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Articles de revues sur le sujet « Discrete location »

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Auteur : Grafiati
Publié le 1 juin 2024

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1

Salhi, Said, Pitu B. Mirchandani et Richard L. Francis. « Discrete Location Theory ». Journal of the Operational Research Society 42, no 12 (décembre 1991) : 1124. http://dx.doi.org/10.2307/2582961.

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2

Salhi, Said. « Discrete Location Theory ». Journal of the Operational Research Society 42, no 12 (décembre 1991) : 1124–25. http://dx.doi.org/10.1057/jors.1991.208.

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3

Sarker, Bhaba R. « Discrete location theory ». European Journal of Operational Research 52, no 3 (juin 1991) : 388–89. http://dx.doi.org/10.1016/0377-2217(91)90179-y.

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4

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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5

Louveaux, F. V. « Discrete stochastic location models ». Annals of Operations Research 6, no 2 (février 1986) : 21–34. http://dx.doi.org/10.1007/bf02027380.

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6

Abdullah, Makola M. « Optimal Location and Gains of Feedback Controllers at Discrete Locations ». AIAA Journal 36, no 11 (novembre 1998) : 2109–16. http://dx.doi.org/10.2514/2.314.

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Abdullah, Makola M. « Optimal location and gains of feedback controllers at discrete locations ». AIAA Journal 36 (janvier 1998) : 2109–16. http://dx.doi.org/10.2514/3.14092.

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8

Herger, Nils, et Steve McCorriston. « On discrete location choice models ». Economics Letters 120, no 2 (août 2013) : 288–91. http://dx.doi.org/10.1016/j.econlet.2013.04.015.

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9

Tuy, Hoang, Michel Minoux et N. T. Hoai-Phuong. « Discrete Monotonic Optimization with Application to a Discrete Location Problem ». SIAM Journal on Optimization 17, no 1 (janvier 2006) : 78–97. http://dx.doi.org/10.1137/04060932x.

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10

Vasilyev, I. L., et A. V. Ushakov. « Discrete Facility Location in Machine Learning ». Journal of Applied and Industrial Mathematics 15, no 4 (novembre 2021) : 686–710. http://dx.doi.org/10.1134/s1990478921040128.

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11

Vasilyev, I. L., et A. V. Ushakov. « Discrete facility location in machine learning ». Diskretnyi analiz i issledovanie operatsii 28, no 4 (29 novembre 2021) : 5–60. http://dx.doi.org/10.33048/daio.2021.28.714.

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12

Marín, Alfredo. « Discrete location for bundled demand points ». TOP 18, no 1 (2 mai 2009) : 242–56. http://dx.doi.org/10.1007/s11750-009-0097-0.

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13

Cánovas, Lázaro, Alfredo Marín et Mercedes Landete. « Extreme points of discrete location polyhedra ». Top 9, no 1 (juin 2001) : 115–38. http://dx.doi.org/10.1007/bf02579074.

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14

Mirchandani, P. B., et J. M. Reilly. « “Spatial nodes” in discrete location problems ». Annals of Operations Research 6, no 7 (juillet 1986) : 201–22. http://dx.doi.org/10.1007/bf02024583.

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15

Erkut, Erhan, Thomas Baptie et Balder von Hohenbalken. « The discrete p-Maxian location problem ». Computers & ; Operations Research 17, no 1 (janvier 1990) : 51–61. http://dx.doi.org/10.1016/0305-0548(90)90027-5.

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16

Moya-Martínez, Alejandro, Mercedes Landete et Juan Francisco Monge. « Close-Enough Facility Location ». Mathematics 9, no 6 (21 mars 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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17

Brown, Colin G., et Ross G. Drynan. « PLANT LOCATION ANALYSIS USING DISCRETE STOCHASTIC PROGRAMMING1* ». Australian Journal of Agricultural Economics 30, no 1 (avril 1986) : 1–22. http://dx.doi.org/10.1111/j.1467-8489.1986.tb00449.x.

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18

MELACHRINOUDIS, EMANUEL. « A DISCRETE LOCATION ASSIGNMENT PROBLEM WITH CONGESTION ». IIE Transactions 26, no 6 (novembre 1994) : 83–89. http://dx.doi.org/10.1080/07408179408966641.

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19

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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20

Krarup, Jakob, David Pisinger et Frank Plastria. « Discrete location problems with push–pull objectives ». Discrete Applied Mathematics 123, no 1-3 (novembre 2002) : 363–78. http://dx.doi.org/10.1016/s0166-218x(01)00346-8.

