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Journal articles on the topic 'Facility location theory'

Author: Grafiati

Published: 11 January 2025

Last updated: 25 July 2025

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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 The

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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 usua

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11

Che-Ani, Adi Irfan, and Roslan Ali. "Facility management demand theory." Journal of Facilities Management 17, no. 4 (2019): 344–55. http://dx.doi.org/10.1108/jfm-09-2018-0057.

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Purpose This study aims to confirm the inverse relationship between scheduled corrective maintenance (SCM) and corrective maintenance (CM) in health-care facility management. That is, the higher the SCM, the lower the demand for CM, and the lower the SCM, the higher the demand for CM. Furthermore, the study shows the importance of SCM as compared with CM in healthcare facilities. Design/methodology/approach This study investigated 28 services in facility engineering services for an exploratory study by using the open-ended approach of the grounded theory. Five years of data with a total of 20,

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12

Bigman, David, and Charles ReVelle. "The Theory of Welfare Considerations in Public Facility Location Problems." Geographical Analysis 10, no. 3 (2010): 229–40. http://dx.doi.org/10.1111/j.1538-4632.1978.tb00652.x.

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13

Bigman, David, and Charles ReVelle. "Welfare Theory, Public Goods, and Public Facility Location: A Reply." Geographical Analysis 11, no. 4 (2010): 389–92. http://dx.doi.org/10.1111/j.1538-4632.1979.tb00704.x.

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14

Lea, Anthony C. "Welfare Theory, Public Goods, and Public Facility Location: A Rejoinder." Geographical Analysis 11, no. 4 (2010): 392–95. http://dx.doi.org/10.1111/j.1538-4632.1979.tb00705.x.

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15

Fotakis, Dimitris, Loukas Kavouras, and Lydia Zakynthinou. "Online Facility Location in Evolving Metrics." Algorithms 14, no. 3 (2021): 73. http://dx.doi.org/10.3390/a14030073.

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The Dynamic Facility Location problem is a generalization of the classic Facility Location problem, in which the distance metric between clients and facilities changes over time. Such metrics that develop as a function of time are usually called “evolving metrics”, thus Dynamic Facility Location can be alternatively interpreted as a Facility Location problem in evolving metrics. The objective in this time-dependent variant is to balance the trade-off between optimizing the classic objective function and the stability of the solution, which is modeled by charging a switching cost when a client’

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16

Aronov, Boris, Marc van Kreveld, Ren� van Oostrum, and Kasturi Varadarajan. "Facility Location on a Polyhedral Surface." Discrete and Computational Geometry 30, no. 3 (2003): 357–72. http://dx.doi.org/10.1007/s00454-003-2769-0.

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17

Allahbakhsh, Mohammad, Saeed Arbabi, Mohammadreza Galavii, Florian Daniel, and Boualem Benatallah. "Crowdsourcing planar facility location allocation problems." Computing 101, no. 3 (2018): 237–61. http://dx.doi.org/10.1007/s00607-018-0670-1.

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18

Kovačić, Danijel, and Marija Bogataj. "Reverse logistics facility location using cyclical model of extended MRP theory." Central European Journal of Operations Research 21, S1 (2012): 41–57. http://dx.doi.org/10.1007/s10100-012-0251-x.

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19

Guha, Sudipto, and Samir Khuller. "Greedy Strikes Back: Improved Facility Location Algorithms." Journal of Algorithms 31, no. 1 (1999): 228–48. http://dx.doi.org/10.1006/jagm.1998.0993.

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20

Cheung, Yam Ki, and Ovidiu Daescu. "Line facility location in weighted regions." Journal of Combinatorial Optimization 22, no. 1 (2009): 52–70. http://dx.doi.org/10.1007/s10878-009-9272-3.

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21

Duer, Przemysław, Stanisław Duer, and Paweł Wrzesień. "Construction of a local location program on the basis of “decision tree”." Bulletin of the Military University of Technology 68, no. 2 (2019): 165–75. http://dx.doi.org/10.5604/01.3001.0013.3009.

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The article presents the problems of building a damage location program in a technical facility based on the theory of the “decision tree”. The basis in such a decision-making process is the functional and diagnostic analysis of the tested technical device. The result of this analysis process is a set of basic (functional) elements with a set of weighting factors assigned to them. An algorithm of fault location is developed in the theory of the “decision tree” in the process of locating faults in the tested vehicle power supply system. Keywords: technical diagnostics, diagnostic reasoning, art

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22

Leng, Kai Jun, and Shu Hong Zhang. "The Application of Fuzzy Theory and Gray-Based Rough Set Theory in the Supplier Selection Decision Making." Applied Mechanics and Materials 26-28 (June 2010): 559–63. http://dx.doi.org/10.4028/www.scientific.net/amm.26-28.559.

