Effectiveness of demand and fulfillment control in dynamic fleet management of ride‐sharing systems

Abstract

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.

Dynamic vehicle routing with random requests: A literature review

“…Regardless of the problem formulation (DD or DS), most rejection strategies adopted in DPDPs are reactive in the sense that a dynamic request is rejected only if it cannot be feasibly inserted into any vehicle's route. The only two anticipatory rejection strategies are proposed by Yang et al (2004) and Haferkamp and Ehmke (2022). Vehicle capacity and hard time windows are the common constraints that limit vehicles' service capability.…”

“…The existing research on DS DPDPs has confirmed the benefits of exploiting the probabilistic information on future P&D requests. For a wide variety of DPDP applications, including FTL transportation (Ghiani et al, 2009;Zolfagharinia and Haughton, 2016), LTL transportation (Ghiani et al, 2009;Vonolfen and Affenzeller, 2016;Steever et al, 2019), DARPs with ride-sharing (Sáez et al, 2008;Cortés et al, 2009;Muñoz-Carpintero et al, 2015;Sayarshad and Chow, 2015;Haferkamp and Ehmke, 2022) and without ride-sharing (Sheridan et al, 2013), extensive computational studies have validated that anticipatory (or proactive) scheduling and routing policies outperform myopic (or reactive) ones.…”

Directions for future research on urban mobility and city logistics

“…In the last decade, service providers already experienced an increase in uncertainty on the demand side as customers used their mobile devices to order goods or transportation online [86]. Consequently, planning and control had to be adapted in reaction and anticipation of new demand [44]. With this already being challenging enough, recently an additional dimension of uncertainty emerged, on the supply side.…”

“…Particularly, advancements in information technology, vehicle autonomy and connectivity, vehicle electrification, payment methodologies, and clearing solutions have enabled creating new types of services and reshaping existing ones. Notable trending topics include: drones and autonomous ground vehicles [1,29,52,57,70,74,75], physical internet for parcel and cargo delivery [14,19,80], electric vehicles and micromobility [43,71], ride-sharing and ride hailing [44,68], crowd shipping and the gig economy [7,82,93], same-day delivery of goods and meals [32,86], collaborated transportation and brokering [30,40].…”

Preface: Special issue on the future of city logistics and urban mobility

“…Haferkamp and Ehmke [3] study demand and fulfillment control policies in ride‐sharing systems. They classify the existing policies in the literature and explore the effectiveness of such policies under varying conditions in order to identify benefits and risks for ride‐sharing systems.…”