Stochastic Second-Order Cone Programming in Mobile Ad-Hoc Networks: Sensitivity to Input Parameters

Francesca Maggioni, Marida Bertocchi, Elisabetta Allevi, Florian A. Potra and Stein Wallace
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Francesca Maggioni: Department of Mathematics, Statistics, Computer Science and Applications, University of Bergamo, Via dei Caniana 2, Bergamo 24127, Italy
Marida Bertocchi: Department of Mathematics, Statistics, Computer Science and Applications, University of Bergamo, Via dei Caniana 2, Bergamo 24127, Italy
Elisabetta Allevi: Department of Quantitative Methods, University of Brescia, Contrada S. Chiara, 50 Brescia 25122, Italy
Florian A. Potra: Department of Mathematics & Statistics, University of Maryland, Baltimore County, U.S.A

Chapter 17 in Stochastic Programming:Applications in Finance, Energy, Planning and Logistics, 2013, pp 467-486 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractIn this paper sensitivity analysis is adopted in order to reveal the role of randomness of a stochastic second-order cone program (Maggioni et al., 2009) for mobile ad-hoc networks starting from the semidefinite stochastic locationaided routing (SLAR) model, described in Ariyawansa and Zhu (2006) and Zhu et al. (2011). The algorithm looks for a destination node and sets up a route by means of the expected zone, the region where the sender node expects to find the destination node and the requested zone defined by the sender node for spreading the route request to the destination node. The movements of the destination node are represented by ellipses scenarios, randomly generated by uniform and normal distributions in a neighborhood of the initial position of the destination node. Sensitivity analysis is performed by considering an increasing number of scenarios, different costs of flooding and latency penalty. Evaluation of Expected Value of Perfect Information EVPI and Value of Stochastic Solution VSS (Maggioni and Wallace, 2010; Birge, 1970) allows us to find the range of values in which it is convenient the deterministic versus the stochastic approach.

Keywords: Stochastic Programming; Optimization with Scenarios; Finance; Energy; Production and Logistics Applications (search for similar items in EconPapers)
Date: 2013
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