Optimal paths in multi-stage stochastic decision networks

Mina Roohnavazfar, Daniele Manerba, Juan Carlos De Martin and Roberto Tadei

Operations Research Perspectives, 2019, vol. 6, issue C

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.

Keywords: Optimal paths; Stochastic decision process; Multi-stage; Asymptotic approximation; Nested Multinomial Logit (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:oprepe:v:6:y:2019:i:c:s221471601930096x

DOI: 10.1016/j.orp.2019.100124

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