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21

Espejo, Inmaculada, Alfredo Marín et Antonio M. Rodríguez-Chía. « Closest assignment constraints in discrete location problems ». European Journal of Operational Research 219, no 1 (mai 2012) : 49–58. http://dx.doi.org/10.1016/j.ejor.2011.12.002.

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22

Campbell, James F. « Continuous and discrete demand hub location problems ». Transportation Research Part B : Methodological 27, no 6 (décembre 1993) : 473–82. http://dx.doi.org/10.1016/0191-2615(93)90018-6.

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23

Berman, Oded, et Jiamin Wang. « Probabilistic location problems with discrete demand weights ». Networks 44, no 1 (2004) : 47–57. http://dx.doi.org/10.1002/net.20015.

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24

Kanellopoulos, Panagiotis, Alexandros A. Voudouris et Rongsen Zhang. « On Discrete Truthful Heterogeneous Two-Facility Location ». SIAM Journal on Discrete Mathematics 37, no 2 (6 juin 2023) : 779–99. http://dx.doi.org/10.1137/22m149908x.

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25

Plastria, Frank, et Lieselot Vanhaverbeke. « Discrete models for competitive location with foresight ». Computers & ; Operations Research 35, no 3 (mars 2008) : 683–700. http://dx.doi.org/10.1016/j.cor.2006.05.006.

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26

Lančinskas, Algirdas, Julius Žilinskas, Pascual Fernández et Blas Pelegrín. « Solution of asymmetric discrete competitive facility location problems using ranking of candidate locations ». Soft Computing 24, no 23 (29 juin 2020) : 17705–13. http://dx.doi.org/10.1007/s00500-020-05106-0.

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27

Mickey, Brian J., et John C. Middlebrooks. « Sensitivity of Auditory Cortical Neurons to the Locations of Leading and Lagging Sounds ». Journal of Neurophysiology 94, no 2 (août 2005) : 979–89. http://dx.doi.org/10.1152/jn.00580.2004.

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We recorded unit activity in the auditory cortex (fields A1, A2, and PAF) of anesthetized cats while presenting paired clicks with variable locations and interstimulus delays (ISDs). In human listeners, such sounds elicit the precedence effect, in which localization of the lagging sound is impaired at ISDs ≲10 ms. In the present study, neurons typically responded to the leading stimulus with a brief burst of spikes, followed by suppression lasting 100–200 ms. At an ISD of 20 ms, at which listeners report a distinct lagging sound, only 12% of units showed discrete lagging responses. Long-lasting suppression was found in all sampled cortical fields, for all leading and lagging locations, and at all sound levels. Recordings from awake cats confirmed this long-lasting suppression in the absence of anesthesia, although recovery from suppression was faster in the awake state. Despite the lack of discrete lagging responses at delays of 1–20 ms, the spike patterns of 40% of units varied systematically with ISD, suggesting that many neurons represent lagging sounds implicitly in their temporal firing patterns rather than explicitly in discrete responses. We estimated the amount of location-related information transmitted by spike patterns at delays of 1–16 ms under conditions in which we varied only the leading location or only the lagging location. Consistent with human psychophysical results, transmission of information about the leading location was high at all ISDs. Unlike listeners, however, transmission of information about the lagging location remained low, even at ISDs of 12–16 ms.
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28

Gao, Xuehong, Chanseok Park, Xiaopeng Chen, En Xie, Guozhong Huang et Dingli Zhang. « Globally Optimal Facility Locations for Continuous-Space Facility Location Problems ». Applied Sciences 11, no 16 (9 août 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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29

Luo, Xiaoling, Wenbo Fan, Yangsheng Jiang et Jun Zhang. « Optimal Design of Bus Stop Locations Integrating Continuum Approximation and Discrete Models ». Journal of Advanced Transportation 2020 (7 septembre 2020) : 1–10. http://dx.doi.org/10.1155/2020/8872748.

