How to cite this paper
Zapukhliak, I., Zaiachuk, Y., Polyanska, A & Kinash, I. (2019). Applying fuzzy logic to assessment of enterprise readiness for changes.Management Science Letters , 9(13), 2277-2290.
Refrences
Buckley, J. J., Eslami, E., & Feuring, T. (2013). Fuzzy mathematics in economics and engineer-ing (Vol. 91). Physica.
Carlsson, C., Fedrizzi, M., & Fullér, R. (2004). Group decision support systems. In Fuzzy Logic in Management (pp. 57-125). Springer, Boston, MA.
Cohen, D. (2007). The essence of change: a guidebook. Tools and tactics of managing transfor-mations in the company. Moscow: Olympus Business, 320 p.
Cummings, T. & Worley, C. (2004). Organization development and change. Ohio: South-Western College Publishers.
Gen, M., Cheng, R.-John Wiley & Sons. (1997). Algorithms and Engineering Design, New York: John Wiley & Sons, 352 p.
Gusieva, O. (2013). Conceptual principles and applied aspects of complex assessment of enterprise readiness for change. Current Problems of the Economy, 7(145), 72-80.
Kantamara, P. & Ractham, V. (2014). Single-loop vs. double-loop learning: an obstacle or success fac-tor for organizational learning: International Journal of Education and Research, 2(7), 55-62
Katel'nikov, D. (1998). Development of the method for identifying nonlinear objects for decision making based on fuzzy logic: author's abstract of dissertation, Kyiv, 16 p.
Keller, A. A. (2010). Fuzzy Conflict Games in Economics and Management: single objective fuzzy bi-matrix games. Contributions to Game Theory and Management, 3(0), 192-219.
Kotter, J. & Cohen, D. (2002). The heart of change: Real-life stories of how people change their or-ganizations. Boston: Harvard Business School Press.
Lootsma, F. (1997). Fuzzy Logic for Planning and Decision Making: Springer, 198 p.
Peters, T. & Waterman, R. (1982). In search of Excellence. New York, NY: Harper & Row.
Polyanska, A. (2012). Development of industrial enterprises on the basis of situational management: theory and methodology: Scientific dissertation, Ivano-Frankivsk.
Rotshtein, A. P., & Katel’nikov, D. I. (1998). Identification of nonlinear objects by fuzzy knowledge bases. Cybernetics and Systems Analysis, 34(5), 676-683.
Rothstein, A. (1996). Medical diagnostics on fuzzy logic, Vinnitsa: Continent-PRIM, 132 p.
Rothstein, A. (1999). Intelligent Identification Technologies: Fuzzy Logic, Genetic Algorithms, Neu-ral Networks, Vinnitsa: UNIVERSUM-Vinnitsa, 320 p.
Rothstein, O., Lariushkin, E. & Mytiushkin, Yu. (2008). Soft Computing in Biology: Multivariate Analysis and Diagnostics: Monograph, Vinnytsia: UNIVERSUM-Vinnytsia, 144 p.
Rutkovskaya, D., Pylinsky, M. & Rutkovskii L. (2004). Neural networks, genetic algorithms and fuzzy systems. Moscow: Hotline, 452 p.
Sementsov, G. & Fadeeva, O. (2005). Method of the choice of the number of terms for the fuzzy de-scription of the basic variables in the F-transformation of the parameters and indicators of the well drilling process. Bulletin of the Khmelnitsky National University, 1, pp. 30-35.
Shang, K. & Hossen, Z. (2013). Applying Fuzzy Logic to Risk Assessment and Decision Making Cas-ualty Actuarial Society, The Canadian Institute of Actuaries, Society of Actuaries, pp. 3-59.
Tsypkin, Ya. (1984). Fundamentals of Information. Theory of Identification. Moscow: Science, 320 p.
Vikhansky, O. & Naumov, A. (2006). Management, 4th ed., Moscow: Economist, 612 p.
Zade, L. (1976). The concept of a linguistic variable and its application to the adoption of approximate solutions, Moscow: World, 165 p.
Zadeh, L. (1988). Fuzzy Logic. IEEE Computer, April, pp. 83-93.
Zaiachuk, Y. (2009). Optimal control of gas-pumping units of compressor stations, taking into account their technical condition: Abstract of dissertation, Ivano-Frankivsk, 19 p.
