Explorable Uncertainty Meets Decision-Making in Logistics

Nicole Megow () and Jens Schlöter ()
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Nicole Megow: University of Bremen
Jens Schlöter: University of Bremen

A chapter in Dynamics in Logistics, 2021, pp 35-56 from Springer

Abstract: Abstract Decision-making under uncertainty is a major challenge in logistics. Mathematical optimization has a long tradition in providing powerful methods for solving logistics problems. While classical optimization models for uncertainty in the input data do not consider the option to actively query the precise value of uncertain input elements, this option is in practice often available at a certain cost. The recent line of research on optimization under explorable uncertainty develops methods with provable performance guarantees for such scenarios. In this chapter, we highlight some recent results from the mathematical optimization perspective and outline the potential power of such model and techniques for solving logistics problems.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-88662-2_2

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DOI: 10.1007/978-3-030-88662-2_2

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