Optimal paths in multi-stage stochastic decision networks

Optimal paths in multi-stage stochastic decision networks

Authors: Roohnavazfar, Mina; Manerba, Daniele; De Martin, Juan Carlos; Tadei, Roberto

Operations Research Perspectives - 2019 Volume 6, Pages 100124

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.2019.100124

Cite 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}
}



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