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Optimal paths in multi-stage stochastic decision networks

Author

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

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.

Suggested Citation

  • Roohnavazfar, Mina & Manerba, Daniele & De Martin, Juan Carlos & Tadei, Roberto, 2019. "Optimal paths in multi-stage stochastic decision networks," Operations Research Perspectives, Elsevier, vol. 6(C).
    • Handle: RePEc:eee:oprepe:v:6:y:2019:i:c:s221471601930096x
    DOI: 10.1016/j.orp.2019.100124
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