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Journal articles on the topic 'Facility location'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2024

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Divéki, Gabriella, and Csanád Imreh. "Online facility location with facility movements." Central European Journal of Operations Research 19, no. 2 (July 1, 2010): 191–200. http://dx.doi.org/10.1007/s10100-010-0153-8.

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12

Hossain, Safwan, Evi Micha, and Nisarg Shah. "The Surprising Power of Hiding Information in Facility Location." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 2168–75. http://dx.doi.org/10.1609/aaai.v34i02.5592.

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Facility location is the problem of locating a public facility based on the preferences of multiple agents. In the classic framework, where each agent holds a single location on a line and can misreport it, strategyproof mechanisms for choosing the location of the facility are well-understood.We revisit this problem in a more general framework. We assume that each agent may hold several locations on the line with different degrees of importance to the agent. We study mechanisms which elicit the locations of the agents and different levels of information about their importance. Further, in addition to the classic manipulation of misreporting locations, we introduce and study a new manipulation, whereby agents may hide some of their locations. We argue for its novelty in facility location and applicability in practice. Our results provide a complete picture of the power of strategyproof mechanisms eliciting different levels of information and with respect to each type of manipulation. Surprisingly, we show that in some cases hiding locations can be a strictly more powerful manipulation than misreporting locations.

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13

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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14

Tang, Luohao, Cheng Zhu, Zaili Lin, Jianmai Shi, and Weiming Zhang. "Reliable Facility Location Problem with Facility Protection." PLOS ONE 11, no. 9 (September 1, 2016): e0161532. http://dx.doi.org/10.1371/journal.pone.0161532.

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15

Körkel, Manfred. "Discrete facility location with nonlinear facility costs." RAIRO - Operations Research 25, no. 1 (1991): 31–43. http://dx.doi.org/10.1051/ro/1991250100311.

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16

Xu, Xinping, Bo Li, Minming Li, and Lingjie Duan. "Two-facility Location Games with Minimum Distance Requirement." Journal of Artificial Intelligence Research 70 (February 17, 2021): 719–56. http://dx.doi.org/10.1613/jair.1.12319.

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We study the mechanism design problem of a social planner for locating two facilities on a line interval [0, 1], where a set of n strategic agents report their locations and a mechanism determines the locations of the two facilities. We consider the requirement of a minimum distance 0 ≤ d ≤ 1 between the two facilities. Given the two facilities are heterogeneous, we model the cost/utility of an agent as the sum of his distances to both facilities. In the heterogeneous two-facility location game to minimize the social cost, we show that the optimal solution can be computed in polynomial time and prove that carefully choosing one optimal solution as output is strategyproof. We also design a strategyproof mechanism minimizing the maximum cost. Given the two facilities are homogeneous, we model the cost/utility of an agent as his distance to the closer facility. In the homogeneous two-facility location game for minimizing the social cost, we show that any deterministic strategyproof mechanism has unbounded approximation ratio. Moreover, in the obnoxious heterogeneous two-facility location game for maximizing the social utility, we propose new deterministic group strategyproof mechanisms with provable approximation ratios and establish a lower bound (7 − d)/6 for any deterministic strategyproof mechanism. We also design a strategyproof mechanism maximizing the minimum utility. In the obnoxious homogeneous two-facility location game for maximizing the social utility, we propose deterministic group strategyproof mechanisms with provable approximation ratios and establish a lower bound 4/3. Besides, in the two-facility location game with triple-preference, where each facility may be favorable, obnoxious, indifferent for any agent, we further motivate agents to report both their locations and preferences towards the two facilities truthfully, and design a deterministic group strategyproof mechanism with an approximation ratio 4.

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17

Ben-Porat, Omer, and Moshe Tennenholtz. "Multiunit Facility Location Games." Mathematics of Operations Research 44, no. 3 (August 2019): 865–89. http://dx.doi.org/10.1287/moor.2018.0948.

