Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/246399 
Year of Publication: 
2019
Citation: 
[Journal:] Operations Research Perspectives [ISSN:] 2214-7160 [Volume:] 6 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2019 [Pages:] 1-10
Publisher: 
Elsevier, Amsterdam
Abstract: 
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.
Subjects: 
Asymptotic approximation
Multi-stage
Nested Multinomial Logit
Optimal paths
Stochastic decision process
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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