Locating Facilities in the Presence of Disruptions and Incomplete Information*

Berman, Oded; Krass, Dmitry; Menezes, Mozart B.C.

doi:10.1111/j.1540-5915.2009.00253.x

Cited by 65 publications

(29 citation statements)

References 14 publications

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“…Snyder and Daskin (2005) study facility location in which some of cases with definite probability become unusable and assume that customers would be served by facilities which are not affected by disruption. Berman et al (2009) and Shen et al (2011) who are inspired by this model developed new location problem models with disruption consideration. They supposed that facilities are not completely reliable and customers do not have any information about a facility being operational or not and it is supposed that every facility may be non-operation by a definite probability.…”

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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“…Drezner (1987) addressed a similar problem and suggested a heuristic. Lee (2001) The problem without the advanced information on the status of the facilities was introduced by Berman et al (2009). They focused on studying the effects of reliability and information on the properties of the optimal solution.…”

Section: Unreliable Facilitiesmentioning

confidence: 99%

“…They focused on studying the effects of reliability and information on the properties of the optimal solution. When the failure of facilities to receive customers for service is due to congestion at the facilities, Berman et al (2007a) considered the problem studied by the earlier Berman et al (2009) using queuing approximations. We note that both of these models assume that customers' search strategy consists of always traveling to the closest unexamined facility, which is not necessarily an optimal strategy.…”

Section: Unreliable Facilitiesmentioning

confidence: 99%

Hub Location Problems: The Location of Interacting Facilities

Kara

Taner

2011

International Series in Operations Research &Amp; Management Science

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), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. PrefaceThis book is the final result of a number of incidents that occurred to us while discussing location issues with colleagues at conferences. Frequently we attended presentations, in which the authors quoted the well-known references that helped to make the discipline what it is today. Upon further inquiry, though, it turned out that some of these colleagues had never actually read the original papers. We then discussed among ourselves which contributions could be credited with shaping the field. And, lo and behold, we found that we, too, had neglected to read some of the papers that form the foundation of our science. Whether it was laziness or other things that got in the way, it had become clear that something had to be done. Our first thought was to collect the original contributions (once we could agree on what they were) and reprint them. When discussing this possibility with a publisher, we immediately ran into a roadblock in the form of copyright. While this appeared to have stopped our enthusiastic effort dead in its tracks, we kept on collecting and reading what we considered original contributions.This went on until we met Camille Price, who suggested that, rather than reprinting the original contributions, we should invite some of the leaders in our field and ask them to describe the original contribution, explain and interpret it, and comment on the impact that it had to the field. This was, of course, an excellent idea, and the response by our colleagues to our pertinent requests was equally enthusiastic. What you hold in your hands is the result of this effort.In other words, the purpose of this book is to provide easy access to the main contributions to location theory. The book is organized as follows. The introductory chapter provides an overview of some of the many facets of location analysis. This is followed by contributions in the main three fields of inquiry: minisum, minimax, and covering problems. The next chapters are part of an ever-growing list of nonstandard location models: models including competitive components, those that locate undesirable facilities, those with probabilistic features, and those that allow interactions between facilities. The following chapters discuss solution techniques: after a discussion of exact and heuristic techniques, we devote an entire chapter to Weiszfeld's method, and another to Lagrangean techniques. The last chapters of this book deal with the spheres of influence that the facilities generate and that attract viii customers to them, an...

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“…In these cases, it is reasonable to expect that, after finding their facility unavailable, customers go directly to a new facility from there, instead of traveling back home and choosing their next closest facility for being served. The models considering this assumption are referred to as facility location with incomplete information (see, Berman et al [10]). …”

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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Locating Facilities in the Presence of Disruptions and Incomplete Information*

Cited by 65 publications

References 14 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

Hub Location Problems: The Location of Interacting Facilities

The reliable p -median problem with at-facility service

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