doi: 10.17586/2226-1494-2023-23-2-323-330


Information model of the essential goods purchase duration

Y. M. Khlyupina, D. A. Kuznetsov, A. A. Laptev


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Khlyupina Yu.M., Kuznetsov D.A., Laptev A.A. Information model of the essential goods purchase duration. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 2, pp. 323–330. doi: 10.17586/2226-1494-2023-23-2-323-330


Abstract
The task of reducing the time for the purchase of essential goods is especially relevant in cases of shortage of free time of buyers. To do this, it is necessary to predict and estimate the time required to purchase goods. Traditional approaches based on cartographic systems do not provide estimates and forecasts, but only allow you to build a route to the right place based on an assessment of the traffic situation. For this reason, the problem of developing a more modern model is relevant, taking into account such factors as the infrastructural location of the store, user evaluation, and the workload of the store. The paper proposes an information model that includes such time costs of the buyer as the search for goods, the route to the place of sale of goods, the purchase of goods. The time spent on the purchase of goods is described using elements of queuing theory. Statistical and direct methods for assessing the workload and queues in the store are highlighted. The developed generalized model contains the parameters necessary to estimate the required time using statistical methods which include traffic forecasting based on user ratings and reviews, analysis of the infrastructure location and public video surveillance cameras, public Application Programming Interface of stores, and Internet services. Correction coefficients have been introduced to adjust the estimation of model parameters depending on the infrastructure location of the store and user ratings. A new information model has been formulated that allows taking into account the dependence of the time required to purchase emergency goods on the workload of the store, its infrastructure location, ratings and user reviews. The simulation model is developed in the AnyLogic environment. An example of using the model to estimate the average time spent on the purchase of emergency goods is demonstrated. The simulation results are consistent with the conducted experiment in which purchases of emergency goods were made in various stores in Saint Petersburg. The developed model can be used when searching for the optimal route to the place of sale of essential goods when planning the construction of stores as well as in the areas of marketing and delivery of goods.

Keywords: model, optimal route, time estimation, cartographic system, purchase of goods

Acknowledgements. This research is financially supported by The Russian Science Foundation, Agreement No. 17-71-30029 with co-financing of the “Bank Saint Petersburg”.

References
  1. Buevich D. Comparative characteristics of the cartographic services. Computer systems and networks: materials of the 54th Scientific Conference of Postgraduates, Undergraduates and Students of BSUIR (Minsk, Belarus), 2018, pp. 53–54. (in Russian)
  2. Hizhnjak Ju.D.Overview of the most popular cartographic services that provide API for developers. NovaInfo, 2017, vol. 70, pp. 38–46. (in Russian)
  3. Barbosa-Filho H., Barthelemy M., Ghoshal G., James C.R., Lenormand M., Louail T., Menezes R., Ramasco J.J., Simini F., Tomasini M. Human mobility: Models and applications. Physics Reports, 2018, vol. 734, pp. 1–74. https://doi.org/10.1016/j.physrep.2018.01.001
  4. Feng J., Li X., Mao B., Xu Q., Bai Y. Weighted complex network analysis of the Beijing subway system: Train and passenger flows. Physica A: Statistical Mechanics and its Applications, 2017, vol.  474, pp. 213–223.
  5. Mihić M., Anić I.D., Milaković I.K. Time spent shopping and consumer clothing purchasing behavior. Ekonomski Pregled, 2018, vol. 69, no. 2, pp. 89–105. https://doi.org/10.32910/ep.69.2.1
  6. Cachero-Martínez S., Vázquez R. Developing the marketing experience to increase shopping time: The moderating effect of visit frequency. Administrative Sciences, 2018, vol. 8, no. 4, pp. 77. https://doi.org/10.3390/admsci8040077
  7. Ostapchuk A.O., Kapshtyk A.I. Contemporary aspects and factors contributing to the quality formation  of trade services for customers. Regional problems of economic transformation, 2018, pp. 160–163. (in Russian)
  8. Van Malder A. The effect of ambient scent on time spent in retail stores. 2019. Available at: https://www.scriptiebank.be/sites/default/files/thesis/2019-10/Masters%20Thesis%20-%20Anke%20Van%20Malder%20.pdf(accessed: 22.10.2022).
  9. Matvienko O.I., Aleshina O.G. Customer journey map - a tool for studying consumer behavior from needing before purchase. Modern Economy Success, 2020, no. 1, pp. 91–98. (in Russian)
  10. Pertsev P. Kendall's classification. Special communication and information security: technologies, management, economics. Proceedings of the 3rd International Scientific Symposium, 2017, pp. 112–113. (in Russian)
  11. Romanenko V.A. Optimizing the allocation of technological resources of an air transport system in the presence of a schedule and fuzzy input data. Vestnik of Samara University. Aerospace and Mechanical Engineering, 2021, vol. 20, no. 3, pp. 160–170. Available at: https://cyberleninka.ru/article/n/optimizatsiya-raspredeleniya-tehnologicheskih-resursov-aviatransportnoy-sistemy-pri-nalichii-raspisaniya-i-nechyotkih-ishodnyh (accessed: 22.10.2022). (in Russian). https://doi.org/10.18287/2541-7533-2021-20-3-160-170
  12. Nefedov V.V., Russkih M.V., Meremkulov A.K., Kushnarenko I.V. Short-term forecasting of passenger flows based on the statistics the empirical way.Bulletin of Higher Educational Institutions. North Caucasus region. Technical Sciences, 2013, no. 6(175), pp. 95–99. Available at: https://cyberleninka.ru/article/n/kratkosrochnoe-prognozirovanie-passazhiropotokov-na-osnove-statisticheskih-dannyh (accessed: 22.10.2022). (in Russian)
  13. Ivanov V.V., Osetrov E.S. Forecasting daily passenger traffic volumes in the moscow metro. Physics of Particles and Nuclei Letters, 2018, vol. 15, no. 1, pp. 107–120.  https://doi.org/10.1134/s1547477118010089
  14. Yudanova V.V. Imitating modeling of mass service systems. Russian Journal of Resources, Conservation and Recycling, 2019, vol. 6, no. 4, pp. 21. Available at: https://resources.today/PDF/23INOR419.pdf (accessed: 22.10.2023). (in Russian).  


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