Stochastic Optimization in Telecommunications

Gaivoronski, Alexei A.

doi:10.1007/978-0-387-30165-5_27

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…In Table 6, columns t max , t min , and t avg report the maximum, minimum, and average CPU time (seconds) for solving replications of each instance. Columns Fea.Cut, L .Cut, and B.Cut, respectively, report the number of feasibility cuts (13) and (26) and the optimality cut (28) being added into MP2 when Algorithm 2 terminates. Columns LB and UB provide the sampling-based lower and upper bounds of the optimal objective value; column Gap reports the gaps between the two bounds.…”

Section: Results Of Algorithm 2 For S-rndmentioning

confidence: 99%

“…The validity of cut (26) can be verified given that the righthand side of Eq. (26) is feasible to V (x, D, I) for any x ∈ S x , which is then bounded by L. Cut (26) and cut (13) constitute L 1 (x) ≥ 0 in MP2.…”

Section: Valid Feasibility and Optimality Cutsmentioning

confidence: 97%

“…The theories of stochastic/robust NDP have been substantially applied to optimize performance of a variety of network classes arising in the applications of telecommunication, transportation, and waster distribution . In such contexts, a network designer often faces multi‐commodity flows rather than single‐commodity flow optimization considered in this article.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Single‐commodity stochastic network design under demand and topological uncertainties with insufficient data

Shen¹,

You²,

Ma³

2017

Naval Research Logistics

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Stochastic network design is fundamental to transportation and logistic problems in practice, yet faces new modeling and computational challenges resulted from heterogeneous sources of uncertainties and their unknown distributions given limited data. In this article, we design arcs in a network to optimize the cost of single‐commodity flows under random demand and arc disruptions. We minimize the network design cost plus cost associated with network performance under uncertainty evaluated by two schemes. The first scheme restricts demand and arc capacities in budgeted uncertainty sets and minimizes the worst‐case cost of supply generation and network flows for any possible realizations. The second scheme generates a finite set of samples from statistical information (e.g., moments) of data and minimizes the expected cost of supplies and flows, for which we bound the worst‐case cost using budgeted uncertainty sets. We develop cutting‐plane algorithms for solving the mixed‐integer nonlinear programming reformulations of the problem under the two schemes. We compare the computational efficacy of different approaches and analyze the results by testing diverse instances of random and real‐world networks. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 154–173, 2017

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Section: Results Of Algorithm 2 For S-rndmentioning

confidence: 99%

Section: Valid Feasibility and Optimality Cutsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Single‐commodity stochastic network design under demand and topological uncertainties with insufficient data

Shen¹,

You²,

Ma³

2017

Naval Research Logistics

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“…Industrial projects in high‐tech industries are often characterised by considerable uncertainty that at the same time carry different flexibilities. Moreover, stochastic programming , as a means to implement portfolio analysis (), provides the optimisation models for adequate treatment of uncertainty in the planning of cloud service delivery. Portfolio theory allows optimising a composition of different cloud service bundles and connectivity types (both for different cloud customers segment) at the same time clearly identifying relevant business decisions.…”

Section: Quantitative Analysis Of Cloud Carrier and Brokermentioning

confidence: 99%

Assessment and optimisation of business opportunities for telecom operators in the cloud value network

Nesse

Svaet

Strasunskas

et al. 2013

Trans. Emerging Tel. Tech.

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Cloud computing represents a major change in how resources of information technology are delivered and consumed and will influence the telecom industry, its future services, business offerings and economics of the telecommunication. New revenues potentials from mediating cloud‐based services are however less discussed, as well as the commonly accepted and completed methodology for this purpose is lacking. The objective of this paper is to clearly identify recommended roles for telecommunication companies (telcos) in the cloud value chain and to present a model for assessing and optimising innovative business opportunities. In particular, we show how the portfolio theory of modern investment science supports risk management in operation of telecom company‐based cloud carriers for service brokering.We recommend a combined broker and carrier role to be a preferred cloud business model for a telco. Such combination is expected to represent the best value proposition for competing against cloud providers and ICT integrators. Further quantitative analysis indicates significant business benefits from differentiating and optimising provision of carrier grade and best effort network connectivity types for a given bundle of cloud services. This implies an efficient use of telco assets and satisfies an increasing demand for network performance in the business market.The novelty of the paper is the developed quantitative optimisation model as well as the analytical identification of the preferred telco role in cloud value network. Practical application of the research may improve dimensioning of technological assets, leading to higher cost efficiency and possibly higher degree of customer satisfaction. Copyright © 2013 John Wiley & Sons, Ltd.

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“…Most of these works are interested in network problems. For single‐level stochastic problems, the reader can refer to [16]. A very recent publication concerning stochastic bilevel problems with a knapsack constraint is due to Özaltin et al [28].…”

Section: Introductionmentioning

confidence: 99%

On a stochastic bilevel programming problem

et al. 2011

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In this article, a mixed integer bilevel problem having a probabilistic knapsack constraint in the first level is proposed. The problem formulation is mainly motivated by practical pricing and service provision problems as it can be interpreted as a model for the interaction between a service provider and customers. A discrete probability space is assumed which allows a reformulation of the problem as an equivalent deterministic bilevel problem. The problem is further transformed into a linear bilevel problem, which in turn yields a quadratic optimization problem, namely the global linear complementarity problem. Based on this quadratic problem, a procedure to compute upper bounds on the initial problem by using a Lagrangian relaxation and an iterative linear minmax scheme is proposed. Numerical experiments confirm that the scheme practically converges.

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Stochastic Optimization in Telecommunications

Cited by 7 publications

References 15 publications

Single‐commodity stochastic network design under demand and topological uncertainties with insufficient data

Single‐commodity stochastic network design under demand and topological uncertainties with insufficient data

Assessment and optimisation of business opportunities for telecom operators in the cloud value network

On a stochastic bilevel programming problem

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