P-hub protection models for survivable hub network design

Kim, Hyun

doi:10.1007/s10109-011-0157-5

Cited by 37 publications

(24 citation statements)

References 36 publications

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“…It is worth making a comparison between the proposed model and the one which Kim [5] presented. As stated before, the most important di erence between these two models is in the ways they treat network potential ow.…”

Section: Analyzing the Resultsmentioning

confidence: 99%

“…Kim [5] presented a protection model by assuming some back-up hubs which could be applied for transferring tra c in situations where primary hubs were not available. In this paper, let RB n ij show the reliability of the back-up route between origin i and destination j, which passes through back-up hub n (i !…”

Section: Literature Reviewmentioning

confidence: 99%

“…Finally, total probability of the potential tra c delivery rate between the origin and destination node pairs (i; j) by primary route and back-up route can be calculated as presented below [5]:…”

Section: Literature Reviewmentioning

confidence: 99%

“…Since the origin and destination node pairs in a network cannot communicate with each other without hubs, any disruption in hubs can result in whole network failure. Hence, the design of survivable networks has been a signi cant issue in telecommunications [5]. In this paper, the survivability of the networks is concerned.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-objective Fuzzy Goal Programming P-hub Location and Protection Model with Back-up Hubs Considering Hubs Establishment Fixed Costs

2016

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Abstract. The critical and undeniable role of hubs in telecommunication and networking brings about some precautions to be taken to protect networks against any disruption. In this paper, a multi-objective model in a group of hub location problems, referred to as protection models for survivable network design, is developed. In fact, this model is a hub location problem that aims to maximize the potential ow between the origin and destination node pairs in the network, which has the minimum potential ow among all O-D pairs, including multiple assignment and back-up routes. In addition, it concerns the xed cost of installation of the hub facilities. A fuzzy goal programming method is applied to solve the model. Di erent scenarios are implemented using Turkish data set and also numerical experiments are presented to illustrate the advantages of the proposed model in some aspects, compared to previous models. The results can give useful insights into telecommunication network design.

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Section: Analyzing the Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Multi-objective Fuzzy Goal Programming P-hub Location and Protection Model with Back-up Hubs Considering Hubs Establishment Fixed Costs

2016

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“…Kim and O'Kelly (2009) propose a reliable model for hub network where each link has a failure probability, and the model tries to locate hubs and allocate demand nodes to them so that the network has maximum reliability. Later, Kim (2012) proposes a protective model where a backup hub is considered after disruption of the primal link. Similar to the previous research, the protective hub location model aims to identify hub location and their allocation that maximize the total network reliability.…”

Section: Introductionmentioning

confidence: 99%

Scenario-based modeling for multiple allocation hub location problem under disruption risk: multiple cuts Benders decomposition approach

Yahyaei

Bashiri

2017

J Ind Eng Int

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The hub location problem arises in a variety of domains such as transportation and telecommunication systems. In many real-world situations, hub facilities are subject to disruption. This paper deals with the multiple allocation hub location problem in the presence of facilities failure. To model the problem, a two-stage stochastic formulation is developed. In the proposed model, the number of scenarios grows exponentially with the number of facilities. To alleviate this issue, two approaches are applied simultaneously. The first approach is to apply sample average approximation to approximate the two stochastic problem via sampling. Then, by applying the multiple cuts Benders decomposition approach, computational performance is enhanced. Numerical studies show the effective performance of the SAA in terms of optimality gap for small problem instances with numerous scenarios. Moreover, performance of multi-cut Benders decomposition is assessed through comparison with the classic version and the computational results reveal the superiority of the multi-cut approach regarding the computational time and number of iterations.

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