Query Minimization Under Stochastic Uncertainty

Chaplick, Steven; Halldórsson, Magnús M.; Lima, Murilo Santos de; Tonoyan, Tigran

doi:10.1007/978-3-030-61792-9_15

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Exploiting Stochastic Information1

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2021

2023

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Cited by 2 publications

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“…Recently, the setting of stochastic explorable uncertainty has received some attention in the context of sorting (Chaplick et al 2020) and the minimum problem (Bampis et al 2021), which Bampis et al (2021) phrases as a (hyper-)graph orientation problem.…”

Section: Exploiting Stochastic Informationmentioning

confidence: 99%

See 1 more Smart Citation

Explorable Uncertainty Meets Decision-Making in Logistics

Megow

Schlöter

2021

Dynamics in Logistics

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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.

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Section: Exploiting Stochastic Informationmentioning

confidence: 99%

“…They also show that the competitive ratio can be improved to 1.5 using randomization. Furthermore, Chaplick et al (2020) introduce an algorithm for sorting a single set of elements under stochastic uncertainty that is optimal in terms of the expected cost E[ALG(J )]. The competitive ratio of this algorithm is unknown.…”

Section: Bibliographical Notesmentioning

confidence: 99%