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Relevant bibliographies by topics / Facility location

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Academic literature on the topic 'Facility location'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2024

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Journal articles on the topic "Facility location"

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Hsia, Hao-Ching, Hiroaki Ishii, and Kuang-Yih Yeh. "AMBULANCE SERVICE FACILITY LOCATION PROBLEM." Journal of the Operations Research Society of Japan 52, no. 3 (2009): 339–54. http://dx.doi.org/10.15807/jorsj.52.339.

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Gao, Xuehong, Chanseok Park, Xiaopeng Chen, En Xie, Guozhong Huang, and Dingli Zhang. "Globally Optimal Facility Locations for Continuous-Space Facility Location Problems." Applied Sciences 11, no. 16 (August 9, 2021): 7321. http://dx.doi.org/10.3390/app11167321.

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The continuous-space single- and multi-facility location problem has attracted much attention in previous studies. This study focuses on determining the globally optimal facility locations for two- and higher-dimensional continuous-space facility location problems when the Manhattan distance is considered. Before we propose the exact method, we start with the continuous-space single-facility location problem and obtain the global minimizer for the problem using a statistical approach. Then, an exact method is developed to determine the globally optimal solution for the two- and higher-dimensional continuous-space facility location problem, which is different from the previous clustering algorithms. Based on the newly investigated properties of the minimizer, we extend it to multi-facility problems and transfer the continuous-space facility location problem to the discrete-space location problem. To illustrate the effectiveness and efficiency of the proposed method, several instances from a benchmark are provided to compare the performances of different methods, which illustrates the superiority of the proposed exact method in the decision-making of the continuous-space facility location problems.

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Moya-Martínez, Alejandro, Mercedes Landete, and Juan Francisco Monge. "Close-Enough Facility Location." Mathematics 9, no. 6 (March 21, 2021): 670. http://dx.doi.org/10.3390/math9060670.

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This paper introduces the concept of close-enough in the context of facility location. It is assumed that customers are willing to move from their homes to close-enough pickup locations. Given that the number of pickup locations is expanding every day, it is assumed that pickup locations can be placed everywhere. Conversely, the set of potential location for opening facilities is discrete as well as the set of customers. Opening facilities and pickup points entails an installation budget and a distribution cost to transport goods from facilities to customers and pickup locations. The (p,t)-Close-Enough Facility Location Problem is the problem of deciding where to locate p facilities among the finite set of candidates, where to locate t pickup points in the plane and how to allocate customers to facilities or to pickup points so that all the demand is satisfied and the total cost is minimized. In this paper, it is proved that the set of initial infinite number of pickup locations is finite in practice. Two mixed-integer linear programming models are proposed for the discrete problem. The models are enhanced with valid inequalities and a branch and price algorithm is designed for the most promising model. The findings of a comprehensive computational study reveal the performance of the different models and the branch and price algorithm and illustrate the value of pickup locations.

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Goldman, A. J. "Optimal Facility-Location." Journal of Research of the National Institute of Standards and Technology 111, no. 2 (March 2006): 97. http://dx.doi.org/10.6028/jres.111.008.

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Buckley, Fred. "Facility Location Problems." College Mathematics Journal 18, no. 1 (January 1987): 24. http://dx.doi.org/10.2307/2686313.

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Buckley, Fred. "Facility Location Problems." College Mathematics Journal 18, no. 1 (January 1987): 24–32. http://dx.doi.org/10.1080/07468342.1987.11973002.

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Friesz, Terry L. "Competitive Facility Location." Networks and Spatial Economics 7, no. 1 (January 17, 2007): 1–2. http://dx.doi.org/10.1007/s11067-006-9008-1.

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Carrizosa, Emilio, and Stefan Nickel. "Robust facility location." Mathematical Methods of Operations Research (ZOR) 58, no. 2 (November 1, 2003): 331–49. http://dx.doi.org/10.1007/s001860300294.

