文章基本信息

标题：Optimal paths in multi-stage stochastic decision networks
本地全文：下载
作者：Mina Roohnavazfar ; Daniele Manerba ; Juan Carlos De Martin 等
期刊名称：Operations Research Perspectives
印刷版ISSN：2214-7160
电子版ISSN：2214-7160
出版年度：2019
卷号：6
页码：1-10
DOI：10.1016/j.orp.2019.100124
语种：English
出版社：Elsevier
摘要：Highlights•We study the optimal path problem over a multi-stage stochastic network.•Stochastic parameters follow an unknown probability distribution.•We propose a deterministic approximation based on recent advances in the extreme values theory.•We derive feasible solutions from a Nested Multinomial Logit model.•We demonstrate accuracy and efficiency of the approach against the expected value problem.AbstractThis 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.
关键词：KeywordsOptimal pathsStochastic decision processMulti-stageAsymptotic approximationNested Multinomial Logit