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Journal articles on the topic 'Facility location theory'

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Published: 11 January 2025

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1

Erkut, Erhan. "Facility location analysis: Theory and applications." European Journal of Operational Research 45, no. 1 (March 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 (January 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 (January 14, 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 (July 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 (September 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 (December 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 (August 30, 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 (September 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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11

Che-Ani, Adi Irfan, and Roslan Ali. "Facility management demand theory." Journal of Facilities Management 17, no. 4 (September 2, 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 20,480 SCM work orders and 84,837 CM work orders were extracted from the central management information system database. Data were analyzed using the Statistical Package for the Social Sciences program. Data were presented in the form of mathematical scores using descriptive statistics and correlation test to elaborate the variable characteristics and make conclusions. Findings This study provides empirical insights about the effectiveness of proactive maintenance in reducing breakdowns for systems or equipment in health-care facilities. Findings suggest that increasing SCM will reduce CM demands. Research limitations/implications The location approach, with restrictions to the comparison between CM and SCM, still allows for exploration, especially on the factors that can reduce the demand for correction. These factors include planned preventive maintenance, work flow process, level of competency of maintenance workers and health-care maintenance strategic planning. Practical implications Proactive maintenance is important in preventing dangerous occurrences in hospitals. Reducing breakdowns increases customer satisfaction. Therefore, this study shows implications to health-care maintenance organizations in the context of business strategic development. Originality/value Data are crucial in proving a hypothesis. This study confirms the evidence of facility management demand theory and highlights the inverse relationship between SCM and CM.
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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 (September 3, 2010): 229–40. http://dx.doi.org/10.1111/j.1538-4632.1978.tb00652.x.

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Bigman, David, and Charles ReVelle. "Welfare Theory, Public Goods, and Public Facility Location: A Reply." Geographical Analysis 11, no. 4 (September 3, 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 (September 3, 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 (February 25, 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’s assignment changes from one facility to another. In this paper, we study the online variant of Dynamic Facility Location. We present a randomized O(logm+logn)-competitive algorithm, where m is the number of facilities and n is the number of clients. In the first step, our algorithm produces a fractional solution, in each timestep, to the objective of Dynamic Facility Location involving a regularization function. This step is an adaptation of the generic algorithm proposed by Buchbinder et al. in their work “Competitive Analysis via Regularization.” Then, our algorithm rounds the fractional solution of this timestep to an integral one with the use of exponential clocks. We complement our result by proving a lower bound of Ω(m) for deterministic algorithms and lower bound of Ω(logm) for randomized algorithms. To the best of our knowledge, these are the first results for the online variant of the Dynamic Facility Location problem.
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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 (September 1, 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 (October 20, 2018): 237–61. http://dx.doi.org/10.1007/s00607-018-0670-1.

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18

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

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19

Cheung, Yam Ki, and Ovidiu Daescu. "Line facility location in weighted regions." Journal of Combinatorial Optimization 22, no. 1 (October 27, 2009): 52–70. http://dx.doi.org/10.1007/s10878-009-9272-3.

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20

Kovačić, Danijel, and Marija Bogataj. "Reverse logistics facility location using cyclical model of extended MRP theory." Central European Journal of Operations Research 21, S1 (July 1, 2012): 41–57. http://dx.doi.org/10.1007/s10100-012-0251-x.

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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 (June 28, 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, artificial intelligence.
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22

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 (April 29, 2018): e4521. http://dx.doi.org/10.1002/cpe.4521.

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23

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

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24

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

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25

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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26

Hasan, Mohammad Khairul, Hyunwoo Jung, and Kyung-Yong Chwa. "Approximation algorithms for connected facility location problems." Journal of Combinatorial Optimization 16, no. 2 (January 3, 2008): 155–72. http://dx.doi.org/10.1007/s10878-007-9130-0.

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27

Angel, Eric, Nguyen Kim Thang, and Damien Regnault. "Improved local search for universal facility location." Journal of Combinatorial Optimization 29, no. 1 (February 12, 2014): 237–46. http://dx.doi.org/10.1007/s10878-014-9711-7.

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28

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 (February 7, 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-located with a large number of actors, the facility and its actors represent an entity that is connected to a wide business network of multiple external actors. On the other hand, when co-located with a small number of actors, the facility becomes a part of the overall supply in the surrounding business environment with a differentiated offering for competitive advantage. Practical implications The research suggests that an appropriate co-locating strategy, for example, when planning the tenant mix of the facility, can contribute to creating a vivid business network in the external environment, which raises the facility to a role of a central entity in such a network. Originality/value The findings explaining how co-location affects the businesses within the facility and within a wider networked environment are novel to the scholarly knowledge on co-location. The research bridges the theories of co-location and business networks that have been treated as separate discourses in previous research.
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29

Zhang, Xiang, David Rey, and S. Travis Waller. "Multitype Recharge Facility Location for Electric Vehicles." Computer-Aided Civil and Infrastructure Engineering 33, no. 11 (June 6, 2018): 943–65. http://dx.doi.org/10.1111/mice.12379.