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18

Swamy, Chaitanya, and David B. Shmoys. "Fault-tolerant facility location." ACM Transactions on Algorithms 4, no. 4 (August 2008): 1–27. http://dx.doi.org/10.1145/1383369.1383382.

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19

Mallozzi, Lina. "Noncooperative facility location games." Operations Research Letters 35, no. 2 (March 2007): 151–54. http://dx.doi.org/10.1016/j.orl.2006.03.003.

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20

Kononov, A. V., Yu A. Kochetov, and A. V. Plyasunov. "Competitive facility location models." Computational Mathematics and Mathematical Physics 49, no. 6 (June 2009): 994–1009. http://dx.doi.org/10.1134/s0965542509060086.

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21

Wilhelm, Mickey R., Robert A. Lichtefeld, and Thomas L. Ward. "Computer-aided facility location." Computers & Industrial Engineering 10, no. 3 (January 1986): 215–25. http://dx.doi.org/10.1016/0360-8352(86)90007-0.

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22

Goemans, Michel X., and Martin Skutella. "Cooperative facility location games." Journal of Algorithms 50, no. 2 (February 2004): 194–214. http://dx.doi.org/10.1016/s0196-6774(03)00098-1.

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23

Current, John, and Samuel Ratick. "Introduction: Facility location modeling." Papers in Regional Science 71, no. 3 (July 1992): 193–97. http://dx.doi.org/10.1007/bf01434263.

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24

Svitkina, Zoya. "Lower-bounded facility location." ACM Transactions on Algorithms 6, no. 4 (August 2010): 1–16. http://dx.doi.org/10.1145/1824777.1824789.

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25

Current, John, and Samuel Ratick. "INTRODUCTION: FACILITY LOCATION MODELING." Papers in Regional Science 71, no. 3 (January 14, 2005): 193–97. http://dx.doi.org/10.1111/j.1435-5597.1992.tb01842.x.

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26

Tcha, Dong-wan, Young-soo Myung, and Ki-ho Chung. "Parametric uncapacitated facility location." European Journal of Operational Research 86, no. 3 (November 1995): 469–79. http://dx.doi.org/10.1016/0377-2217(94)00070-s.

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27

Suzuki, Atsuo, and Zvi Drezner. "SOLVING CONSTRAINED TWO-FACILITY LOCATION PROBLEMS(ISOLDE XII)." Journal of the Operations Research Society of Japan 56, no. 3 (2013): 157–65. http://dx.doi.org/10.15807/jorsj.56.157.

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28

Fofou, Raoul Fonkoua, Zhigang Jiang, Qingshan Gong, and Yihua Yang. "A Decision-Making Model for Remanufacturing Facility Location in Underdeveloped Countries: A Capacitated Facility Location Problem Approach." Sustainability 14, no. 22 (November 16, 2022): 15204. http://dx.doi.org/10.3390/su142215204.

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Underdeveloped countries are gradually opening remanufacturing facilities to recover end-of-life products (EOL). Locating these facilities in underdeveloped countries is quite challenging because many factors related to the environment, economics, and ethics have to be considered. This paper proposes a decision-making model for locating remanufacturing facilities, a critical factor in implementing remanufacturing in underdeveloped countries. Our principal objective is to obtain the capacity, number, and geographical locations for newly established remanufacturing facilities using a Capacitated Facility Location Problem (CFLP) approach. The mathematical model helps us find the number of facilities that will need to be opened to fully recover the EOL products and the total cost during the entire process. A case study on the establishment of SEVALO Remanufacturing Machinery Co., Ltd. in Cameroon is used to demonstrate the CFLP approach. The results and analyses show that the successful establishment of SEVALO in Cameroon will significantly help to reduce the quantity of construction machinery parts dumped into the environment.

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29

Zhang, Min, Jun Huang, and Jian‐ming Zhu. "Reliable facility location problem considering facility failure scenarios." Kybernetes 41, no. 10 (October 12, 2012): 1440–61. http://dx.doi.org/10.1108/03684921211276666.

