Decision Support System for Order Processing Based on Cross-programming Technology

2023;
: pp. 167 - 188
1
Lviv Polytechnic National University, Information Systems and Networks Department,
2
Lviv Polytechnic National University, Information Systems and Networks Department
3
Ivan Franko National University of Lviv, Applied Mathematics Department
4
Lviv Polytechnic National University, Information Systems and Networks Department
5
Osnabrück University, International Economic Policy Chair

A typical standard architecture of the support system has been proposed, making decisions on forming and implementing solutions based on cross-programming and heavy calculations and similar functional capabilities before it. The technology for disaggregating such systems on the basis of cross- programming and efficient calculations, as well as reducing costs/hours/resources for disaggregation, promotion and support of such support systems to support the adoption of a solution, has also been proposed. A structural model of the system has been proposed to enable expansion and distribution in all areas of electronic commerce in modern Ukraine. This is relevant for today, in the face of a large-scale war, when the skin business of the region is constantly moving from an offline mode of operation to an online one, given the availability of standards and illegal benefits for such systems in the world.

  1. Ekspress Dostavka. URL: https://www.sat.ua/about/strategy/ekspress-dostavka/.
  2. Kwilinski A., Zaloznova Y., Trushkina N., Rynkevych N. (2020). Organizational and methodological support for Ukrainian coal enterprises marketing activity improvement. In E3S Web of Conferences,  Vol. 168, p. 00031. EDP Sciences. DOI: 10.1051/e3sconf/202016800031
  3. Overmeyer L., Ventz, K. Falkenberg, S. et al. (2010). Interfaced multidirectional small-scaled modules for intralogistics operations. Logist. Res., 2, 123–133. DOI: 10.1007/s12159-010-0038-1.
  4. Comprehensive statistical publications. URL: http://www.ukrstat.gov.ua/druk/publicat/kat_u/2020/zb/07/zb_ Ukraine% 20in%20figures_ u.pdf.
  5. Chołodecki M. (2023). The Impact of the COVID-19 Pandemic on the Postal Market. Challenges and Opportunities for the Postal Regulatory Framework. In: Parcu, P. L., Brennan, T. J., Glass, V. (eds) The Postal and Delivery Contribution in Hard Times. Topics in Regulatory Economics and Policy. Springer, Cham. DOI: 10.1007/978- 3-031-11413-7_16
  6. Kis Y., Chyrun L., Tsymbaliak T., Chyrun L. (2020). Development of System for Managers Relationship Management with Customers. In: Lytvynenko, V., Babichev, S., Wójcik, W., Vynokurova, O., Vyshemyrskaya, S., Radetskaya, S. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2019. Advances in Intelligent Systems and Computing, Vol. 1020. Springer, Cham. DOI: 10.1007/978-3-030-26474-1_29
  7. Berko A., Bublyk M., Chyrun L., Matseliukh Y., Levus R., Panasyuk V., et al. (2021). Models and Methods for E-Commerce Systems Designing in the Global Economy Development Conditions Based on Mealy and Moore Machines. In COLINS,  1574–1593.
  8. Vysotska V., Bublyk M., Vysotsky A., Berko A., Chyrun L., Doroshkevych K. (2020). Methods and tools for web resources processing in e-commercial content systems. In 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT), Vol. 1, 114–118. DOI: 10.1109/CSIT49958.2020.9321950
  9. Lytvyn V., et al. (2019). Design of the architecture of an intelligent system for distributing commercial content in the internet space based on SEO-technologies, neural networks, and Machine Learning. Eastern-European Journal of Enterprise Technologies, 2(2), 15–34. DOI: 10.15587/1729-4061.2019.164441
  10. Demchuk A., Lytvyn V., Vysotska V., Dilai, M. (2020). Methods and Means of Web Content Personalization for Commercial Information Products Distribution. Advances in Intelligent Systems and Computing, Vol. 1020. Springer, Cham. DOI: 10.1007/978-3-030-26474-1_24
  11. Gozhyj A., Kalinina I., Vysotska V., Sachenko S., Kovalchuk R. (2020). Qualitative and Quantitative Characteristics Analysis for Information Security Risk Assessment in E-Commerce Systems. In ICTES,  177–190. URL:    http://ceur-ws.org/Vol-2762/paper12.pdf
  12. Gozhyj A., Vysotska V., Yevseyeva I., Kalinina I., Gozhyj V. (2019). Web Resources Management Method Based on Intelligent Technologies. Advances in Intelligent Systems and Computing, Vol. 871. Springer, Cham. DOI: 10.1007/978-3-030-01069-0_15.
  13. Bublyk M., Vysotska V., Chyrun L., Panasyuk V., Brodyak, O. (2021). Assessing Security Risks Method in E-Commerce System for IT Portfolio Management. In IntelITSIS, 362–379. URL: http://ceur-ws.org/Vol- 2853/paper42.pdf
  14. Balush I., Vysotska V., Albota, S. (2021). Recommendation System Development Based on Intelligent Search, NLP and Machine Learning  Methods. In MoMLeT+ DS, 584–617. URL: http://ceur-ws.org/Vol- 2917/paper39.pdf
  15. Vysotska V., Berko A., Lytvyn V., Kravets P., Dzyubyk L., Bardachov Y., Vyshemyrska S. (2021). Information Resource Management Technology Based on Fuzzy Logic. Advances in Intelligent Systems and Computing, Vol. 1246. Springer, Cham. DOI: 10.1007/978-3-030-54215-3_11
  16. Lytvyn V., et al. (2019). Design of a recommendation system based on Collaborative Filtering and machine learning considering personal needs of the user. Eastern-European Journal of Enterprise Technologies, 4(2), 6–28. DOI: 10.15587/1729-4061.2019.175507
  17. Lytvyn V., Gozhyj A., Kalinina I., Vysotska V., Shatskykh V., Chyrun, L. (2019). An Intelligent System of the Content Relevance at the Ex-ample of Films According to User Needs. CEUR Workshop Proceedings (ICTES 2019), Vol. 2516. URL: https://sci.ldubgd.edu.ua/bitstream/123456789/7402/1/paper1%20Vol- 2516.%20%D0%A0.%201-23.pdf
  18. Antonyuk N., et al. (2019, September). Consolidated information web resource for online tourism based on data integration and geolocation. In 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT), Vol. 1,  15–20. DOI: 10.1109/STC-CSIT.2019.8929790.