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This work presents the combination of fuzzy theory and rough set theory to solve facility location selection problems under the condition of involving different objective/subjective attributes. We try to utilize individual merits for each method and combine it to form a reliable selection of alternative suppliers. An empirical example is illustrated to show the effectiveness of the integrated method. Our results showed that the integrated method can allow decision makers to get the best candidate of supplier location, and is recommended in the practice therefore.

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23

Kari, Majidabad Abbas, Marzieh Mozafari, and Ali Naimi-Sadigh. "Stackelberg-Nash Equilibrium in competitive facility location game among a franchisor and two investors." Modeling in engineering 17, no. 57 (2019): 111–25. https://doi.org/10.22075/JME.2018.15243.1511.

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In the competitive location problems, the matter of the optimal location of single or multiple facilities are in a condition in which competitors exist as well. This paper deals with a type of a competitive location on which a leader possibly uses the investment of other investors through concession and receives a percentage of their income. He also can place his own facilities on potential locations that are available. In fact there are three decision-makers, one as a leader, others as followers who get in the game of facility location for placing their facilities. The location of facilities

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24

Artto, Karlos, Tuomas Ahola, Riikka Kyrö, and Antti Peltokorpi. "Managing business networks for value creation in facilities and their external environments." Facilities 35, no. 1/2 (2017): 99–115. http://dx.doi.org/10.1108/f-07-2015-0049.

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Purpose The purpose of this paper is to increase understanding of the logic of business network formation among the co-located and external actors of a facility. Design/methodology/approach The research adopts a theory-building approach through developing propositions inductively from the empirical case study on four purposefully sampled modern service station facilities. The focus is on analyzing how a facility and its inherent co-located actors represent an entity that forms a business network with external actors in the facility’s environment. Findings The findings propose that when co-loca

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25

DeVerteuil, Geoffrey. "Reconsidering the legacy of urban public facility location theory in human geography." Progress in Human Geography 24, no. 1 (2000): 47–69. http://dx.doi.org/10.1191/030913200668094045.

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26

Wang, Cheng, Zhuo Hu, Ming Xie, and Yuxiang Bian. "Sustainable facility location‐allocation problem under uncertainty." Concurrency and Computation: Practice and Experience 31, no. 9 (2018): e4521. http://dx.doi.org/10.1002/cpe.4521.

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27

Averbakh, Igor, and Sergei Bereg. "Facility location problems with uncertainty on the plane." Discrete Optimization 2, no. 1 (2005): 3–34. http://dx.doi.org/10.1016/j.disopt.2004.12.001.

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28

Hasan, Mohammad Khairul, Hyunwoo Jung, and Kyung-Yong Chwa. "Approximation algorithms for connected facility location problems." Journal of Combinatorial Optimization 16, no. 2 (2008): 155–72. http://dx.doi.org/10.1007/s10878-007-9130-0.

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29

Angel, Eric, Nguyen Kim Thang, and Damien Regnault. "Improved local search for universal facility location." Journal of Combinatorial Optimization 29, no. 1 (2014): 237–46. http://dx.doi.org/10.1007/s10878-014-9711-7.

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30

Zhang, Xiang, David Rey, and S. Travis Waller. "Multitype Recharge Facility Location for Electric Vehicles." Computer-Aided Civil and Infrastructure Engineering 33, no. 11 (2018): 943–65. http://dx.doi.org/10.1111/mice.12379.

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31

Fathali, Jafar. "Grey Median Problem and Vertex Optimality." Statistics, Optimization & Information Computing 11, no. 3 (2023): 670–76. http://dx.doi.org/10.19139/soic-2310-5070-1527.

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The median problem is a basic model in location theory and transportation sciences. This problem deals with locating a facility on a network, to minimize the sum of weighted distances between the facility and the vertices of the network. In this paper, the cases that weights of vertices, edge lengths or both of them are grey numbers, are considered. For all these cases, we show that the set of vertices of network contains a solution of the median problem. This property is called vertex optimality. Median problem with grey parameters and its properties are first considered in this paper.

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32

Grover, Sapna, Neelima Gupta, and Samir Khuller. "LP-based approximation for uniform capacitated facility location problem." Discrete Optimization 45 (August 2022): 100723. http://dx.doi.org/10.1016/j.disopt.2022.100723.

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33

Barahona, Francisco, and Fabián A. Chudak. "Near-optimal solutions to large-scale facility location problems." Discrete Optimization 2, no. 1 (2005): 35–50. http://dx.doi.org/10.1016/j.disopt.2003.03.001.