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30

Jang, Chunhui, and Heesoon Jang. "A Study on the Influence of Feng Shui Factors on the Intention of Tenants to Renew Contracts in Multi-Auction History Commercial Facility: Focusing on the Seoul Commercial Facility." Residential Environment Institute Of Korea 22, no. 1 (March 30, 2024): 141–58. http://dx.doi.org/10.22313/reik.2022.22.1.141.

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A Study on the Interpretation of Feng shui Factors in the Commercial Facility Location Analysis - Focused on the Multiple Auctions in Jung-gu, Seoul -Using the multiple auctions in Jung-gu, Seoul as a focal point, this study aims to analyze the interpretation of environmental factors in the location analysis of commercial facilities. The research focuses on the study of commercial facility locations, specifically in terms of their relationship with environmental factors.The research methodology includes the following steps: 1. Data Collection - Collect data related to commercial facility locations in Jung-gu, Seoul, particularly those obtained through multiple auctions. - Gather data on environmental factors such as geographic characteristics, natural environment (landscape, water, mountains, etc.), and geographical location. 2. Data Preprocessing - Preprocess the collected data to transform it into a suitable format for analysis - Cleanse the data and extract relevant variables for the study. 3. Environmental Factors Analysis: - Utilize Geographic Information Systems (GIS) to analyze the environmental factors related to commercial facility locations. - Conduct spatial analysis to identify patterns and relationships between commercial facilities and environmental factors. 4. Interpretation and Evaluation - Interpret the results of the analysis to gain insights into the interpretation of environmental factors in cmmercial facility location analysis - Evaluate the significance and impact of environmental factors on commercial facility locations in Jung-gu, Seoul. 5. Conclusion and Recommendations:Summarize the findings and draw conclusions based on the interpretation of environmental factors in commercial facility location analysis - Provide recommendations for future decision-making processes regarding commercial facility locations, taking into account the identified environmental factors. By conducting this study, we aim to contribute to the understanding and interpretation of environmental factors in the location analysis of commercial facilities, specifically focusing on the multiple auctions in Jung-gu, Seoul.

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31

Jang, Chunhui, and Heesoon Jang. "A Study on the Influence of Feng Shui Factors on the Intention of Tenants to Renew Contracts in Multi-Auction History Commercial Facility: Focusing on the Seoul Commercial Facility." Residential Environment Institute Of Korea 22, no. 1 (March 30, 2024): 141–58. http://dx.doi.org/10.22313/reik.2024.22.1.141.

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A Study on the Interpretation of Feng shui Factors in the Commercial Facility Location Analysis - Focused on the Multiple Auctions in Jung-gu, Seoul -Using the multiple auctions in Jung-gu, Seoul as a focal point, this study aims to analyze the interpretation of environmental factors in the location analysis of commercial facilities. The research focuses on the study of commercial facility locations, specifically in terms of their relationship with environmental factors.The research methodology includes the following steps: 1. Data Collection - Collect data related to commercial facility locations in Jung-gu, Seoul, particularly those obtained through multiple auctions. - Gather data on environmental factors such as geographic characteristics, natural environment (landscape, water, mountains, etc.), and geographical location. 2. Data Preprocessing - Preprocess the collected data to transform it into a suitable format for analysis - Cleanse the data and extract relevant variables for the study. 3. Environmental Factors Analysis: - Utilize Geographic Information Systems (GIS) to analyze the environmental factors related to commercial facility locations. - Conduct spatial analysis to identify patterns and relationships between commercial facilities and environmental factors. 4. Interpretation and Evaluation - Interpret the results of the analysis to gain insights into the interpretation of environmental factors in cmmercial facility location analysis - Evaluate the significance and impact of environmental factors on commercial facility locations in Jung-gu, Seoul. 5. Conclusion and Recommendations:Summarize the findings and draw conclusions based on the interpretation of environmental factors in commercial facility location analysis - Provide recommendations for future decision-making processes regarding commercial facility locations, taking into account the identified environmental factors. By conducting this study, we aim to contribute to the understanding and interpretation of environmental factors in the location analysis of commercial facilities, specifically focusing on the multiple auctions in Jung-gu, Seoul.

