Target Programming with Multicriterial Restrictions Application to the Defense Budget Optimization

Authors

  • O.V. Barabash State University of Telecommunications, Kyiv, Ukraine
  • P. Open`ko Ivan Cherniakhovskyi National Defense University of Ukraine, Kyiv, Ukraine
  • O.V. Kopiika Institute of Telecommunications and Global Information Space, Kyiv, Ukraine
  • H.V. Shevchenko State University of Telecommunications, Kyiv, Ukraine
  • N.B. Dakhno State University of Telecommunications, Kyiv, Ukraine

DOI:

https://doi.org/10.3849/aimt.01291

Keywords:

method of leading priorities, target function, utility function, targeting informational technology, weighting factors

Abstract

The analysis of the main factors of decision-making on media planning, methods of automation the advertising targeting and existing models and algorithms discovered, the contradictions between the possibilities of traditional methods and classical models of advertising budget distribution. The requirements to automate decision-making support in managing the advertising process became important when organizing and conducting public Purchases for the needs of the Armed Forces of Ukraine. In order to increase the efficiency of defense resources management for rational advertising budget distribution between different types of advertising platforms, an advanced mathematical model for making decisions on the application of targeting informational technology to advertising has been developed.

Author Biography

  • P. Open`ko, Ivan Cherniakhovskyi National Defense University of Ukraine, Kyiv, Ukraine

    Candidate of Technical Sciences (Ph.D. in Technical Sciences)

References

SMIT, E.G., VAN NOORT, G. and VOORVELD, H.A. Understanding Online Behavioral Advertising: User Knowledge, Privacy Concerns and Online Coping Behavior in Europe. Computers in Human Behavior, 2014, vol. 32, no. 1, p. 15-22. https://doi.org/10.1016/j.chb.2013.11.008.

SCHUMANN, J.H., VON WANGENHEIM, F. and GROENE, N. Targeted Online Advertising: Using Reciprocity Appeals to Increase Acceptance among Users of Free Web Services. Journal of Marketing, 2014, vol. 78, no. 1, p. 59-75. https://doi.org/10.1509/jm.11.0316.

MOORE, R.S., MOORE, M.L., SHANAHAN, K.J., HORKY, A. and MACK, B. Creepy Marketing: Three Dimensions of Perceived Excessive Online Privacy Violation. Marketing Management, 2015, vol. 25, no. 1, p. 42-53. ISSN 1534-973X.

HAM, C.-D. and NELSON, M.R. The Role of Persuasion Knowledge, Assessment of Benefit and Harm, and Third-Person Perception in Coping with Online Behavioral Advertising, Computers in Human Behavior, 2016, vol. 62, no. 1, p. 689-702. https://doi.org/10.1016/j.chb.2016.03.076.

BARABASH, O.V., DAKHNO, N.B., SHEVCHENKO, H.V., MUSIENKO, A.P. and NESHCHERET, O.S. Information Technology of Targeting: Optimization of Decision Making Process in a Competitive Environment. International Journal of Intelligent Systems and Applications, 2017, vol. 9, no. 12, p. 1-9. https://doi.org/10.5815/ijisa.2017.12.01.

BARABASH, O.V. and SHEVCHENKO, G.V. Methodology for Assessing the Effectiveness of Decision-Making on the Application of Information Technology Targeting In Conditions of Competition and Incomplete Awareness. Collection of Scientific Works of the Military Institute of Kyiv Taras Shevchenko National University, 2017, vol. 57, p. 192-203. ISSN 2524-0056.

HASSAN, N. and HALIM, B.A. Mathematical Modelling Approach to the Management of Recreational Tourism Activities at Wetland Putrajaya. Sains Malaysiana, 2012, vol. 41, no. 9, p. 1155-1161. ISSN 0126-6039.

HASSAN, N., SIEW, L.W. and SHEN, S.Y. Portfolio Decision Analysis with Maximin Criterion in the Malaysian Stock Market. Applied Mathematical Sciences, 2012, vol. 6, no. 109-112, p. 5483-5486. ISSN 1312-885X.

NASH J.F. Noncooperative Games. Annals of Mathematics, 1951, vol. 54, p. 286-298. https://doi.org/10.2307/1969529.

KOVACOVA, M., KUBALA, P., KLESTIK, T. and VALASKOVA, K. Bankruptcy Models: Verifying their Validity as a Predictor of Corporate Failure. Polish Journal of Management Studies, 2018, vol. 18, no. 1, p. 167-179. https://doi.org/10.17512/pjms.2018.18.1.13.

RUPESH, K.P., PREM, V. and PRADEEP, K.A Goal Programming Model for Paper Recycling System. Omega, 2008, vol. 36, no. 3, p. 405-417. https://doi.org/10.1016/j.omega.2006.04.014.