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34

Dai, Wenqiang, and Xianju Zeng. "Incremental Facility Location Problem and Its Competitive Algorithms." Journal of Combinatorial Optimization 20, no. 3 (2009): 307–20. http://dx.doi.org/10.1007/s10878-009-9219-8.

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35

Aboutahoun, Abdallah, Salem Mahdi, Mahmoud El-Alem та Mohamed ALrashidi. "Modified and Improved Algorithm for Finding a Median Path with a Specific Length (ℓ) for a Tree Network". Mathematics 11, № 16 (2023): 3585. http://dx.doi.org/10.3390/math11163585.

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The median path problem (min-sum criterion) is a common problem in graph theory and tree networks. This problem is open to study because its applications are growing and extending in different fields, such as providing insight for decision-makers when selecting the optimal location for non-emergency services, including railroad lines, highways, pipelines, and transit routes. Also, the min-sum criterion can deal with several networks in different applications. The location problem has traditionally been concerned with the optimal location of a single-point facility at either a vertex or along a

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36

Karim, Rubayet, and Koichi Nakade. "An integrated location-inventory model for a spare part’s supply chain considering facility disruption risk and CO2 emission." Journal of Industrial Engineering and Management 14, no. 2 (2021): 87. http://dx.doi.org/10.3926/jiem.3250.

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Purpose: Managing the inventory of spare parts is very difficult because of the stochastic nature of part’s demand. Also, only controlling the inventory of the spare part is not enough; instead, the supply chain of the spare part needs to be managed efficiently. Moreover, every organization now aims to have a resilient and sustainable supply chain to overcome the risk of facility disruption and to ensure environmental sustainability. This paper thus aims to establish a model of inventory-location relating to the resilient supply chain network of spare parts.Design/methodology/approach: First,

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37

Eyden, Samunderu, and Brose Sven. "Reconfiguring a Multi-period Facility Model – An Empirical Test in a Dynamic Setting." BOHR International Journal of Operations Management Research and Practices 1, no. 1 (2021): 17–27. http://dx.doi.org/10.54646/bijomrp.003.

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Facility location is an important problem faced by companies in many industries. Finding an optimal location for facilities and determining their size involves the consideration of many factors, including proximity to customers and suppliers, availability of skilled employees and support services, and cost-related factors, for example, construction or leasing costs, utility costs, taxes, availability of support services, and others. The demand of the surrounding region plays an important role in location decisions. A high population density may not necessarily cause a proportional demand for p

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38

Samunderu, Eyden, and Otto Hahn Hahn. "Reconfiguring a multi-period facility model—An empirical test in a dynamic setting." BOHR International Journal of Operations Management Research and Practices 1, no. 1 (2022): 17–27. http://dx.doi.org/10.54646/bijomrp.2022.03.

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Facility location is an important problem faced by companies in many industries. Finding an optimal location for facilities and determining their size involves the consideration of many factors, including proximity to customers and suppliers, availability of skilled employees and support services, and cost-related factors, for example, construction or leasing costs, utility costs, taxes, availability of support services, and others. The demand of the surrounding region plays an important role in location decisions. A high population density may not necessarily cause a proportional demand for p

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39

Krivulin, Nikolai K., and Maksim A. Briushinin. "Solving a two-facility location problem in a space with Chebyshev metric." Vestnik of Saint Petersburg University. Mathematics. Mechanics. Astronomy 9, no. 4 (2022): 625–35. http://dx.doi.org/10.21638/spbu01.2022.405.

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A minimax two-facility location problem in multidimensional space with Chebyshev metric is examined subject to box constraints on the feasible location area. In the problem, there are two groups of points with known coordinates, and one needs to find coordinates for optimal location of two new points under the given constraints. The location of the new points is considered optimal if it minimizes the maximum of the following values: the distance between the first new point and the farthest point in the first group, the distance between the second new point and the farthest point in the second

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40

Puerto, Justo, Federica Ricca, and Andrea Scozzari. "Reliability problems in multiple path-shaped facility location on networks." Discrete Optimization 12 (May 2014): 61–72. http://dx.doi.org/10.1016/j.disopt.2014.01.003.

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41

Korupolu, Madhukar R., C. Greg Plaxton, and Rajmohan Rajaraman. "Analysis of a Local Search Heuristic for Facility Location Problems." Journal of Algorithms 37, no. 1 (2000): 146–88. http://dx.doi.org/10.1006/jagm.2000.1100.

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42

Editor, Section, Prince Kusi, Eric Appiah-Twumasi Twumasi, and Professor Darkwah. "LOCATION OF ADDITIONAL LIBRARY FACILITY IN BEREKUM MUNICIPALITY USING BERMAN AND DREZNER ALGORITHM." Journal of Statistics and Actuarial Research 5, no. 1 (2021): 1–20. http://dx.doi.org/10.47604/jsar.1332.