Zapukhliak, I. (2017). Theoretical and methodological bases of development of gas transportation en-terprises in conditions of instability of the environment of their functioning: Scientific dissertation, Ivano-Frankivsk.
Carlsson, C., Fedrizzi, M., & Fullér, R. (2004). Group decision support systems. In Fuzzy Logic in Management (pp. 57-125). Springer, Boston, MA.
Cohen, D. (2007). The essence of change: a guidebook. Tools and tactics of managing transfor-mations in the company. Moscow: Olympus Business, 320 p.
Cummings, T. & Worley, C. (2004). Organization development and change. Ohio: South-Western College Publishers.
Gen, M., Cheng, R.-John Wiley & Sons. (1997). Algorithms and Engineering Design, New York: John Wiley & Sons, 352 p.
Gusieva, O. (2013). Conceptual principles and applied aspects of complex assessment of enterprise readiness for change. Current Problems of the Economy, 7(145), 72-80.
Kantamara, P. & Ractham, V. (2014). Single-loop vs. double-loop learning: an obstacle or success fac-tor for organizational learning: International Journal of Education and Research, 2(7), 55-62
Katel'nikov, D. (1998). Development of the method for identifying nonlinear objects for decision making based on fuzzy logic: author's abstract of dissertation, Kyiv, 16 p.
Keller, A. A. (2010). Fuzzy Conflict Games in Economics and Management: single objective fuzzy bi-matrix games. Contributions to Game Theory and Management, 3(0), 192-219.
Kotter, J. & Cohen, D. (2002). The heart of change: Real-life stories of how people change their or-ganizations. Boston: Harvard Business School Press.
Lootsma, F. (1997). Fuzzy Logic for Planning and Decision Making: Springer, 198 p.
Peters, T. & Waterman, R. (1982). In search of Excellence. New York, NY: Harper & Row.
Polyanska, A. (2012). Development of industrial enterprises on the basis of situational management: theory and methodology: Scientific dissertation, Ivano-Frankivsk.
Rotshtein, A. P., & Katel’nikov, D. I. (1998). Identification of nonlinear objects by fuzzy knowledge bases. Cybernetics and Systems Analysis, 34(5), 676-683.
Rothstein, A. (1996). Medical diagnostics on fuzzy logic, Vinnitsa: Continent-PRIM, 132 p.
Rothstein, A. (1999). Intelligent Identification Technologies: Fuzzy Logic, Genetic Algorithms, Neu-ral Networks, Vinnitsa: UNIVERSUM-Vinnitsa, 320 p.
Rothstein, O., Lariushkin, E. & Mytiushkin, Yu. (2008). Soft Computing in Biology: Multivariate Analysis and Diagnostics: Monograph, Vinnytsia: UNIVERSUM-Vinnytsia, 144 p.
Rutkovskaya, D., Pylinsky, M. & Rutkovskii L. (2004). Neural networks, genetic algorithms and fuzzy systems. Moscow: Hotline, 452 p.
Sementsov, G. & Fadeeva, O. (2005). Method of the choice of the number of terms for the fuzzy de-scription of the basic variables in the F-transformation of the parameters and indicators of the well drilling process. Bulletin of the Khmelnitsky National University, 1, pp. 30-35.
Shang, K. & Hossen, Z. (2013). Applying Fuzzy Logic to Risk Assessment and Decision Making Cas-ualty Actuarial Society, The Canadian Institute of Actuaries, Society of Actuaries, pp. 3-59.
Tsypkin, Ya. (1984). Fundamentals of Information. Theory of Identification. Moscow: Science, 320 p.
Vikhansky, O. & Naumov, A. (2006). Management, 4th ed., Moscow: Economist, 612 p.
Zade, L. (1976). The concept of a linguistic variable and its application to the adoption of approximate solutions, Moscow: World, 165 p.
Zadeh, L. (1988). Fuzzy Logic. IEEE Computer, April, pp. 83-93.
Zaiachuk, Y. (2009). Optimal control of gas-pumping units of compressor stations, taking into account their technical condition: Abstract of dissertation, Ivano-Frankivsk, 19 p.
Zapukhliak, I. (2017). Theoretical and methodological bases of development of gas transportation en-terprises in conditions of instability of the environment of their functioning: Scientific dissertation, Ivano-Frankivsk.