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.
https://doi.org/10.1016/j.orp.2019.100124Cite as:
@article{Roohnavazfar_2019,
doi = {10.1016/j.orp.2019.100124},
url = {https://doi.org/10.1016%2Fj.orp.2019.100124},
year = 2019,
publisher = {Elsevier {BV}},
volume = {6},
pages = {100124},
author = {Mina Roohnavazfar and Daniele Manerba and Juan Carlos De Martin and Roberto Tadei},
title = {Optimal paths in multi-stage stochastic decision networks},
journal = {Operations Research Perspectives}
}