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30

Fathali, Jafar. "Grey Median Problem and Vertex Optimality." Statistics, Optimization & Information Computing 11, no. 3 (January 18, 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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31

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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32

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

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33

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

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34

Aboutahoun, Abdallah, Salem Mahdi, Mahmoud El-Alem, and Mohamed ALrashidi. "Modified and Improved Algorithm for Finding a Median Path with a Specific Length (ℓ) for a Tree Network." Mathematics 11, no. 16 (August 18, 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 an edge in a network. Recently, numerous investigators have investigated this classic problem and have studied the location of many facilities, such as paths, trees, and cycles. The concept of the median, which measures the centrality of a vertex in a graph, is extended to the paths in a graph. In this paper, we consider the problem of locating path-shaped facilities on a tree network. A new modified and improved algorithm for finding a median single path facility of a specified length in a tree network is proposed. The median criterion for optimality considers the sum of the distances from all vertices of the tree to the path facility. This problem under the median criterion is called the ℓ-core problem. The distance between any two vertices in the tree is equal to the length of the unique path connecting them. This location problem usually has applications in distributed database systems, pipelines, the design of public transportation routes, and communication networks.
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35

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 products or services. The demography of a region could dictate the demand for products, and this, in turn, affects a facility’s size and location. The location of a company’s competitors also affects the location of that company’s facilities. Another important aspect in facility location modeling is that many models focus on current demand and do not adequately consider future demand. However, while making location decisions in an industry in decline, carefully and accurately considering future demand is especially important, and the question in focus is whether to shrink or close down certain facilities with the objective of keeping a certain market share or maximizing profit, especially in a competitive environment.<br /> This paper develops a model which enables companies to select sites for their businesses according to their strategy. The model analyzes the strategic position of the company and forms a guideline for the decision. It investigates which facilities should be closed, (re)opened, shrunk, or expanded. If facilities are to shrink or expand, the model also determines their new capacities. It depicts the impact on market share and accounts for the costs of closure and reopening. A number of papers deal with location theory and its applications, but few have been written for modeling a competitive environment in the case of declining demand. Existing papers in this area of research are mostly static in nature, do not offer multi-period approaches, nor do they incorporate the behavior of competitors in the market. To demonstrate the validity of the model, it is first solved using a small problem set – three facilities, three demand locations, and three periods – in LINGO solver. To get a better understanding of the model’s behavior, several additional scenarios were constructed. First, the number of demand locations was extended to 10. Our findings show that the model presented provides an extension of existing facility location models that can be applied to a variety of location problems in commercial and industry sectors that need to make their decisions considering future periods and competitors. The developed heuristic shows multiple options for solving the problem, including their advantages and disadvantages, respectively. The Java code and LINGO fragments thus developed can be used to provide easy access to related problems.
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36

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 products or services. The demography of a region could dictate the demand for products, and this, in turn, affects a facility’s size and location. The location of a company’s competitors also affects the location of that company’s facilities. Another important aspect in facility location modeling is that many models focus on current demand and do not adequately consider future demand. However, while making location decisions in an industry in decline, carefully and accurately considering future demand is especially important, and the question in focus is whether to shrink or close down certain facilities with the objective of keeping a certain market share or maximizing profit, especially in a competitive environment. This paper develops a model which enables companies to select sites for their businesses according to their strategy. The model analyzes the strategic position of the company and forms a guideline for the decision. It investigates which facilities should be closed, (re)opened, shrunk, or expanded. If facilities are to shrink or expand, the model also determines their new capacities. It depicts the impact on market share and accounts for the costs of closure and reopening. A number of papers deal with location theory and its applications, but few have been written for modeling a competitive environment in the case of declining demand. Existing papers in this area of research are mostly static in nature, do not offer multi-period approaches, nor do they incorporate the behavior of competitors in the market. To demonstrate the validity of the model, it is first solved using a small problem set–three facilities, three demand locations, and three periods–in LINGO solver. To get a better understanding of the model’s behavior, several additional scenarios were constructed. First, the number of demand locations was extended to 10. Our findings show that the model presented provides an extension of existing facility location models that can be applied to a variety of location problems in commercial and industry sectors that need to make their decisions considering future periods and competitors. The developed heuristic shows multiple options for solving the problem, including their advantages and disadvantages, respectively. The Java code and LINGO fragments thus developed can be used to provide easy access to related problems.
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37