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32

Galvão, Roberto Diéguez. "Uncapacitated facility location problems: contributions." Pesquisa Operacional 24, no. 1 (April 2004): 7–38. http://dx.doi.org/10.1590/s0101-74382004000100003.

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The objective of the present paper is to review my personal contributions in the field of uncapacitated facility location problems. These contributions took place throughout my academic career, from the time I was a Ph.D. student at Imperial College to the present day. They cover approximately 30 years, from 1973 to 2003; they address: algorithms developed for the p-median problem and for a general formulation of uncapacitated location problems; the study of dynamic location models; covering and hierarchical location problems; queuing-based probabilistic location models. The contributions encompass theoretical developments, computational algorithms and practical applications. All work took place in an academic environment, with the invaluable collaboration of colleagues (both in Brazil and abroad) and research students at COPPE. Each section in the paper is dedicated to a topic that involves a personal contribution. Every one of them is placed within the context of the existing literature.

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33

Cheng, Chun, Yossiri Adulyasak, and Louis-Martin Rousseau. "Robust Facility Location Under Disruptions." INFORMS Journal on Optimization 3, no. 3 (July 2021): 298–314. http://dx.doi.org/10.1287/ijoo.2021.0054.

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Facility networks can be disrupted by, for example, power outages, poor weather conditions, or natural disasters, and the probabilities of these events may be difficult to estimate. This could lead to costly recourse decisions because customers cannot be served by the planned facilities. In this paper, we study a fixed-charge location problem (FLP) that considers disruption risks. We adopt a two-stage robust optimization method, by which facility location decisions are made here and now and recourse decisions to reassign customers are made after the uncertainty information on the facility availability has been revealed. We implement a column-and-constraint generation (C&CG) algorithm to solve the robust models exactly. Instead of relying on dualization or reformulation techniques to deal with the subproblem, as is common in the literature, we use a linear programming–based enumeration method that allows us to take into account a discrete uncertainty set of facility failures. This also gives the flexibility to tackle cases when the dualization technique cannot be applied to the subproblem. We further develop an approximation scheme for instances of a realistic size. Numerical experiments show that the proposed C&CG algorithm outperforms existing methods for both the robust FLP and the robust p-median problem.

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34

Hansen, Pierre, Dominique Peeters, and Jacques-François Thisse. "Facility Location Under Zone Pricing." Journal of Regional Science 37, no. 1 (February 1997): 1–22. http://dx.doi.org/10.1111/0022-4146.00040.

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35

Tamir, Arie. "Obnoxious Facility Location on Graphs." SIAM Journal on Discrete Mathematics 4, no. 4 (November 1991): 550–67. http://dx.doi.org/10.1137/0404048.

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36

Bertsimas, Dimitris J. "Traveling Salesman Facility Location Problems." Transportation Science 23, no. 3 (August 1989): 184–91. http://dx.doi.org/10.1287/trsc.23.3.184.

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37

Berman, Oded, and Edward H. Kaplan. "Equity Maximizing Facility Location Schemes." Transportation Science 24, no. 2 (May 1990): 137–44. http://dx.doi.org/10.1287/trsc.24.2.137.

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38

Owen, Susan Hesse, and Mark S. Daskin. "Strategic facility location: A review." European Journal of Operational Research 111, no. 3 (December 1998): 423–47. http://dx.doi.org/10.1016/s0377-2217(98)00186-6.

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39

Datta, S. "A DSS for Facility Location." Paradigm 3, no. 2 (July 1999): 12–17. http://dx.doi.org/10.1177/0971890719990203.

