Specially Structured Uncapacitated Facility Location Problems

Jones, Philip C.; Lowe, Timothy J.; Müller, Georg; Xu, Ning; Ye, Yinyu; Zydiak, James L.

doi:10.1287/opre.43.4.661

Cited by 40 publications

(15 citation statements)

References 19 publications

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“…When this can be done, Kolen shows that this covering problem can be solved in polynomial time, see also Kolen and Tamir (1990). Jones et al (1995) identified another class of uncapacitated facility location problems, where not every instance fits the Kolen framework but that can still be solved to optimality in polynomial time. An instance is in this class if facility and demand point indices can be ordered so that the following holds: (a) Continuity: If j, ∈P i , then k ∈P i where j < k << .…”

Section: Special Cases Of the Uncapacitated Facility Location Problemmentioning

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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Section: Special Cases Of the Uncapacitated Facility Location Problemmentioning

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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“…Since that time numerous articles have been published that deal with the properties and solution of the mathematical model, e.g., see the surveys in Krarup and Pruzan (1983), Labbé et al (1995), Labbé and Louveaux (1997), and the books by Mirchandani and Francis (1990), Francis et al (1992), and Daskin (1995). Despite the inherent assumptions of the model that may limit its practicality, the SPLP has gained considerable importance as a basic model in several combinatorial problems dealing, e.g., with vehicle dispatching, set covering, set partitioning, assortment, and, more recently, information retrieval and data mining (Pentico 1976(Pentico , 1988Jones et al 1995;Tripathy et al 1999). The SPLP is also used in multicriteria extensions ReVelle 1998, 2000;Myung et al 1997), and is shown to be an embedded problem in a number of types of location problems (ReVelle and Laporte 1996).…”

Section: Introductionmentioning

confidence: 99%

Primal-Dual Variable Neighborhood Search for the Simple Plant-Location Problem

Hansen

Brimberg

Urošević

et al. 2007

INFORMS Journal on Computing

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T he variable neighborhood search metaheuristic is applied to the primal simple plant-location problem and to a reduced dual obtained by exploiting the complementary slackness conditions. This leads to (i) heuristic resolution of (metric) instances with uniform fixed costs, up to n = 15 000 users, and m = n potential locations for facilities with an error not exceeding 0.04%; (ii) exact solution of such instances with up to m = n = 7 000; and (iii) exact solutions of instances with variable fixed costs and up to m = n = 15 000.

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“…For example, the minimization of the sum of the weighted priorities of satisfiable data requests (based on their deadlines) is used as the optimization criterion in the mathematical model of a basic data staging problem presented in Section 3. But for the facility location problem, in general, researchers adopt optimization criteria that are related to the physical distances between plants and facilities in either a continuous or discrete domain without any prioritizing schemes or deadline related factors (e.g., [4,6,9,10,14]). Thus, although lessons can be drawn from the design of algorithms for different versions of the facility location problem, there are no obvious direct correlations between either the formulations or the potential solutions of those two problems.…”

Section: Related Workmentioning

confidence: 99%

“…For each requested data item [7], M [8], and M [9], respectively. For V [2] (corresponding to M [2]), Drq[0,2] = {3} corresponding to the request for δ[0] from M [6].…”

Section: For Allmentioning

confidence: 99%

“…For V [2] (corresponding to M [2]), Drq[0,2] = {3} corresponding to the request for δ[0] from M [6]. Similarly, for the example shown in Figure 2(b), Drq [1,3] = {0} corresponding to the request of δ [1] from M [8], Drq [1,5] = {1} corresponding to the request of δ [1] from M [9], and Drq [1,4] = Drq [1,7] = ∅. [3] simultaneously due to network conflict for the example shown in Figure 2).…”

Section: For Allmentioning

confidence: 99%

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