Advertisement

Research and Development of Models and Program for Optimal Product Line Control

  • Taisa BorovskaEmail author
  • Dmitry Grishin
  • Irina Kolesnik
  • Victor Severilov
  • Ivan Stanislavsky
  • Tetiana Shestakevych
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1080)

Abstract

The article is devoted to the development of mathematical models and program for methods of optimal control of product lines. The classes of the lines are analyzed, the mathematical model of the line as a control object for the manufacturer, retailer and customer are proposed. The analysis of previous developments was carried out: market models with asymmetric information structure, models of manufacturers of the production segment and alternative simulation models of the product line. The study of the dynamics and steady state of the product line was carried out. To study the dynamics of the product line, a simulation model of «producers, product lines, consumers» was used, in which the choice of consumers is simulated in the samples. The problem of optimal aggregation of a multidimensional nonlinear, stochastic and non-stationary object «product line» has been set and solved. Optimal control program has been developed on the basis of optimal aggregation. Examples of modeling are given.

Keywords

Optimal aggregation Product line Production function Demand function Information technology 

References

  1. 1.
    Borovska, T.M., Vernigora, I.V., Grishin, D.I., Severilov, V.A., Gromaszek, K., Aizhanova, A.: Adaptive production control system based on optimal aggregation methods. In: Proceedings of the SPIE 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 108086O, 1 October 2018).  https://doi.org/10.1117/12.2501520
  2. 2.
    Borovska, T., Vernigora, I., Kolesnyk, I., Kushnir, A.: Control of multi-channel multiphase queuing system based on optimal aggregation methodology. In: Proceedings of the 13th International Scientific and Technical Conference «Computer science and information technologies» CSIT 2018, Lviv, Ukraine, 11–14 September 2018, vol. 1, pp. 259–265. Publishing House «Vezha and Ko», Lviv (2018). ISBN 978-1-5386-6463-6Google Scholar
  3. 3.
    Borovska, T.M.: Mathematical models of functioning and development of production systems based on optimal aggregation methodology. VNTU, Vinnitsa, Ukraine, 308. ISBN 978–966–641–731–5Google Scholar
  4. 4.
    Krishnamurthi, P.: Product lining and price lining (2007). http://fmcg-marketing.blogspot.com/2007/10/product-lining-and-price-lining.html. Accessed 20 Mar 2016
  5. 5.
    Hanks, G.: Examples of a product line extension (n.d.). http://smallbusiness.chron.com/examples-product-line-extension-69425.html. Accessed 20 Mar 2016
  6. 6.
    Learn marketing: Product strategy (n.d.). http://www.learnmarketing.net/productobjectives.htm. Accessed 20 Mar 2016
  7. 7.
    Wilsom, O.L., Norton, A.J.: Optimal entry timing for a product line extension. Market Sci. 8(1), 1–17 (1989). JSTOR 184099. https://www.jstor.org/stable/184099
  8. 8.
    Quinonez, N.: 5 Product line pricing strategies you need to know (2014). https://blog.udemy.com/product-line-pricing/. Accessed 20 Mar 2016
  9. 9.
    Xinxin, L.: Self-selection and information role of online product reviews. Inf. Syst. Res. 19(4), 56–64 (2007)Google Scholar
  10. 10.
    Neubauer, J., Steffen, B., Margaria, T.: Higher-order process modeling: product-lining, variability modeling and beyond (2013)Google Scholar
  11. 11.
    Mukha, Ap.A.: Control of the process of complex engineering systems and processes development. Characteristic features of FMEA-analysis application. Mathematical machine and systems, no. 2, pp. 168–176 (2012). ISSN 1028-9763Google Scholar
  12. 12.
    Rüttimann, B.: Introduction to Modern Manufacturing Theory, p. 149. Springer International Publishing AG 2018 (2016).  https://doi.org/10.1007/978-3-319-58601-4
  13. 13.
    Nersessian, N.J., Chandrasekharan, S.: Hybrid analogies in conceptual innovation in science. Cogn. Syst. Res. 10, 178–188 (2009).  https://doi.org/10.1016/j.cogsys.2008.09.009CrossRefGoogle Scholar
  14. 14.
    Burkov, V.N., Novikov, D.A.: Introduction to the theory of active systems, 125 p. ICP RAS (1996)Google Scholar
  15. 15.
    Opoitsev, V.I.: Equilibrium and stability in models of collective behavior, 245 p. World, Moscow, USSR (1977)Google Scholar
  16. 16.
    Forrester, J.W.: Basics of Cybernetics Enterprises (Industrial Dynamics), 340 p. Progress, Moscow, USSR (1971)Google Scholar
  17. 17.
    Leontiev, V.: Theoretical assumptions and nonobservable facts. Economy, ideology, politics, USA, no. 9, p. 15 (1972)Google Scholar
  18. 18.
    Bellman, R., Gliksberg, I., Gross, O.: Certain Problems of Mathematical Control Theory, 233 p. Publishing House of Foreign Literature (1962)Google Scholar
  19. 19.
    Weijia, D., Ginger, Z., Jungmin, L.: Optimal aggregation of consumer ratings. NBER Working Paper No. 18567, pp. 12–23Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Vinnytsia National Technical UniversityVinnytsiaUkraine
  2. 2.Lviv Polytechnic National UniversityLvivUkraine

Personalised recommendations