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Relevant bibliographies by topics / Facility location theory

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Journal articles

Academic literature on the topic 'Facility location theory'

Author: Grafiati

Published: 11 January 2025

Last updated: 12 June 2025

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Journal articles on the topic "Facility location theory"

1

Erkut, Erhan. "Facility location analysis: Theory and applications." European Journal of Operational Research 45, no. 1 (1990): 116–17. http://dx.doi.org/10.1016/0377-2217(90)90165-8.

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2

Deshmukh, Abhijit V. "Facility location and the theory of production." Journal of Manufacturing Systems 11, no. 3 (1992): 224–25. http://dx.doi.org/10.1016/0278-6125(92)90007-3.

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3

Furuta, Takehiro, Mihiro Sasaki, Fumio Ishizaki, Atsuo Suzuki, and Hajime Miyazawa. "A NEW CLUSTERING MODEL OF WIRELESS SENSOR NETWORKS USING FACILITY LOCATION THEORY." Journal of the Operations Research Society of Japan 52, no. 4 (2009): 366–76. http://dx.doi.org/10.15807/jorsj.52.366.

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4

TEITZ, MICHAEL B. "TOWARD A THEORY OF URBAN PUBLIC FACILITY LOCATION." Papers in Regional Science 21, no. 1 (2005): 35–51. http://dx.doi.org/10.1111/j.1435-5597.1968.tb01439.x.

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5

Hudak, Paul F. "Application of facility location theory to groundwater remediation." Applied Geography 14, no. 3 (1994): 232–44. http://dx.doi.org/10.1016/0143-6228(94)90040-x.

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6

Lea, Anthony C. "Welfare Theory, Public Goods, and Public Facility Location." Geographical Analysis 11, no. 3 (2010): 217–39. http://dx.doi.org/10.1111/j.1538-4632.1979.tb00691.x.

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7

Friesz, Terry L., Roger L. Tobin, and Tan Miller. "Existence theory for spatially competitive network facility location models." Annals of Operations Research 18, no. 1 (1989): 267–76. http://dx.doi.org/10.1007/bf02097808.

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8

Şen, Halil, and Mehmet Fatih Demiral. "Hospital Location Selection with Grey System Theory." European Journal of Economics and Business Studies 5, no. 1 (2016): 66. http://dx.doi.org/10.26417/ejes.v5i1.p66-79.

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The facility location selection is one of the most important decisions for investors and entrepreneurs. It is a strategic issue besides often decides the fate of such a facility. In this kind of strategic decisions, decision makers should take into account various objectives and criteria and the process of location selection is inherently complicated. This paper considers the hospital location selection for a new public hospital by using Gray Relational Analysis (GRA) and Analytic Hierarchy Process (AHP). Gray Relational Analysis have been developed based on Grey System Theory. Grey System Theory is an interdisciplinary approach which first quantified by Deng in the early 1980’s as an alternative method in creating the uncertainty have been proposed. The basic idea of emergence is to estimate the behavior of the systems which cannot be overcome by the stochastic or fuzzy methods with limited number of data. In this paper, the weights of criteria have been determined by using Analytic Hierarchic Process, then the grey relational degrees have been calculated for each alternative location.

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9

Li, Wei-Lin, Peng Zhang, and Da-Ming Zhu. "On Constrained Facility Location Problems." Journal of Computer Science and Technology 23, no. 5 (2008): 740–48. http://dx.doi.org/10.1007/s11390-008-9172-5.

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10

Daham, Hajem Ati. "Neutrosophic Discrete Facility Location Problems." International Journal of Neutrosophic Science 19, no. 1 (2022): 29–47. http://dx.doi.org/10.54216/ijns.190102.

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Discrete facility location problems are classified as types of facility location problems, wherein decisions on choosing facilities in specific locations are made to serve the demand points of customers, thus minimizing the total cost. The covering- and median-based problems are the common classified types of discrete facility location problems, which both comprise different classes of discrete problems as reviewed in this research. However, the discrete facility location problems shown in deterministic and known information and data under uncertain, vague, and ambiguous environments have usually been solved using intuitionistic fuzzy approaches. Neutrosophic is recently applied to tackle the uncertainty and ambiguity of information and data. This paper considered solving the discrete facility location problems under the neutrosophic environment, wherein the information of the locations, distances, times, and costs is uncertain. The mathematical models for the main types of neutrosophic discrete facility location problems, which remain unclear till now despite previous related works, are formulated in this study. Numerical examples demonstrated testing of the neutrosophic discrete models and comparison with the optimization solutions obtained from the normal situations.

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