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 (February 5, 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, applying queuing theory, a location-inventory model for a spare parts supply chain facing a facility disruption risk and has a restriction for CO2 emission, is developed. The model is later formulated as a non-linear mixed-integer programming problem and is solved using MATLAB.Findings: The model gives optimal decisions about the location of the warehouse facility and the policy of inventory management of each location selected. The sensitivity analysis shows that the very low probability of facility disruption does not influence controlling the average emission level. However, the average emission level certainly decreases with the increment of the disruption probability when the facility disruption probability is significant.Practical implications: Using this model, based on the cost and emission parameters and the likelihood of facility disruption, the spare part’s manufacturer can optimize the total average cost of the spare part’s supply chain through making a trade-off between productions, warehouse selection, inventory warehousing and demand allocation.Originality/value: Previous research focuses only on developing a framework for designing an efficient spare parts planning and control system. The inventory-location model for spare parts is not addressed in the sense of risk of facilities disturbance and emission. This research first time jointly considered the probabilistic facility disruption risk and carbon emission for modeling the spare part’s supply chain network.
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38

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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39

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 (October 2000): 146–88. http://dx.doi.org/10.1006/jagm.2000.1100.

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40

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 (August 8, 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 Jininjini to locate additional Library facility for Library users in Berekum Municipality. Matlab program software was used for the coding of the Floyd-Warshall algorithm. The codes for Floyd-Warshall algorithm was developed and ran on DellAMD Athlon (tm) II P360 Dual-Core Processor 2.30GHz of RAM 3.00GB, 64-bit Operating System with Windows Ultimate Laptop Computer Results: The analysis of the study revealed that, an additional library facility using Berman and Drezner (2008) should be located at Akrofro with an objective function value of 8. The results obtained from the study is useful to locate a public library facility that will benefit all the people in Berekum municipality. Specifically, the results revealed the new public library facility to be built in Berekum Municipality should be sited at Akrofro. Unique contribution to theory, practice and policy: The implication of the results is that the minimum distance travelled by the farthest library user to the new library facility at Akrofro is 8 kilometres. Stakeholders should adopt the use of Berman and Drezner's algorithms in establishing facilities such as markets, hospitals, recreational centres and so on
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41

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 (January 2004): 87–106. http://dx.doi.org/10.1016/j.comgeo.2003.07.007.

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42

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

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43

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 group, and the distance between the first and second new points. The location problem is formulated as a multidimensional optimization problem in terms of tropical mathematics that studies the theory and applications of algebraic systems with idempotent operations. A direct analytical solution to the problem is derived based on the use of methods and results of tropical optimization. A result is obtained which describes the set of optimal location of the new points in a parametric form ready for formal analysis of solutions and straightforward calculation.
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44

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 (August 12, 2016): 266–78. http://dx.doi.org/10.1007/s10878-016-0064-2.

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45

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 (August 1, 2020): 848–60. http://dx.doi.org/10.1007/s10878-020-00631-y.

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46

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 (April 6, 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 social utility. The model comprehensively considers factors such as the level of decision-making, the utilization efficiency, and capacity constraints of shelters. The gravity model is introduced to simulate the decision-making behavior of evacuees. A calculation example and its solution prove the high practicability and operability of the AEE model in an actual shelter site selection and construction investment, which can achieve the global optimization of evacuation time and the maximization of the use efficiency of the shelters under the financial constraints. It provides a scientific and effective decision-making method for the multi-objective location optimization problem of shelters.
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47

Li, Hui, Bo Zhang, and Xiangyu Ge. "Modeling Emergency Logistics Location-Allocation Problem with Uncertain Parameters." Systems 10, no. 2 (April 17, 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 proposed model and to illustrate decision-making strategy.
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48

Anagnostopoulos, Aris, Russell Bent, Eli Upfal, and Pascal Van Hentenryck. "A simple and deterministic competitive algorithm for online facility location." Information and Computation 194, no. 2 (November 2004): 175–202. http://dx.doi.org/10.1016/j.ic.2004.06.002.

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49

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 (July 4, 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 to develop a model with a practical algorithm. This paper provides an algorithm and complexity analysis to determine the number of rooted trees s covering the given fuzzy graph. In addition, a model is constructed with three optimization programming problems in the facility location problem and a tree covering fuzzy graphs. The model includes two types of the facility location problem, simultaneously addressing a variable covering radius and a fixed covering radius. A numerical example is provided to further describe the model, then, in the application part of the paper, the proposed model is applied to solve the real-life problem of maximizing demand saturation by minimizing the number of small denominations in the Indian banking system. This problem involves the data input of different indicators in the banking system along with details of the denominations of banknotes.
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50

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 solve the bilevel model. Numerical experimentation is performed in this study and the results are compared to benchmarks from the existing literature on the subject in order to emphasize the benefits of the proposed approach in terms of solution quality and estimation time.
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