Artikel

Optimal paths in multi-stage stochastic decision networks

This paper deals with the search of optimal paths in a multi-stage stochastic decision network as a first application of the deterministic approximation approach proposed by Tadei et al. [48]. In the network, the involved utilities are stage-dependent and contain random oscillations with an unknown probability distribution. The problem is modeled as a sequential choice of nodes in a graph layered into stages, in order to find the optimal path value in a recursive fashion. It is also shown that an optimal path solution can be derived by using a Nested Multinomial Logit model, which represents the choice probability at the different stages. The accuracy and efficiency of the proposed method are experimentally proved on a large set of randomly generated instances. Moreover, insights on the calibration of a critical parameter of the deterministic approximation are also provided.

Sprache
Englisch

Erschienen in
Journal: Operations Research Perspectives ; ISSN: 2214-7160 ; Volume: 6 ; Year: 2019 ; Pages: 1-10 ; Amsterdam: Elsevier

Klassifikation
Wirtschaft
Thema
Asymptotic approximation
Multi-stage
Nested Multinomial Logit
Optimal paths
Stochastic decision process

Ereignis
Geistige Schöpfung
(wer)
Roohnavazfar, Mina
Manerba, Daniele
De Martin, Juan Carlos
Tadei, Roberto
Ereignis
Veröffentlichung
(wer)
Elsevier
(wo)
Amsterdam
(wann)
2019

DOI
doi:10.1016/j.orp.2019.100124
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Roohnavazfar, Mina
  • Manerba, Daniele
  • De Martin, Juan Carlos
  • Tadei, Roberto
  • Elsevier

Entstanden

  • 2019

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