved by a previously presented approach based on the Profitable Tour Problem.