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Degener, Bastian, Joachim Gehweiler, and Christiane Lammersen. "Kinetic Facility Location." Algorithmica 57, no. 3 (November 11, 2008): 562–84. http://dx.doi.org/10.1007/s00453-008-9250-7.

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Svitkina, Zoya, and ÉVA Tardos. "Facility location with hierarchical facility costs." ACM Transactions on Algorithms 6, no. 2 (March 2010): 1–22. http://dx.doi.org/10.1145/1721837.1721853.

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Dissertations / Theses on the topic "Facility location"

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Thangavelu, Balajee. "Single-Facility location problem among two-dimensional existing facility locations." Ohio University / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1175283985.

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Jackson, Leroy A. "Facility location using cross decomposition /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1996. http://handle.dtic.mil/100.2/ADA320150.

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Thesis (M.S. in Operations Research) Naval Postgraduate School, December 1995.
Thesis advisor(s): Robert F. Dell. "December 1995." Includes bibliographical references (p. 51-52). Also available online.

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Jackson, Leroy A. "Facility Location Using Cross Decomposition." Thesis, Monterey, California. Naval Postgraduate School, 1995. http://hdl.handle.net/10945/30739.

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The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government.
Determining the best base stationing for military units can be modeled as a capacitated facility location problem with sole sourcing and multiple resource categories. Computational experience suggests that cross decomposition, a unification of Benders Decomposition and Lagrangean relaxation, is superior to other contemporary methods for solving capacitated facility location problems. Recent research extends cross decomposition to pure integer prograrnming problems with explicit application to capacitated facility location problems with sole sourcing; however, this research offers no computational experience. This thesis implements two cross decomposition algorithms for the capacitated facility location problem with sole sourcing and compares these decomposition algorithms with branch and bound methods. For some problems tested, cross decomposition obtains better solutions in less time; however, cross decomposition does not always perform better man branch and bound due to the time required to obtain the cross decomposition bound that is theoretically superior to other decomposition bounds.

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Gul, Evren. "Robust Facility Location With Mobile Customers." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613341/index.pdf.

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In this thesis, we study the dynamic facility location problem with mobile customers considering the permanent facilities. Our general aim is to locate facilities considering the movements of customers in time. The problem is studied for three objectives: P-median, P-center and MINMAX P-median. We show that dynamic facility location problem is a large instance of a static facility location problem for P-median and P-center objectives. In the problem, we represent the movements of each customer in time with a time series. Using clustering approaches, we develop a heuristic approach for the problem with P-median objective. K-means algorithm is used as a clustering algorithm and dynamic time warping is used in order to define similarities between the customer time series. Solution method is tested on several experimental settings. We obtain results, which differ at most 2% from the optimal, in small computation times. Generally, in the literature, MINMAX P-median is solved with a heuristic depending on scenarios planning (see Serra and Marianov, 1998). The heuristic finds an initial solution according to scenarios, later the initial solution is tried to be improved. We provide a bounding procedure on the solution of the problem. The bounds can be used by decision maker to judge the solution quality before proceed. The bounding procedure is also analyzed in different experimental settings.

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Chardaire, P. "Facility location optimization and cooperative games." Thesis, University of East Anglia, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267523.

