Cost management of an innovative chemical project based on fuzzy logical approaches
Беилин И.Л.1, Хоменко В.В.2
1 Казанский национальный исследовательский университет
2 Академия наук Республики Татарстан
Download PDF | Downloads: 16 | Citations: 21
Journal paper
(РИНЦ, ВАК)
опубликовать статью | оформить подписку
Volume 7, Number 4 (October-December 2017)
Please, cite as:
Beilin I.L., Khomenko V.V. Upravlenie sebestoimostyyu innovatsionnogo khimicheskogo proekta na osnove podkhodov nechetkoy logiki // Voprosy innovatsionnoy ekonomiki. – 2017. – Tom 7. – № 4. – S. 437-448. – doi: 10.18334/vinec.7.4.38663.
Indexed in Russian Science Citation Index: https://elibrary.ru/item.asp?id=32311425
Cited: 21 by 31.03.2023
Abstract:
We suggest fuzzy logic approaches to managing the cost of an innovative chemical project. The urgency of development depends on a high degree of uncertainty related to the future economic efficiency of such projects. This involves the difficulties in transferring science-intensive technology from laboratory to production, traditionally high level of competition in the chemical sector and the lack of information about the project economy in the past. The theory of fuzzy sets is primarily related to the quantitative estimation of uncertainty, allows to formalize linguistic uncertainties and apply mathematical operators such as addition, subtraction, multiplication and division in a fuzzy domain. Consequently, a fuzzy number can also be used in economic analysis to replace an unambiguous cost assessment with their fuzzy values. We’ve found out that calculation based on continuous fuzzy numbers is relevant, when there is a need for a quick approximate estimation of the total costs of a large number of innovative projects proposed for investment. In cases when we need a thorough assessment of the total costs of a small number of projects that have passed the preliminary selection, calculations should be based on discrete fuzzy numbers.
Keywords: management, fuzzy logic, cost, innovative chemical project
JEL-classification: O33, O32, С45
References:
Batyrshin S., Kaynak O., Rudas E. (2002). Fuzzy modeling based on generalized conjunction operations IEEE Transactions on Fuzzy Systems. 10 (5). 678-683.
Beilin I.L. (2006). Prikladnye svoystva novyh sopolimerov tsiklicheskikh karbonatov s izotsianatami razlichnogo stroeniya [Applied properties of new copolymers of cyclic carbonates with isocyanates of various structures]. Plasticheskie massy. (4). 19-22. (in Russian).
Beilin I.L. (2012). Otsenka konkurentosposobnosti malogo innovatsionnogo predpriyatiya po FZ 217 [Assessment of the competitiveness of a small innovative enterprise by the Federal Law 217]. Bulletin of Kazan National Research Technological University. 15 (21). 173-174. (in Russian).
Beilin I.L., Arkhireev V.P. (2005). Izuchenie anionnoy sopolimerizatsii propilenkarbonata s izotsianatami [Study of the anionic copolymerization of propylene carbonate with isocyanates]. Bulletin of Kazan National Research Technological University. (1). 369. (in Russian).
Beilin I.L., Arkhireev V.P. (2011). Synthesis and structure of copoly(amide esters) based on cyclic carbonates and monofunctional isocyanates Protection of Metals and Physical Chemistry of Surfaces. 47 (4). 478-483.
Beilin I.L., Arkhireev V.P., Azimov Yu.I. (2006). Novye poliamidoefiry na osnove propilenkarbonata [New propilencarbonate-based polyamidethers]. Izvestiya vysshikh uchebnyh zavedeniy. Seriya: Khimiya i khimicheskaya tekhnologiya. 46 (1). 109. (in Russian).
Beilin I.L., Arkhireev V.P., Nefedova M.A. (2006). Sintez i struktura novyh sopolimerov tsiklicheskikh karbonatov s monoizotsianatami [Copolymerization of cyclic carbonates with isocyanates under anionic initiation conditions and structure of the new copolymers]. Plasticheskie massy. (1). 23-27. (in Russian).
Beilin I.L., Nefedova M.A., Arkhireev V.P. (2006). Anionnaya sopolimerizatsiya tsiklicheskikh karbonatov s monoizotsianitami [Anionic copolymerization of cyclic carbonates with monoisocyanates]. Bulletin of Kazan National Research Technological University. (1). 163-169. (in Russian).
Crowther K.G., Haimes Y.Y. (2010). Development of the Multiregional Inoperability Input-Output Model (MRIIM) for Spatial Explicitness in Preparedness of Interdependent Regions Systems Engineering. 131 (1). 182-201.
Inuiguchi M., Ramik J. (2000). Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem Fuzzy Sets and Systems. (3). 3-28.
Inuiguchi M., Tanino T. (2001). Portfolio selection under independent possibilistic information Fuzzy Sets and Systems. 2 83-92.
Salakhutdinov R.Z., Ismagilov I.I. (2005). Modelirovanie i prinyatie resheniy v ekonomike na osnove teorii nechetkikh mnozhestv [Modeling and decision making in the economy based on the theory of fuzzy sets] Kazan. (in Russian).
Uziel Sandler, Lev Tsitolovsky (2008). Neural Cell Behavior and Fuzzy Logic Springer. 461-478.
Zadeh L. (2002). Toward a perception-based theory of probabilistic reasoning with imprecise probabilities Journal of Statistical Planning and Inference. 233-264.
Zobel C.W., L. Khansa Quantifying (2012). Cyberinfrastructure Resilience against Multi-Event Attacks Decision Sciences. 43 (4). 84-109.
Страница обновлена: 06.12.2023 в 03:04:30