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