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On April 27, 1802, I gave a shout of joy ... It was seven years ago I proposed to myself a problem which I have not been able to solve directly, but for which I had found by chance a solution, and I knew that it was correct, without being able to prove it. The matter often returned to my mind and I had sought twenty times unsuccessfully for this solution. For some days I had carried the idea about with me continually. At last, I do not know how, I found it, together with a large number of curious and new considerations concerning the theory of probability. Andre Marie Ampere. Facility location problems (or plant location problems) are general models that can be used when a set of clients has to be served by facilities. More precisely, we are given a set of potential facility locations and a set of clients. The optimization problem is to select a subset of the locations at which to place facilities and then to assign clients to theses facilities so as to minimize total cost. Most formulations considered in this thesis can be viewed as general models that can be applied to a wide range of context and practical situations. However, as this research has been partly initiated by the interest of the author in telecommunication network design we will introduce these models by considering problems in this particular area. In the context of telecommunication network design an application of discrete location theory is the optimization of access networks with concentrators. Typically, we have a number of terminal points that must be connected to a service point. An obvious solution is to use a dedicated link for each terminal (star network). However, it is clear that this solution can be very expensive when the number of terminals is large and when they are far from the service point. Access networks are often constructed by inserting concentrators between the terminals and the service point. Many terminals are connected to a facility which in turn is connected by a single link to the service point. The objective is to build a network that will provide the service at minimum cost. If no extra constraints are involved the mmimum cost network problem can be expressed as an uncapacitated facility location problem (UFL). If the number of terminals that can be connected to a concentrator is limited we obtain a so-called capacitated facility location problem (CFL). CFL can be extended to consider various types of concentrators with various capacities. This problem is the multi-capacitated facility location problem (MCFL). MCFL is a straightforward model for low speed packet switched data networks typical among which are networks connecting sellingpoint terminals to a database. For other networks, the problem may involve various traffic constraints. In chapter 1 we present those problems and compare solutions obtained by Lagrangian relaxation and simulated annealing algorithms. The architecture mentioned above can be extended with more than one hierarchical level of concentrator. Unfortunately, we pay for this cost saving through a decrease of reliability. Therefore, the number of levels is often limited to one or two. In chapter 2 we study an extension of UFL and CFL to two levels of concentrators. Obviously, the structure of a network changes according to the way requirements vary with time. In order to plan investments and to develop strategies, the evolution of a network has to be determined for several years ahead (typically four or five years). In this case the main questions to answer are: Where and when to establish concentrators and of what size? In chapter 3 we study this problem for the dynamic version of UFL. Now, with the network optimization problem, there naturally arises the problem of allocating the total minimum cost among customers fairly. Namely, we would like to allocate the cost in such a way that no subgroup of users would have incentive to withdraw and build their own network. The standard way to approach such a problem is by the means of cooperative game theory. In chapter 4 we study the core of location games derived from UFL and CFL, and in chapter 5 we propose methods to compute the nucleolus of these games.

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Nadirler, Deniz. "Undesirable And Semi-desirable Facility Location Problems." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605244/index.pdf.

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In this thesis, single undesirable and semi-desirable facility location problems are analyzed in a continuous planar region considering the interaction between the facility and the existing demand points. In both problems, the distance between the facility and the demand points is measured with the rectilinear metric. The aim in the first part where the location of a pure undesirable facility is considered, is to maximize the distance of the facility from the closest demand point. In the second part, where the location of a semi-desirable facility is considered, a conflicting objective measuring the service cost of the facility is added to the problem of the first part. For the solution of the first problem, a mixed integer programming model is used. In order to increase the solution efficiency of the model, new branch and bound strategies and bounding schemes are suggested. In addition, a geometrical method is presented which is based on upper and lower bounds. For the biobjective problem, a three-phase interactive geometrical branch and bound algorithm is suggested to find the most preferred efficient solution.

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Wang, Qingda. "Facility location constrained to a simple polygon." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq61034.pdf.

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Myung, Young-soo, and Dong-wan Tcha. "Return on Investment Analysis for Facility Location." Massachusetts Institute of Technology, Operations Research Center, 1991. http://hdl.handle.net/1721.1/5306.

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We consider how the optimal decision can be made if the optimality criterion of maximizing profit changes to that of maximizing return on investment for the general uncapacitated facility location problem. We show that the inherent structure of the proposed model can be exploited to make a significant computational reduction.

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Zhang, Jingru. "Geometric Facility Location Problems on Uncertain Data." DigitalCommons@USU, 2017. https://digitalcommons.usu.edu/etd/6337.

