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.
- Language
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Englisch
- Bibliographic citation
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Journal: Operations Research Perspectives ; ISSN: 2214-7160 ; Volume: 6 ; Year: 2019 ; Pages: 1-10 ; Amsterdam: Elsevier
- Classification
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Wirtschaft
- Subject
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Asymptotic approximation
Multi-stage
Nested Multinomial Logit
Optimal paths
Stochastic decision process
- Event
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Geistige Schöpfung
- (who)
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Roohnavazfar, Mina
Manerba, Daniele
De Martin, Juan Carlos
Tadei, Roberto
- Event
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Veröffentlichung
- (who)
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Elsevier
- (where)
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Amsterdam
- (when)
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2019
- DOI
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doi:10.1016/j.orp.2019.100124
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Roohnavazfar, Mina
- Manerba, Daniele
- De Martin, Juan Carlos
- Tadei, Roberto
- Elsevier
Time of origin
- 2019
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