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All issues Volume 214 (2020) E3S Web Conf., 214 (2020) 03025 References
Open Access
Issue
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
Article Number 03025
Number of page(s) 9
Section Digital Development and Environmental Management of Energy Supply Chain
DOI https://doi.org/10.1051/e3sconf/202021403025
Published online 07 December 2020
  1. Kacen JJ., Hess J.D., Walker D. “Spontaneous selection: The influence of product and retailing factors on consumer impulse purchases. ” Journal of retailing and consumer services, 2012, 19(6), pp. 578-588. [Google Scholar]
  2. Zhao Feng, Wang Z.e. “Research on The Application of Improved SLP and AHP Methods in Supermarket layout. ”Logistics Science and Technology, 2018(9), pp. 13-17. [Google Scholar]
  3. Liu Zhihai, Zhang Dandan. “Supermarket Layout Optimization Based on Improved SLP. ” Logistics engineering and management, 2017(3), pp. 117-120. [Google Scholar]
  4. FAYYAD U. “From Data Mining to Knowledge Discovery in Databases. ” AI Magazine, 1996, 17(3), pp. 37-54. [Google Scholar]
  5. HE X, MIN F., ZHU W. “Parametric Rough Sets with Application to Granular Association Rule Mining. ” Mathematical Problems in Engineering, 2013, 2013(2), pp. 1-13. [Google Scholar]
  6. Francisco Azuaje, Witten IH, Frank E. “ Data Mining: Practical Machine Learning Tools and Techniques 2nd edition. ” Biomedical Engineering Online, 2006, 5(1), pp. 51. [Google Scholar]
  7. Li Xiao, Chen Wenbin, Cheng min. “Supermarket Layout Design Algorithm Based on Shopping Data. ” Digital design, 2016, 10(5), pp. 36-40. [Google Scholar]
  8. Wang Tingting, Yang Qin. “Optimal Scheduling of Supermarket Cash Registers Based on Simulated Plant Growth Algorithm. ” Computer application, 2014, 34(5), pp. 1516-1520. [Google Scholar]
  9. Andrade EB.. “Behavioral Consequences of Affect: Combining Evaluative and Regulatory Mechanisms. ” Journal of consumer research, 2005, 32(3), pp. 355-362. [Google Scholar]
  10. Zhang Yunlai. “Research on Integration of Impulsive Purchase Influence Mechanism Based on Emotional Perspective. ” Journal of Harbin Business University (SOCIAL SCIENCE EDITION), 2009(04):, pp. 3-7. [Google Scholar]
  11. Jia Tongling. “Store Line and “Magnetic Point” Design. ” China Cooperation Times, 2014-06-06 (B03). [Google Scholar]
  12. Gu Guangsheng. ““Magnetic Point” Theory and Display Skills of Small and Medium-sized Supermarkets: A Case Study of Meizhou Pacific Supermarket. ” Commercial Research, 2007 (06):, pp. 144-147. [Google Scholar]
  13. Masao Ohta, Yoshiyuki Higuchi. “Study on the Design of Supermarket Store Layouts: The Principle of “Sales Magnet”. World Academy of Science, Engineering and Technology Vol: 7 2013-01-26. [Google Scholar]
  14. R. Agrawal, T. Imielinski, A. Swami. “Mining Association Rules between Sets of Items in Large Databases”. Y. L. Diao. Acm Sigmod Record, Washington DC, May 26-28, 1993, NewYork: ACM Press, 1003, pp. 207-216. [Google Scholar]
  15. Hui S. K, Fader, P.S. Bradlow. “Research note-The traveling salesman goes shopping: The systematic deviations of grocery paths from TSP optimality. ” Marketing Science. 2009, pp. 566-572. [Google Scholar]
  16. X. Wenjie, L. Senbiao, H. Lihong. ” Simulation model of supermarket queuing system. ” Chinese Control Conference (CCC). Hangzhou, 2015, pp. 8819-8823. [Google Scholar]
  17. C. Yanfei, Z. Yafeng. SWOT Analysis on the Supermarkets in China[A]. International Conference on Business Computing and Global Informatization[C]. Shanghai, 2012, pp. 206-209. [Google Scholar]
  18. Huang Xigeng. The Role of Magnetic Dots in The Display Design of Drugstores [N]. 21st Century Pharmacy. 2017-11-20 (B02). [Google Scholar]
  19. Yu Liqiong. “Exploring The Combination of Regional Shopping Mall Formats Based on Consumer Preferences”. Commercial Economy Research, 2018 (1): 47-49. [Google Scholar]
  20. Péter Boros, “Orsolya Fehér, Zoltán Lakner, e. l. Modeling supermarket re-layout from the owner’s perspective. ” Annals of Operations Research, 2015, 238(1), pp. 60-63. [Google Scholar]
  21. Xiong Ping. “Data Mining Algorithm and Clementine Practice. ” Beijing: Tsinghua University Press, 2011. [Google Scholar]
  22. Kacen J, Hess J.D., Walker D. “Spontaneous selection: The influence of product and retailing factors on consumer impulse purchases. ” Journal of retailing and consumer services, 2012, 19(6), pp. 578-588. [Google Scholar]
  23. Iris IM.. “In-store impact on impulse shoppers. ” Marketing, 2002, 34(2), pp. 357-364. [Google Scholar]
  24. Neha P. Mehta, Pawan K.Chugan.. “The Impact of Visual Merchandising on Impulse Buying Behavior of Consumer: A Case from Central Mall of Ahmedabad India. ” Social Science Electronic Publishing., 2011, 57 (3), pp. 599-610. [Google Scholar]
  25. Zhang Shenghua, Li Jiahui, Li N.a. “Application of Association Rule Analysis in the Commodity Layout of Supermarkets. ” SME Management and Science and Technology, 2019 (05), pp. 61-63. [Google Scholar]
  26. Mowrey CH., Parikh P.J., Gue K.R. “A Model to Optimize Rack Layout in a Retail Store. ” European Journal of Operational Research, 2018, 377(2), pp. 71-83. [Google Scholar]
  27. Volpe R, Cho C. “Market structure determinants of performance for independent supermarkets. ” Managerial and Decision Economics, 2019, 40(8), pp. 871-881. [Google Scholar]
  28. Bill Page, Giang Trinh, Svetlana Bogomolova. “Comparing two supermarket layouts: The effect of a middle aisle on basket size, spend, trip duration and endcap use. ” Journal of Retailing and Consumer Services, 2019, 47(1), pp. 49-56. [Google Scholar]
  29. Elif Ozgormus, Alice E.Smith.. “A data-driven approach to grocery store block layout. ” Computers & Industrial Engineering, 2020, 139(10), pp. 55-62. [Google Scholar]
  30. Jean-Paul Arnaout, Caline ElKhoury, Gamze Karayaz. “Solving the multiple level warehouse layout problem using ant colony optimization. ” Operational Research, 2020, 20(1), pp. 473-490. [Google Scholar]

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