Hub network design problem in the presence of disruptions

Parvaresh, F.; Husseini, S. M. Moattar; Golpayegany, S. A. Hashemi; Karimi, Behrooz

doi:10.1007/s10845-012-0717-7

Cited by 59 publications

(30 citation statements)

References 75 publications

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“…First objective function is used in order to minimize means of transportation cost and second objective function is used to maximize cost of disruption imposed on network. Parvaresh et al (2014) develop model presented in 2013 and present it in two-level with three objective functions and add a decision variable with two other constraints to modify solution algorithm. In a newer research, Azizi et al (2014) propose a new formulation with conservation of different theories.…”

Section: Literaturementioning

confidence: 99%

An adaptive large neighborhood search heuristic for solving the reliable multiple allocation hub location problem under hub disruptions

Chaharsooghi¹,

Momayezi²,

Ghaffarinasab³

2017

10.5267/j.ijiec

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The hub location problem (HLP) is one of the strategic planning problems encountered in different contexts such as supply chain management, passenger and cargo transportation industries, and telecommunications. In this paper, we consider a reliable uncapacitated multiple allocation hub location problem under hub disruptions. It is assumed that every open hub facility can fail during its use and in such a case, the customers originally assigned to that hub, are either reassigned to other operational hubs or they do not receive service in which case a penalty must be paid. The problem is modeled as two-stage stochastic program and a metaheuristic algorithm based on the adaptive large neighborhood search (ALNS) is proposed. Extensive computational experiments based on the CAB and TR data sets are conducted. Results show the high efficiency of the proposed solution method.

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Section: Literaturementioning

confidence: 99%

An adaptive large neighborhood search heuristic for solving the reliable multiple allocation hub location problem under hub disruptions

Chaharsooghi¹,

Momayezi²,

Ghaffarinasab³

2017

10.5267/j.ijiec

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“…It is difficult to solve such problems by using traditional quantitative methods [7] and the programming software. In this paper we employ the tabu search [26] based on the Monte Carlo method to solve the model. The basic idea of the solution is to randomly generate a set of nodes, and then the former nodes are treated as the initial hub solution set.…”

Section: Estimations Of the Expected Projection Cost And Resiliencementioning

confidence: 99%

A Stochastic Programming Approach for Resilient Hub Location in Power Projection Network considering Random Hub Failures

Zhang

Hailing

Wang³

2017

Mathematical Problems in Engineering

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Hubs are critical facilities in the power projection network. Due to the uncertainty factors such as terrorism threats, severe weather, and natural disasters, hub facilities may be disrupted randomly, which could lead to excessive cost or loss in practice. One of the most effective ways to withstand and reduce the impact of disruptions is designing more resilient networks. In this paper, a stochastic programming model is employed for the hub location problem in the presence of random hub failures. A heuristic algorithm based on Monte Carlo method and tabu search is put forward to solve the model. The proposed approach is more general if there are numbers of hubs that would fail even with different failure probability. Compared with the benchmark model, the model which takes the factor of stochastic failure of hubs into account can give a more resilient power projection network.

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“…They propose simulated annealing algorithm to solve the bi-level model. In the other research, the same authors propose a multiple objective bi-level programming under intentional disruption (Parvaresh et al 2014). The network designer objectives are to minimize network total transportation cost in usual condition and worst-case transportation costs after r-interdiction.…”

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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Hub network design problem in the presence of disruptions

Cited by 59 publications

References 75 publications

An adaptive large neighborhood search heuristic for solving the reliable multiple allocation hub location problem under hub disruptions

An adaptive large neighborhood search heuristic for solving the reliable multiple allocation hub location problem under hub disruptions

A Stochastic Programming Approach for Resilient Hub Location in Power Projection Network considering Random Hub Failures

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

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