Stochastic Dynamic Vehicle Routing in the Light of Prescriptive Analytics: A Review

Ninja Söffker, Marlin W. Ulmer, Dirk C. Mattfeld

Veröffentlichungen: Beitrag in FachzeitschriftArtikelPeer Reviewed


Stochastic dynamic vehicle routing problems have become an essential part of logistics and mobility services. In such problems, a sequence of vehicle routing decisions have to be made in reaction and anticipation of newly revealed stochastic information. To this end, a variety of computational operations research methods has emerged in the literature, increasingly integrating potential future information in their decision making. The integration of information models into decision models via computational methods is known as prescriptive analytics, the most recent advance of business analytics. In this paper, we explore the existing work and future potential of prescriptive analytics for stochastic dynamic vehicle routing. We identify the characteristics of decision models and information models unique in stochastic dynamic vehicle routing and analyze how different methodology meets the characteristics requirements. We use the insights to derive recommendations about promising methodology when approaching specific stochastic dynamic vehicle routing problems.
Seiten (von - bis)801-820
FachzeitschriftEuropean Journal of Operational Research
Frühes Online-Datum16 Juli 2021
PublikationsstatusVeröffentlicht - 1 Mai 2022

ÖFOS 2012

  • 101015 Operations Research