LAUSR

In recent years, innovative ride‐sharing systems have gained significant attention. In such systems, dynamic fleet management covers demand and fulfillment control to determine which stochastically incoming requests are to be satisfied and how vehicle resources are utilized for their fulfillment, respectively. Demand and fulfillment control can be implemented ranging from straightforward myopic to more sophisticated anticipatory. In this paper, our aim is twofold: (1) we want to classify how policies implement demand and fulfillment control in the related literature on dynamic fleet management; (2) we want to explore the effectiveness of demand and fulfillment control under varying conditions in order to identify benefits and risks for ride‐sharing systems. To this end, we define policies that differ in the optimization of demand and/or fulfillment control through the exploitation of either confirmed or complete information. Our experimental results demonstrate that demand and fulfillment control affect the performance and service quality of ride‐sharing systems quite differently.