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Facility location, as an important topic in computer science and operations research, is concerned with placing facilities for "serving" demand points (each representing a customer) to minimize the (service) cost. In the real world, data is often associated with uncertainty because of measurement inaccuracy, sampling discrepancy, outdated data sources, resource limitation, etc. Hence, problems on uncertain data have attracted much attention. In this dissertation, we mainly study a classical facility location problem: the k- center problem and several of its variations, on uncertain points each of which has multiple locations that follow a probability density function (pdf). We develop efficient algorithms for solving these problems. Since these problems more or less have certain geometric flavor, computational geometry techniques are utilized to help develop the algorithms. In particular, we first study the k-center problem on uncertain points on a line, which is aimed to find k centers on the line to minimize the maximum expected distance from all uncertain points to their expected closest centers. We develop efficient algorithms for both the continuous case where the location of every uncertain point follows a continuous piecewise-uniform pdf and the discrete case where each uncertain point has multiple discrete locations each associated with a probability. The time complexities of our algorithms are nearly linear and match those for the same problem on deterministic points. Then, we consider the one-center problem (i.e., k= 1) on a tree, where each uncertain point has multiple locations in the tree and we want to compute a center in the tree to minimize the maximum expected distance from it to all uncertain points. We solve the problem in linear time by proposing a new algorithmic scheme, called the refined prune-and-search. Next, we consider the one-dimensional one-center problem of uncertain points with continuous pdfs, and the one-center problem in the plane under the rectilinear metric for uncertain points with discrete locations. We solve both problems in linear time, again by using the refined prune-and-search technique. In addition, we study the k-center problem on uncertain points in a tree. We present an efficient algorithm for the problem by proposing a new tree decomposition and developing several data structures. The tree decomposition and these data structures may be interesting in their own right. Finally, we consider the line-constrained k-center problem on deterministic points in the plane where the centers are required to be located on a given line. Several distance metrics including L1, L2, and L1 are considered. We also study the line-constrained k-median and k-means problems in the plane. These problems have been studied before. Based on geometric observations, we design new algorithms that improve the previous work. The algorithms and techniques we developed in this dissertation may and other applications as well, in particular, on solving other related problems on uncertain data.

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Das, Pali. "Solutions of some single facility location problems." Thesis, University of North Bengal, 1997. http://hdl.handle.net/123456789/600.

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Books on the topic "Facility location"

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Zanjirani Farahani, Reza, and Masoud Hekmatfar, eds. Facility Location. Heidelberg: Physica-Verlag HD, 2009. http://dx.doi.org/10.1007/978-3-7908-2151-2.

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Drezner, Zvi, ed. Facility Location. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-5355-6.

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Drezner, Zvi, and Horst W. Hamacher, eds. Facility Location. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56082-8.

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Saldanha-da-Gama, Francisco, and Shuming Wang. Facility Location Under Uncertainty. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-55927-3.

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Karakitsiou, Athanasia. Modeling Discrete Competitive Facility Location. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21341-5.

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Miller, Tan C., Terry L. Friesz, and Roger L. Tobin. Equilibrium Facility Location on Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03280-0.

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Zvi, Drezner, and Hamacher Horst, eds. Facility location: Applications and theory. Berlin: Springer, 2002.

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Rosenfield, Donald B. The retailer facility location problem. Cambridge, Mass: Massachusetts Institute of Technology, 1985.

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W, Hamacher Horst, and Drezner Zvi, eds. Facility location: Applications and theory. Berlin: Springer, 2001.

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Miller, Tan C. Equilibrium facility location on networks. Berlin: Springer Verlag, 1996.

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Book chapters on the topic "Facility location"

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Drezner, Zvi, Kathrin Klamroth, Anita Schöbel, and George O. Wesolowsky. "The Weber Problem." In Facility Location, 1–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56082-8_1.

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ReVelle, Charles, and Justin C. Williams. "Reserve Design and Facility Siting." In Facility Location, 307–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56082-8_10.

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Berman, Oded, and Dmitry Krass. "Facility Location Problems with Stochastic Demands and Congestion." In Facility Location, 329–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56082-8_11.

