Joint chance-constrained survivable multicommodity network design problem with demand uncertainty

Khodayifar, Salman; Farjaie, Mohammadreza

Volume 10, Issue 3 (11-2024) mmr 2024, 10(3): 33-51 | Back to browse issues page

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Khodayifar S, Farjaie M. Joint chance-constrained survivable multicommodity network design problem with demand uncertainty. mmr 2024; 10 (3) :33-51
URL: http://mmr.khu.ac.ir/article-1-3351-en.html

Joint chance-constrained survivable multicommodity network design problem with demand uncertainty

Salman Khodayifar ^*

¹, Mohammadreza Farjaie²

1- Institute for Advanced Studies in Basic Sciences (IASBS) , s.khodayifar@iasbs.ac.ir
2- Institute for Advanced Studies in Basic Sciences (IASBS)

Abstract: (121 Views)

Survivable Capacitated Networks Design Problem (SCNDP for short) is one of the most essential issues in telecommunication and transportation networks. A survivable network is a network that is designed to remain operational in the event of a component(s) failure(s) (cable cuts, electronic failures on switching centers and so forth). In SCNDP, researchers usually consider survivability with two different ways named diversification and reservation. Diversification consists of dividing the demand of source-sink node pairs into two or more node-disjoint (or arc-disjoint) paths, and in reservation after the failure of a component(s) of the network, part of the demand for node pairs can still be satisfied by rerouting. To implement reservation, the links on the network ought to have enough capacity to support the new flows after the rerouting has been made. ‎Therefore, in this paper, we first present one model for the problem of survivable capacitated network design based on the reservation strategy. In many real-world applications, the observed values are often uncertain, for example random, fuzzy, etc. Therefore, we consider the demand vector (the right hand side values) randomly and then by using joint chance-constrained and probability concepts, obtain the corresponding deterministic model. Then, we propose an approximation optimization approach and use piecewise tangent approximation and piecewise linear methods to obtain the lower and upper bounds for it, respectively. Finally, computational results show the efficiency of the proposed model and approximation methods for relatively large-scale networks.

Keywords: Network design problem, Survivability, Joint chance-constrained programming, Independent random variables.

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