Reliable facility location design under disruptions

Li, Qingwei; Zeng, Bo; Savachkin, Alex

doi:10.1016/j.cor.2012.11.012

Cited by 156 publications

(69 citation statements)

References 18 publications

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“…In [Snyder, 2006] one can find an excellent review on Facility Location under uncertainty, describing contributions not only from the stochastic but also from the RO perspective. More recent references to Facility Location under uncertainty include [Snyder and Daskin, 2005, Averbakh, 2005, Snyder and Daskin, 2006, Cui et al, 2010, Shen et al, 2011, Albareda-Sambola et al, 2011, Adjiashvili, 2012, Gao, 2012, Alumur et al, 2012, Albareda-Sambola et al, 2013, Gïlpinar et al, 2013 and [Li et al, 2013].…”

Section: The Rrufl and Previously Proposed Problemsmentioning

confidence: 99%

“…A 2SSO approach for this problem has been considered in [Ravi and Sinha, 2006], where approximation algorithms have been proposed. In [Snyder and Daskin, 2005, Cui et al, 2010, Shen et al, 2011 and [Li et al, 2013], uncertainty has been addressed only in the set of locations (R k = R 0 and A k = A 0 , ∀k ∈ K). As stressed by the authors, this model is suitable for applications where facilities might become unavailable in a second stage due to disruptions caused by natural disasters, terrorists attacks or labor strikes [see Cui et al, 2010].…”

Section: The Rrufl and Previously Proposed Problemsmentioning

confidence: 99%

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Networks, uncertainty, applications and a crusade for optimality

Álvarez-Miranda

2015

4OR-Q J Oper Res

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Section: The Rrufl and Previously Proposed Problemsmentioning

confidence: 99%

Section: The Rrufl and Previously Proposed Problemsmentioning

confidence: 99%

Networks, uncertainty, applications and a crusade for optimality

Álvarez-Miranda

2015

4OR-Q J Oper Res

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“…In [44] one can find an excellent review on Facility Location under uncertainty, describing contributions not only from the stochastic but also from the RO perspective. More recent references to Facility Location under uncertainty include [45,7,46,19,43,2,1,25,4,3,26] and [34].…”

Section: The Rrufl and Previously Proposed Problemsmentioning

confidence: 99%

“…A 2SSO approach for this problem has been considered in [41], where approximation algorithms have been proposed. In [45,19,43] and [34], uncertainty has been addressed only in the set of locations (R k = R 0 and A k = A 0 , ∀k ∈ K). As stressed by the authors, this model is suitable for applications where facilities might become unavailable in a second stage due to disruptions caused by natural disasters, terrorists attacks or labor strikes [see 19].…”

Section: The Rrufl and Previously Proposed Problemsmentioning

confidence: 99%

The recoverable robust facility location problem

Álvarez-Miranda

Fernández

Ljubić

2015

Transportation Research Part B: Methodological

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This work deals with a facility location problem in which location and allocation (transportation) policy is defined in two stages such that a first-stage solution should be robust against the possible realizations (scenarios) of the input data that can only be revealed in a second stage. This solution should be robust enough so that it can be recovered promptly and at low cost in the second stage. In contrast to some related modeling approaches from the literature, this new recoverable robust model is more general in terms of the considered data uncertainty; it can address situations in which uncertainty may be present in any of the following four categories: provider-side uncertainty, receiver-side uncertainty, uncertainty in-between, and uncertainty with respect to the cost parameters.For this novel problem, a sophisticated branch-and-cut framework based on Benders decomposition is designed and complemented by several non-trivial enhancements, including scenario sorting, dual lifting, branching priorities, matheuristics and zero-half cuts. Two large sets of instances that incorporate spatial and demographic information of countries such as Germany and US (transportation) and Bangladesh and the Philippines (disaster management) are introduced. They are used to analyze in detail the characteristics of the proposed model and the obtained solutions as well as the effectiveness, behavior and limitations of the designed algorithm.

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“…In this case, the most critical failures need to be identified in order to take them into account. A frequent extension to these models is to consider the possibility of fortifying some of the located facilities (see Li et al [22]). …”

Section: Introductionmentioning

confidence: 99%

The reliable p -median problem with at-facility service

Albareda-Sambola

Hinojosa

Puerto

2015

European Journal of Operational Research

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This paper deals with a discrete facility location model where service is provided at the facility sites. It is assumed that facilities can fail and customers do not have information on failures before reaching them. As a consequence, they may need to visit more than one facility, following an optimized search scheme, in order to get service. The goal of the problem is to locate p facilities in order to minimize the expected total travel cost. The paper presents two alternative mathematical programming formulations for this problem and proposes a matheuristic based on a network flow model to provide solutions to it. The computational burden of the presented formulations is tested and compared on a test-bed of instances.

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Reliable facility location design under disruptions

Cited by 156 publications

References 18 publications

Networks, uncertainty, applications and a crusade for optimality

Networks, uncertainty, applications and a crusade for optimality

The recoverable robust facility location problem

The reliable p -median problem with at-facility service

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