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Purpose: To model location of an additional library facility in the Berekum Municipality as a conditional p-center problem which will serve as a reference centre for Schools and Colleges within the municipality Methodology: The data for this study was the road distance between the suburbs of Berekum Municipality. The suburbs of the municipality were coded and Floyd's algorithm was used to find the distance matrix, d (i, j) for all pairs shortest path. Subsequently, the Researchers used Berman and Drezner's algorithm on 18-nodes network which had two existing library facilities in Berekum and J

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43

Gudmundsson, Joachim, Herman Haverkort, Sang-Min Park, Chan-Su Shin, and Alexander Wolff. "Facility location and the geometric minimum-diameter spanning tree." Computational Geometry 27, no. 1 (2004): 87–106. http://dx.doi.org/10.1016/j.comgeo.2003.07.007.

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44

Benkoczi, Robert, Binay K. Bhattacharya, Sandip Das, and Jeff Sember. "Single facility collection depots location problem in the plane." Computational Geometry 42, no. 5 (2009): 403–18. http://dx.doi.org/10.1016/j.comgeo.2008.04.004.

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45

He, Lei, and Ziang Xie. "Optimization of Urban Shelter Locations Using Bi-Level Multi-Objective Location-Allocation Model." International Journal of Environmental Research and Public Health 19, no. 7 (2022): 4401. http://dx.doi.org/10.3390/ijerph19074401.

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Recently, global natural disasters have occurred frequently and caused serious damage. As an important urban space resource and public service facility, the reasonable planning and layout optimization of shelters is very important to reduce the disaster loss and improve the sustainable development of cities. Based on the review of location theory and models for shelter site selection, this study constructs a bi-level multi-objective location-allocation model, an accessibility, economy, and efficiency (AEE) model, based on sequential decision logic to maximize the economic sustainability and so

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46

Melo, Lucas P., Flávio K. Miyazawa, Lehilton L. C. Pedrosa, and Rafael C. S. Schouery. "Approximation algorithms for k-level stochastic facility location problems." Journal of Combinatorial Optimization 34, no. 1 (2016): 266–78. http://dx.doi.org/10.1007/s10878-016-0064-2.

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47

Han, Lu, Dachuan Xu, Yicheng Xu, and Dongmei Zhang. "Approximating the $$\tau $$-relaxed soft capacitated facility location problem." Journal of Combinatorial Optimization 40, no. 3 (2020): 848–60. http://dx.doi.org/10.1007/s10878-020-00631-y.

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48

Li, Hui, Bo Zhang, and Xiangyu Ge. "Modeling Emergency Logistics Location-Allocation Problem with Uncertain Parameters." Systems 10, no. 2 (2022): 51. http://dx.doi.org/10.3390/systems10020051.

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In order to model the emergency facility location-allocation problem with uncertain parameters, an uncertain multi-objective model is developed within the framework of uncertainty theory. The proposed model minimizes time penalty cost, distribution cost and carbon dioxide emissions. The equivalents of the model are discussed via operational laws of uncertainty distribution. By employing the goal attainment technique, a series of Pareto-optimal solutions are generated that can be used for decision-making. Finally, several numerical experiments are presented to verify the validity of the propose

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49

Camacho-Vallejo, José-Fernando, Álvaro Eduardo Cordero-Franco, and Rosa G. González-Ramírez. "Solving the Bilevel Facility Location Problem under Preferences by a Stackelberg-Evolutionary Algorithm." Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/430243.

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This research highlights the use of game theory to solve the classical problem of the uncapacitated facility location optimization model with customer order preferences through a bilevel approach. The bilevel model provided herein consists of the classical facility location problem and an optimization of the customer preferences, which are the upper and lower level problems, respectively. Also, two reformulations of the bilevel model are presented, reducing it into a mixed-integer single-level problem. An evolutionary algorithm based on the equilibrium in a Stackelberg’s game is proposed to so

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50

Bhattacharya, Anushree, and Madhumangal Pal. "A Fuzzy Graph Theory Approach to the Facility Location Problem: A Case Study in the Indian Banking System." Mathematics 11, no. 13 (2023): 2992. http://dx.doi.org/10.3390/math11132992.

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A fuzzy graph G is stated to have a set of trees as its tree cover if all the vertices of G are in their union. The maximum weight tree in the tree cover is assumed to be the cost of a tree cover for a fuzzy graph. For an integer β>0, finding a set of trees to cover all the vertices of a graph with minimum cost and at most β number of spanning trees is known as the β-tree cover problem. Combining the tree-covering concept and facility location problem in a fuzzy environment for solving critical real-life problems in the recent era is a more fruitful approach. This issue strongly inspires us

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