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Digital technology of centralized procurement organization
 
PIIS042473880018980-7-1
DOI10.31857/S042473880018980-7
Publication type Article
Status Published
Authors
Natalya Aleynikova 
Occupation: Assistant Professor
Affiliation: Voronezh State University
Address: Voronezh, Universitetskaya pl., 1
Mikhail Matveev
Occupation: Head of Department, Department of Information Technologies of Management, Computer Science Faculty
Affiliation: Voronezh State University
Address: Russian Federation, Voronezh
Journal nameEkonomika i matematicheskie metody
EditionVolume 58 Issue 1
Pages70-79
Abstract

The article is devoted to the development of models framework for digital transformation of processes in corporate and public procurement systems. In particular, the solution of the problem of multicriteria choice in economic systems described by the three-part graph "producer — resource — consumer" is considered. A mathematical model for the optimal distribution of a homogeneous resource from suppliers to consumers in a centralized procurement system is proposed. This model is reduced to a transport problem with intermediate points. Optimization is aimed at achieving maximum compliance in terms of the totality of technical and commercial characteristics of a homogeneous resource. To set the requirements for these characteristics on the part of the consumer, it is proposed to use fuzzy variables. This provides the consumer with a flexible mechanism for describing resource requirements based on his preferences. An operator of aggregation of local correspondences concerning a set of characteristics is proposed in the form of a discrete Choquet integral with a fuzzy measure. Using the example of production equipment, it is shown how it is possible to formalize the parameters of the model, and then to optimize and automate the process of distributing equipment by solving a transport problem with intermediate points in such a way, that the maximum correspondence in its characteristics is achieved. The developed models and algorithms can be used to create information services on electronic trading platforms, including the of public procurement.

Keywordsdigital transformation; centralized procurement system; homogeneous resource; linguistic and fuzzy variables; aggregation; Choquet integral; fuzzy measure; multi-criteria choice; transport problem with intermediate points
Received27.02.2022
Publication date18.03.2022
Number of characters21568
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1. Автоматизация закупок: опыт крупнейших заказчиков, структура рынка, тренды. Исследование_TAdviser (2019). Режим доступа: >>>>
2. Будяков А.Н., Гетманова К.Г., Матвеев М.Г. (2017). Решение задачи выбора ресурсов и их поставщиков в условиях противоречивости технических и коммерческих требований // Вестник Воронежского государственного университета, серия: Системный анализ и информационные технологии. № 3. С. 26–34.
3. Караев А.Э., Москвитина И.В., Будяков А.Н. (2015). Использование методики планирования по обезличенным МТР при закупках и поставках комплектного оборудования для крупных проектов нефтяной отрасли // Нефть России. № 10. С. 52–53.
4. Леденева Т.М., Подвальный С.Л. (2016). Агрегирование информации в оценочных системах // Вестник Воронежского государственного университета, серия: Системный анализ и информационные технологии. № 4. С. 155–164.
5. Матвеев М.Г. (2021). Информационные технологии формирования предложения на электронной торговой площадке с технологией «маркетплейс» // Экономика и математические методы. Т. 57. № 1. С. 105–112.
6. Мягких Н.Ю., Гриненко С.В. (2017). Механизм централизации системы государственных закупок // Научное обозрение. Экономические науки. № 3. С. 69–72.
7. Нечеткие множества в моделях управления и искусственного интеллекта (1986). Под ред. Д.А. Поспелова. М.: Наука. Гл. ред. физ.-мат. лит.
8. Таха Х.А. (2001). Введение в исследование операций. М.: Издательский дом «Вильямс».
9. Ягер Р. (ред.) (1986). Нечеткие множества и теория возможностей. Последние достижения. М.: Радио и связь.
10. Amin S.H., Razmi J. (2009). An integrated fuzzy model for supplier management: A case study of ISP selection and evaluation. Expert Systems with Applications, 36, 4, 8639–8648.
11. Amin S.H., Zhang G. (2012). An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert Systems with Applications, 39, 8, 6782–6791.
12. Detyniecki M. (2000). Mathematical aggregation operators and their application to video querying. Ph.D. dissertation, University of Paris VI, Paris, France.
13. Mendoza A., Ventura J.A. (2012). Analytical models for supplier selection and order quantity allocation. Applied Mathematical Modelling, 36, 8, 3826–3835.
14. Mendoza F., Ventura J.A. (2011). Modeling actual transportation costs in supplier selection and order quantity allocation decisions. Operational Research, 13, 1, 5–25.
15. Moosavi S.A., Ebrahimnejad S. (2017). A new multi-objective mathematical model for supplier selection in uncertain environment. 13th International Conference on Industrial Engineering (IIEC2017).
16. Roth A.E. (2003). The origins, history, and design of the resident match. Jama, 289, 7, 909.
17. Roth A.E., Rothblum U.G. (1999). Truncation strategies in matching markets — in search of advice for participants. Econometrica, 67, 1, 21–43.
18. Yager R.R. (1988). Ordered weighted averaging aggregation operators in multi-criteria decision making // IEEE Trans. on Systems, Man and Cybernetics, T.18. C.183–190.

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