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Although transit stop location problem has been extensively studied, the two main categories of modeling methodologies, i.e., discrete models and continuum approximation (CA) ones, seem have little intersection. Both have strengths and weaknesses, respectively. This study intends to integrate them by taking the advantage of CA models’ parsimonious property and discrete models’ fine consideration of practical conditions. In doing so, we first employ the state-of-the-art CA models to yield the optimal design, which serves as the input to the next discrete model. Then, the stop location problem is formulated into a multivariable nonlinear minimization problem with a given number of stop location variables and location constraint. The interior-point algorithm is presented to find the optimal design that is ready for implementation. In numerical studies, the proposed model is applied to a variety of scenarios with respect to demand levels, spatial heterogeneity, and route length. The results demonstrate the consistent advantage of the proposed model in all scenarios as against its counterparts, i.e., two existing recipes that convert CA model-based solution into real design of stop locations. Lastly, a case study is presented using real data and practical constraints for the adjustment of a bus route in Chengdu (China). System cost saving of 15.79% is observed by before-and-after comparison.
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30

Iimura, Takuya, et Pierre von Mouche. « Discrete hotelling pure location games : potentials and equilibria ». ESAIM : Proceedings and Surveys 71 (août 2021) : 163–74. http://dx.doi.org/10.1051/proc/202171163.

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We study two-player one-dimensional discrete Hotelling pure location games assuming that demand f(d) as a function of distance d is constant or strictly decreasing. We show that this game admits a best-response potential. This result holds in particular for f(d) = wd with 0 < w ≤ 1. For this case special attention will be given to the structure of the equilibrium set and a conjecture about the increasingness of best-response correspondences will be made.
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31

Salhi, S., et M. Daskin. « Network and Discrete Location : Models, Algorithms and Applications. » Journal of the Operational Research Society 48, no 7 (juillet 1997) : 763. http://dx.doi.org/10.2307/3010074.

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32

Salhi, S., et A. I. Barros. « Discrete and Fractional Programming Techniques for Location Models. » Journal of the Operational Research Society 50, no 7 (juillet 1999) : 775. http://dx.doi.org/10.2307/3010333.

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33

Gluszak, Michal, et Bartlomiej Marona. « Discrete choice model of residential location in Krakow ». Journal of European Real Estate Research 10, no 1 (2 mai 2017) : 4–16. http://dx.doi.org/10.1108/jerer-01-2016-0006.

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Purpose This paper aims to discuss the link between socio-economic characteristics of house buyers and their housing location choices. The major objective of the study is an examination of the role of household socio-economic characteristics. The research addresses the importance of previous residence location and latent housing motives for intra-urban housing mobility. Design/methodology/approach The research examines housing preferences structure and analyzes housing location choices in the city of Krakow (Poland) using discrete choice model (conditional logit model). The research is based on stated preference data from Krakow. Findings The results of this study suggest that demand for housing alternatives is negatively linked to the distance from current residence. Other factors stay equal, the further the distance, the less likely a household is willing to choose a location within the metropolitan area. The study indicates that housing motives can help explain housing location decisions. Practical implications The paper provides an empirical assessment of housing decisions in Krakow, one of the major metropolitan areas in Poland. Originality/value The paper contributes to a better understanding of the nature of housing decision and housing preferences in emerging markets in Central and Eastern Europe. As a result, presented research helps to fill the gap in housing market and urban economics literature.
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34

Martinec, I. « Methods for a class of discrete location problems ». Optimization 16, no 4 (janvier 1985) : 581–95. http://dx.doi.org/10.1080/02331938508843052.

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35

Goodman, Allen C., Harold D. Holder, Eleanor Nishiura et Janet R. Hankin. « A Discrete Choice Model of Alcoholism Treatment Location ». Medical Care 30, no 12 (décembre 1992) : 1097–110. http://dx.doi.org/10.1097/00005650-199212000-00003.

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36

Daskin, M. « Network and Discrete Location : Models, Algorithms and Applications ». Journal of the Operational Research Society 48, no 7 (juillet 1997) : 763–64. http://dx.doi.org/10.1057/palgrave.jors.2600828.

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37

Dinwoodie, John. « Network and discrete location : Models, algorithms and applications ». Journal of Transport Geography 4, no 4 (décembre 1996) : 302–3. http://dx.doi.org/10.1016/s0966-6923(97)89394-5.

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38

Plastria, Frank. « Network and discrete location models, algorithms and applications ». Location Science 4, no 1-2 (mai 1996) : 117–19. http://dx.doi.org/10.1016/s0966-8349(97)84406-3.

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39

Cappanera, P., G. Gallo et F. Maffioli. « Discrete facility location and routing of obnoxious activities ». Discrete Applied Mathematics 133, no 1-3 (novembre 2003) : 3–28. http://dx.doi.org/10.1016/s0166-218x(03)00431-1.

