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. (2019). 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.

Optimal paths in multi-stage stochastic decision networks / Roohnavazfar, Mina; Manerba, Daniele; DE MARTIN, JUAN CARLOS; Tadei, Roberto. - In: OPERATIONS RESEARCH PERSPECTIVES. - ISSN 2214-7160. - ELETTRONICO. - 6:100124(2019), pp. 1-10. [10.1016/j.orp.2019.100124]

Optimal paths in multi-stage stochastic decision networks

ROOHNAVAZFAR, MINA;Daniele Manerba;Juan Carlos De Martin;Roberto Tadei
2019

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. (2019). 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2752646
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