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Campbell, James F., Andreas T. Ernst, and Mohan Krishnamoorthy. "Hub Location Problems." In Facility Location, 373–407. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56082-8_12.

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Karch, Oliver, Hartmut Noltemeier, and Thomas Wahl. "Location and Robotics." In Facility Location, 409–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56082-8_13.

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Rendl, Franz. "The Quadratic Assignment Problem." In Facility Location, 439–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56082-8_14.

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Plastria, Frank. "Continuous Covering Location Problems." In Facility Location, 37–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56082-8_2.

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Current, John, Mark Daskin, and David Schilling. "Discrete Network Location Models." In Facility Location, 81–118. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56082-8_3.

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Marianov, Vladimir, and Daniel Serra. "Location Problems in the Public Sector." In Facility Location, 119–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56082-8_4.

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Drezner, Tammy, and H. A. Eiselt. "Consumers in Competitive Location Models." In Facility Location, 151–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56082-8_5.

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Conference papers on the topic "Facility location"

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Moscibroda, Thomas, and Roger Wattenhofer. "Facility location." In the twenty-fourth annual ACM SIGACT-SIGOPS symposium. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1073814.1073834.

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Meyerson, A. "Online facility location." In Proceedings 42nd IEEE Symposium on Foundations of Computer Science. IEEE, 2001. http://dx.doi.org/10.1109/sfcs.2001.959917.

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Yilmaz, Emre, Sanem Elbasi, and Hakan Ferhatosmanoglu. "Predicting Optimal Facility Location without Customer Locations." In KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3097983.3098198.

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Svitkina, Zoya, and Éva Tardos. "Facility location with hierarchical facility costs." In the seventeenth annual ACM-SIAM symposium. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1109557.1109576.

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Meyerson, Adam. "Profit-earning facility location." In the thirty-third annual ACM symposium. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/380752.380756.

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Bespamyatnikh, S., B. Bhattacharya, D. Kirkpatrick, and M. Segal. "Mobile facility location (extended abstract)." In the 4th international workshop. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/345848.345858.

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Feldman, Michal, Amos Fiat, and Iddan Golomb. "On Voting and Facility Location." In EC '16: ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2940716.2940725.

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Weng, Kerui. "Distance Constrained Facility Location Problem." In 2009 IITA International Conference on Services Science, Management and Engineering (SSME). IEEE, 2009. http://dx.doi.org/10.1109/ssme.2009.66.

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Krogmann, Simon. "Two-Sided Facility Location Games." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/963.

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Facility location problems have been studied in settings like hospital placement or the competition between stores. In some cases, a central authority coordinates facility placements to optimize metrics like the coverage of an area or emergency response time. In many cases, however, facilities are placed by multiple rational agents to maximize their utility, e.g., the number of clients they attract. In previous research, these games feature simplistic client behavior independent of other clients' strategic choices, e.g., visiting the closest facility. Our goal is to understand what happens if clients also act selfishly, resulting in a two-stage game consisting of strategic facility and client agents. In three recent publications, we investigated such two-stage models for clients that optimize their waiting times. We showed the existence and gave algorithms for (approximate) subgame perfect equilibria, a common extension of Nash equilibria for sequential games. To learn more about this domain, we intend to investigate further natural client behaviors and eventually create a more general model or hierarchy of two-sided facility location games. With this, we aim to make predictions in real-world settings, e.g., the placement of renewable energy infrastructure.

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Goh, Shen-Tat, Shudong Liu, Tracy Yong, and Eric Foo. "Scenario Analysis with Facility Location Optimization." In TENCON 2018 - 2018 IEEE Region 10 Conference. IEEE, 2018. http://dx.doi.org/10.1109/tencon.2018.8650398.

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Reports on the topic "Facility location"

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REYNOLDS, J. A. Fast Flux Test Facility Asbestos Location Tracking Program. Office of Scientific and Technical Information (OSTI), April 1999. http://dx.doi.org/10.2172/781692.