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40

Drezner, Tammy. "Equilibrium facility location on networks." Location Science 5, no. 2 (August 1997): 131. http://dx.doi.org/10.1016/s0966-8349(98)00013-8.

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41

Serafino, Paolo, and Carmine Ventre. "Heterogeneous facility location without money." Theoretical Computer Science 636 (July 2016): 27–46. http://dx.doi.org/10.1016/j.tcs.2016.04.033.

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42

Mulligan, Gordon F. "Equality measures and facility location." Papers in Regional Science 70, no. 4 (October 1991): 345–65. http://dx.doi.org/10.1007/bf01434593.

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43

Aboolian, Robert, Oded Berman, and Dmitry Krass. "Optimizing facility location and design." European Journal of Operational Research 289, no. 1 (February 2021): 31–43. http://dx.doi.org/10.1016/j.ejor.2020.06.044.

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44

Drezner, Tammy, Zvi Drezner, and Pawel Kalczynski. "Gradual cover competitive facility location." OR Spectrum 42, no. 2 (March 18, 2020): 333–54. http://dx.doi.org/10.1007/s00291-020-00585-x.

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45

Li, Yu, and Da-chuan Xu. "Soft-capacitated facility location game." Acta Mathematicae Applicatae Sinica, English Series 26, no. 1 (March 23, 2009): 93–98. http://dx.doi.org/10.1007/s10255-008-8111-0.

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46

Ortiz-Astorquiza, Camilo, Ivan Contreras, and Gilbert Laporte. "Multi-level facility location problems." European Journal of Operational Research 267, no. 3 (June 2018): 791–805. http://dx.doi.org/10.1016/j.ejor.2017.10.019.

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47

Tucker, Joanne M., and Gary R. Armstrong. "Introducing International Issues: Facility Location." Decision Sciences Journal of Innovative Education 6, no. 2 (July 2008): 297–303. http://dx.doi.org/10.1111/j.1540-4609.2008.00175.x.

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48

Balcik, B., and B. M. Beamon. "Facility location in humanitarian relief." International Journal of Logistics Research and Applications 11, no. 2 (February 13, 2008): 101–21. http://dx.doi.org/10.1080/13675560701561789.

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49

Holik, William. "Maintenance Facility Location Optimization Review." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 1 (January 2019): 289–99. http://dx.doi.org/10.1177/0361198118821674.

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State departments of transportation (DOTs) are responsible for operating and maintaining the highway system in a safe and efficient manner to facilitate the movement of people and goods. Maintenance facilities play a critical role in the operations of DOTs by storing equipment and materials, providing a base of operation for department of transportation personnel, and serving as the beginning and ending location for maintenance activities. Maintenance activities occur year-round, but winter maintenance activities are typically the most time and resource intensive for northern state DOTs. Maintenance operations generally consist of facilities, routes, and equipment. The life spans of maintenance facilities are typically measured in decades, which compounds any inefficiencies in their locations over a long time horizon. Less research has been conducted to optimize facility locations than routes and fleets. This paper reviews the available literature related to facility optimization, including maintenance facilities and non-maintenance facilities. Several specific takeaways were documented from the research reviewed. DOTs have conducted research to optimize the locations of their facilities. These works have typically examined a small area and included only a few facilities. The larger-scale optimizations only located facilities at the town level. A second level of facilities may be necessary to improve the cost effectiveness of the optimization. Such facilities could include material storage depots strategically placed between maintenance sections to reduce the amount of deadheading. At a certain point, further consolidation of maintenance facilities will increase the overall costs because more vehicles and staff are required to reach the further extents of maintenance areas.

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50

Mulligan, Gordon F. "EQUALITY MEASURES AND FACILITY LOCATION." Papers in Regional Science 70, no. 4 (January 14, 2005): 345–65. http://dx.doi.org/10.1111/j.1435-5597.1991.tb01737.x.

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