HASSAN, N., PAZIL, A.H.M., IDRIS, N.S. and RAZMAN, N.F. A Goal Programming Model for Bakery Production. Advances in Environmental Biology, 2013, vol. 7, no. 1, p. 187-190. https://doi.org/10.12988/ams.2013.310574.

MUKHIN, V., LOUTSKII, H., BARABASH, O., KORNAGA, Y. and STESHYN, V. Models for Analysis and Prognostication of the Indicators of the Distributed Computer Systems Characteristics. International Review on Computers and Software, 2015, vol. 10, no. 12, p. 1216-1224. https://doi.org/10.15866/irecos.v10i12.8023.

CHENG, S.K., ELKMEL, A. and NILAY, S. Optimization Methods for Petroleum Fields Development and Production Systems: a Review. Optimization and Engineering, 2017, vol. 12, no. 1, p. 1-35. https://doi.org/10.1007/s11081-017-9365-2.

SHEVCHENKO, G. Decision Theory for Optimal Design of Advertising Company with Target Audience Maximization. In Zborník príspevkov z medzinárodné vedecko-odborné konferencie «Riadenie bezpečnosti zložitých systémov 2015». Liptovský Mikuláš: Akadémia ozbrojených síl gen. M. R. Štefánika, 2015, p. 323-331. ISBN 978-80-8040-506-9.

KLIESTIK, T., MISANKOVA, M. and BARTOSOVA, V. Application of Multi Criteria Goal Programming Approach for Management of the Company. Applied Mathematical Sciences, 2015, vol. 9, no. 115, p. 5715-5727. https://doi.org/10.12988/ams.2015.57488.

UMARUSMAN, N. Min-Max Goal Programming Approach for Solving Multi‐Objective De Novo Programming Problems. International Journal of Operations Research, 2013, vol. 10, n. 2, p. 92-99. ISSN 1813-713X.

DODONOV, A.G., LANDE, D.V., PRISHCHEPA, V.V. and PUTIATIN, V.G. Competitive Intelligence of Computer Networks (in Russian). Kyiv: Institute for Information Recording National Academy of Science of Ukraine, 2013, 250 p. ISBN 978-966-00-1087-1.

MASHKOV, V., BARILLA, J. and SIMR, P. Applying Petri Nets to Modeling of Many-Core Processor Self-Testing when Tests are Performed Randomly. Journal of Electronic Testing Theory and Applications, 2013, vol. 29, no. 1, p. 25-34. https://doi.org/10.1007/s10836-012-5346-8.

BLEIER, A. and EISENBEISS, M. The Importance of Trust for Personalized Online Advertising. Journal of Retailing, 2015, vol. 91, no. 3, p. 390-409. https://doi.org/10.1016/j.jretai.2015.04.001.

RAUVERS, F., REMMELSWAAL, P., FRANSEN, M.L., DAHLEN, M. and VAN NOORT, G. The Impact of Creative Media Advertising on Consumer Responses: Two Field Experiments. International Journal of Advertising, 2018, vol. 37, no. 5, p. 749-768. https://doi.org/10.1080/02650487.2018.1480167.

PASHYNSKA, N., SNYTYUK, V., PUTRENKO, V. and MUSIENKO, A. A Decision Tree in a Classification of Fire Hazard Factors. Еastern-European Journal of Enterprise Technologies, 2016, vol. 5, no. 10, p. 32-37. https://doi.org/10.15587/1729-4061.2016.79868.

BEKESIENE, S. and HOSKOVA-MAYEROVA, S. Decision Tree-Based Classification Model for Identification of Effective Leadership Indicators. Journal of Mathematical and Fundamental Science, 2018, vol. 50, no. 2, p. 121-141. https://doi.org/10.5614/j.math.fund.sci.2018.50.2.2.

LEE, S., LEE, Y., Lee, JOING, I. and PARK, J. Personalized e-Services: Consumer Privacy Concern and Information Sharing. Social Behavior and Personality, 2015, vol. 43, no. 5, p. 729-740. https://doi.org/10.2224/sbp.2015.43.5.729.

AGUIRRE, E., MAHR, D., GREWAL, D., DE RUYTER, K. and WETZELS, M. Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness. Journal of Retailing, 2015. vol. 91, no. 1, p. 34-49. https://doi.org/10.1016/j.jretai.2014.09.005.

Downloads

Published

01-07-2019

Issue

Section

Research Paper

Categories

How to Cite

Barabash, O., Open`ko, P., Kopiika, O., Shevchenko, H., & Dakhno, N. (2019). Target Programming with Multicriterial Restrictions Application to the Defense Budget Optimization. Advances in Military Technology, 14(2), 213-229. https://doi.org/10.3849/aimt.01291

Similar Articles

31-40 of 236

You may also start an advanced similarity search for this article.