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40

Li, Xiaohua, et Lansun Shen. « Fast text location based on discrete wavelet transform ». Journal of Electronics (China) 22, no 4 (juillet 2005) : 385–94. http://dx.doi.org/10.1007/bf02687926.

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41

Venkateshan, Prahalad, Ronald H. Ballou, Kamlesh Mathur et Arulanantha P. P. Maruthasalam. « A Two-echelon joint continuous-discrete location model ». European Journal of Operational Research 262, no 3 (novembre 2017) : 1028–39. http://dx.doi.org/10.1016/j.ejor.2017.03.077.

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42

Hosage, C. M., et M. F. Goodchild. « Discrete space location-allocation solutions from genetic algorithms ». Annals of Operations Research 6, no 2 (février 1986) : 35–46. http://dx.doi.org/10.1007/bf02027381.

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43

Kozhamkulova, Zhadra, Mukhit Maikotov, Bibinur Kirkizbayeva, Zhumakyz Chingenzhinova, Gulzhan Sapieva et Venera Kerimbaeva. « EXTENSION METHOD FOR LOCATION PROBLEMS WITH DISCRETE OBJECTS ». Theoretical & ; Applied Science 58, no 02 (28 février 2018) : 64–69. http://dx.doi.org/10.15863/tas.2018.02.58.16.

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44

Daskin, M. « Network and Discrete Location : Models, Algorithms and Applications ». Journal of the Operational Research Society 48, no 7 (1997) : 763. http://dx.doi.org/10.1038/sj.jors.2600828.

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45

Chen, Guihua, Zhihong Huang et Zhijie Mai. « Two-dimensional discrete Anderson location in waveguide matrix ». Journal of Nonlinear Optical Physics & ; Materials 23, no 03 (septembre 2014) : 1450033. http://dx.doi.org/10.1142/s0218863514500337.

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Anderson location is an important wave phenomenon when the system contains disorder. Anderson location of light is a significant topic in optical science. Arrays of evanescently coupled waveguides made of nonlinear materials are the fundamental model of discrete nonlinear optics. Guided propagation of light in such arrays emulates electronic wave functions in fundamental periodic and disordered potentials of solid state physics. In this work, the Anderson location effect in a two-dimensional waveguide matrix is studied, and the influence of the nonlinearity on the localized effect induced by the disorder of the system is considered.
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46

Campbell, James F. « Integer programming formulations of discrete hub location problems ». European Journal of Operational Research 72, no 2 (janvier 1994) : 387–405. http://dx.doi.org/10.1016/0377-2217(94)90318-2.

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47

Darzentas, J. « A discrete location model with fuzzy accessibility measures ». Fuzzy Sets and Systems 23, no 1 (juillet 1987) : 149–54. http://dx.doi.org/10.1016/0165-0114(87)90106-0.

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48

Lančinskas, Algirdas, Pascual Fernández, Blas Pelegín et Julius Žilinskas. « Improving solution of discrete competitive facility location problems ». Optimization Letters 11, no 2 (7 août 2015) : 259–70. http://dx.doi.org/10.1007/s11590-015-0930-3.

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49

Zhang, Hao, Ying Xiong, Mingke He et Chongchong Qu. « Location Model for Distribution Centers for Fulfilling Electronic Orders of Fresh Foods under Uncertain Demand ». Scientific Programming 2017 (2017) : 1–13. http://dx.doi.org/10.1155/2017/3423562.

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The problem of locating distribution centers for delivering fresh food as a part of electronic commerce is a strategic decision problem for enterprises. This paper establishes a model for locating distribution centers that considers the uncertainty of customer demands for fresh goods in terms of time-sensitiveness and freshness. Based on the methodology of robust optimization in dealing with uncertain problems, this paper optimizes the location model in discrete demand probabilistic scenarios. In this paper, an improved fruit fly optimization algorithm is proposed to solve the distribution center location problem. An example is given to show that the proposed model and algorithm are robust and can effectively handle the complications caused by uncertain demand. The model proposed in this paper proves valuable both theoretically and practically in the selection of locations of distribution centers.
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50

Durmaz, Engin, Necati Aras et İ. Kuban Altınel. « Discrete approximation heuristics for the capacitated continuous location–allocation problem with probabilistic customer locations ». Computers & ; Operations Research 36, no 7 (juillet 2009) : 2139–48. http://dx.doi.org/10.1016/j.cor.2008.08.003.

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