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http://dspace.wunu.edu.ua/handle/316497/23910| Назва: | Fuzzy-multiple Approach in Choosing the Optimal Term for Implementing the Innovative Project |
| Автори: | Sachenko, Svitlana Chereshnyuk, Oksana Panasyuk, Valentyna Golyash, Iryna Banasik, Arkadiusz |
| Ключові слова: | innovative project; fuzzy model; management; component; asymmetry coefficient; variation coefficient of asymmetry |
| Дата публікації: | 2017 |
| Видавництво: | Proceedings of 8th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Bucharest, Romania |
| Бібліографічний опис: | Exploring Strategic Risk. https://www2.deloitte.com/ua/en/pages/ governance-risk-and-compliance/articles/exploring-strategic-risk. html. Last accessed 15.02.2017. [2] G. Kendall, S. Rollins, “Advanced project portfolio management and the PMO: Multiplying ROI at warp speed,” J. Ross Publishing, pp. 69–79, 2003. [3] P. Kossakowski, P. Bilski, “Analysis of the self-organizing mapbased investment strategy,” International Journal of Computing, vol. 16, issue 1, pp. 10-17, 2017. [4] N. Furr, J. Dyer, “Choose the right innovation method at the right time,” Harvard Business Review, 2014, [Online]. Available: https://hbr.org/2014/12/choose-the-right-innovation-method-at-the -right-time. [5] A. Paolini, M. Glaser, “Project selection methods that pick winners,” Research Management, pp. 26-29, 2016. [6] M. Coffin, B. Taylor, “Multiple criteria R&D project selection and scheduling using fuzzy logic,” Computers & Operations Research, vol. 23, issue 3, pp. 207–220, 1996. [7] L. Meade, A. Presley, “R&D project selection using the analytic network process,” IEEE Transactions on Engineering Management, vol. 49, issue 49, pp. 59–66, 2002. [8] M. Mian, Project Economics and Decision Analysis: Probabilistic Models, PennWell Books, 2011, pp. 66–67. [9] J. V. Bezdek, “Using fuzzy logic in business,” Procedia – Social and Behavioral Sciences, vol. 124, pp. 371–380, 2014. [10] K. Shang, Z. Hossen, Applying Fuzzy Logic to Risk Assessment and Decision Making, Casualty Actuarial Society, Canadian Institute of Actuaries, Society of Actuaries, 2013, 59 p. [11] T. Lendyuk, S. Sachenko, S. Rippa, G. Sapojnyk, “Fuzzy rules for tests complexity changing for individual learning path construction,” Proceedings of the 8th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2015), Warsaw, Poland, 24-26 September 2015, pp. 945-948. [12] R. Doskocil, “An evaluation of total project risk based on fuzzy logic,” Business: Theory and Practice, vol. 17, pp. 23–31, 2016. [13] S. Fraser, M. Bojadziev, Fuzzy Logic for Business, Finance, and Management, World Scientific, 2007, 233 p. [14] A. S. Ptuskin, Fuzzy Models and Methods in Management, Moscow, MSTU them. Bauman, 2008, 216 p. (in Russian) [15] L. Machacha, P. Bhattacharya, “A fuzzy-logic-based approach to project selection,” IEEE Transactions on Engineering Management, vol. 47, issue 1, pp. 65–73, 2000. [16] Y. P. Kondratenko, Ie. V. Sidenko, “Decision-making based on fuzzy estimation of quality level for cargo delivery,” in book: Recent Developments and New Directions in Soft Computing. Studies in Fuzziness and Soft Computing, L. A. Zadeh, et al. (Eds), Springer International Publishing Switzerland, pp. 331–344, 2014. [17] T. J. Ross, Fuzzy Logic with Engineering Applications, McGraw- Hill Inc., 1995, pp. 90–112. [18] S. Shtovba, Introduction to the Theory of Fuzzy Sets and Fuzzy Logic, [Online]. Available: http://matlab.exponenta.ru/ fuzzylogic/book1/. [19] G. Boshnakov, Some Measures for Asymmetry of Distributions, Mathematics Department University of Manchester Institute of Science and Technology, 2006, pp. 2–8. 536 |
| Короткий огляд (реферат): | A paper is devoted to the management of innovative projects based on the theory of fuzzy sets. To analyze project risks based on the terms of their implementation - acceleration, according to the plan and with the delay, authors proposed to employ both the asymmetry coefficient and the coefficient of asymmetry variation which are characterizing the asymmetric distribution of costs due to changes in the time interval of project implementation. On the basis of those coefficients above the fuzzy model was designed taking into account the duration of project implementation and the risks associated with the emergence of additional costs. Values of both coefficients are assigned in such way to meet the low, medium and high level of their performance. Fuzzy rules for assessing the feasibility of implementing the innovative project are developed. It’s experimentally proofed the proposed fuzzy model gives a result that enables to get the optimal implementation period of the innovative project. |
| URI (Уніфікований ідентифікатор ресурсу): | http://dspace.tneu.edu.ua/handle/316497/23910 |
| Розташовується у зібраннях: | Статті |
Файли цього матеріалу:
| Файл | Опис | Розмір | Формат | |
|---|---|---|---|---|
| IDAACS_2017_VOL_1.pdf | 149.98 kB | Adobe PDF | Переглянути/Відкрити |
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