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Klise, Katherine, and Michael Bynum. Facility Location Optimization Model for COVID-19 Resources. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1617839.

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Bingamon, Brian. Location of HENC 1 TA-63 TRU Waste Facility. Office of Scientific and Technical Information (OSTI), November 2020. http://dx.doi.org/10.2172/1716737.

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Flaherty, Julia, and Ernest Antonio. Hanford Waste Treatment Plant LAB Facility Stack Effluent Monitoring - Sampling Probe Location Qualification Evaluation. Office of Scientific and Technical Information (OSTI), July 2021. http://dx.doi.org/10.2172/1814642.

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Flaherty, Julia, and Ernest Antonio. Hanford Waste Treatment Plant LAB Facility Stack Effluent Monitoring - Sampling Probe Location Qualification Evaluation. Office of Scientific and Technical Information (OSTI), February 2022. http://dx.doi.org/10.2172/1872735.

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Flaherty, Julia, Ernest Antonio, Carolyn AM Burns, Richard Daniel, and Jennifer Yao. Hanford Waste Treatment Plant Effluent Management Facility Stack Effluent Monitoring Sampling Probe Location Qualification Evaluation. Office of Scientific and Technical Information (OSTI), April 2022. http://dx.doi.org/10.2172/1880068.

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Flaherty, Julia, Ernest Antonio, Carolyn AM Burns, Richard Daniel, and Jennifer Yao. Hanford Waste Treatment Plant Low Activity Waste Facility Stack Effluent Monitoring - Sampling Probe Location Qualification Evaluation. Office of Scientific and Technical Information (OSTI), April 2022. http://dx.doi.org/10.2172/1872744.

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Flaherty, Julia, Ernest Antonio, Carolyn AM Burns, Richard Daniel, and Jennifer Yao. Hanford Waste Treatment Plant Low Activity Waste Facility Stack Effluent Monitoring: Sampling Probe Location Qualification Evaluation. Office of Scientific and Technical Information (OSTI), February 2024. http://dx.doi.org/10.2172/2348926.

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Puckett. PR-277-103700-R01 Guidelines for Preventing Underground Facility Damage as a Result of HDD. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 2012. http://dx.doi.org/10.55274/r0010450.

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Despite the advances that have been made in the horizontal directional drilling (HDD) industry over the past 40 years, damage to existing underground facilities as a result of HDD operations remains a serious concern. HDD poses a unique risk of damage to underground facilities due to the fact that obstructions along the drilled path are seldom evident during construction. HDD operations also have the potential to cause damage even when a seemingly adequate separation distance is maintained between the drilled path and existing facilities. As a result of these issues, damage prevention practices in the HDD industry are becoming increasingly important as facility corridors become more congested. In order to effectively reduce the potential for facility damage resulting from HDD operations, it�s important for those involved in HDD design and construction to understand why facility damage can occur and how it can be prevented. The objective of this report is to present guidelines that will both reduce the potential for damage to existing facilities during HDD operations and improve the accuracy with which the location of completed HDD installations is documented.

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Kelner, Britton, and Sparks. L51986 Natural Gas Sample Collection and Handling Phase II Simulated Field Conditions. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), January 2003. http://dx.doi.org/10.55274/r0011157.

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Abstract:

Phase II was originally planned as a series of field tests to confirm the results of the sampling methods performance tests conducted during Phase I. However, the API Chapter 14.1 Gas Sampling Research Working Group chose to have the tests conducted at a newly developed wet gas test facility located at the Colorado Engineering Experiment Station (CEESI), in Nunn, Colorado. Three general tests were conducted. Test Plan I was intended to investigate the effects of sample point location on on-line gas chromatograph (GC) analyses and on spot sampling methods. Test Plan II was intended to investigate the effects of sample point location on on-line GC analyses and to compare several spot sampling methods when sampling from the same point. Test Plan III was intended to investigate the effects of coupling configurations and cylinder temperature on two specific methods: Helium Pop, and purging - Fill/Empty.

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