Аннотації

Автор(и):
Yuanyuan Li
Дата публікації:

07.02.2019

Анотація (укр):

Проведено ґрунтовний огляд та порівняльний аналіз відомих багатокритеріальних методів прийняття рішень, які використовуються для задачі ранжування або відбору раціональних альтернатив. Виявлено, що більшість методів враховує умови невизначеності зовнішнього середовища. Оскільки проблема впровадження стратегій диверсифікації, як правило, фінансово затратна, пропонується використовувати для відбору стратегій диверсифікації діяльності будівельних підприємств комплекс методів, які зосереджені на багатокритеріальне прийняття рішень в умовах невизначеності, а також враховують можливу розмитість даних, нескладно реалізуються та дозволяють інтуїтивно інтерпретувати результати (наявні шкали кількісних або якісних оцінок) без постійного залучення експертів або особи, яка приймає рішення.

Анотація (рус):

Анотація (англ):

A thorough review and comparative analysis of well-known multi-criteria decision-making methods used for the task of ranking or selecting rational alternatives has been conducted in this article. It is revealed that most methods take into account the conditions of ambiguity of the environment. Since the problem of implementing diversification strategies is usually financially costly, it is proposed to use for selecting strategies for diversifying the activities of construction enterprises, a set of methods that focus on multicriteria decision-making under uncertainty. It is desirable to take into account the possible blurry of data, easy to implement and allow intuitively interpret the results (available scale of quantitative or qualitative assessments) without the constant involvement of experts or the decision maker.

Література:

References:

  1. Yuanyuan, Li, Biloshchytska, Svitlana. (2019). Diversification of activity as a component of adaptive strategic management of construction enterprise. Management of development of complex systems, 37, 173-177.

2.   Kuchansky, A., Biloshchytskyi, A., Andrashko, Yu., Biloshchytska, S., Shabala, Ye., Myronov, O. (2018). Development of adaptive combined models for predicting time series based on similarity identification. Eastern-European Journal of Enterprise Technologies, 1/4 (91), 32–42. DOI: 10.15587/1729-4061.2018.121620.

3.   Kuchansky A., Biloshchytskyi, A. (2015). Prediction of time series by selective comparison with the sample. Eastern-European Journal of Enterprise Technologies, 6/4 (78), 13-18.

4.   Kuchansky, A., Nikolenko, V., Rachenko, A. (2015). A method for identifying trends in financial time series based on trend models of forecasting. Management of Development of Complex Systems, 24, 84-89.

5.   Kuchansky, A., Nikolenko, V. (2015). Pattern matching method for time-series forecasting. Management of Development of Complex Systems, 22 (1), 101-106.

6.   Berzlev, A. (2013). Methods of pre-forecasting fractal time series analysis. Management of development of complex systems, 16, 76-81.

7.   Berzlev, A. (2013). The current state of information systems of time series forecasting. Management of development of complex systems, 13, 78-82.

8.   Biloshchytskyi, A.A. (2012). Vector method of goal-setting projects in design-vector space. Management of development of complex systems. Kyiv, Ukraine, KNUCA: 11, 110–114.

9.   Kolesnikova, E.V. (2013). Modeling poorly structured project management systems. Bul. Odes. Polytechnic. University., 3(42), 127–131.

10. Rach, V., Rossoshanskaya, O., Medvedeva, O. (2010). Status and Trends in the Development of the Trend Project Management Methodology. Management of the development of complex systems, 3, 118–122.

11. Oganov, A.V. & Gogunsky, V.D. (2013). Use the Theory of Constrains in PMO implementation at the organization.GESJ: Computer Science and Telecommunications, 4(40), 59-65. 

12. Tesla, Yu. (2010). Information technology of project management based on ERPP (enterprise resources planning in project) and APE (administrated projects of the enterprise) systems / Yu.M. Teslya A.O. Beloshchitsky, N.Yu. Tesla// Management of development of complex systems, 1, 16–20.

13. Morozov, V., Kalnichenko, O. & Liubyma, I. (2017) Managing projects configuration in development distributed information systems. 2nd IEEE International Conference on Advances Information and Communication, P.154–157. doi: 10.1109/aiact.2017.8020088

14. Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.

 

15. Khadam, I. & Kaluarachchi, J. (2003). Multi-criteria decision analysis with probabilistic risk assessment for the management of contaminated ground water. Environmental Impact Assessment Review, 23(6), 683-721.

16. Balmat, J., Lafont, F., Maifret, R. & Pessel, N. (2011). A decision-making system to maritime risk assessment. Ocean Engineering, 38(1), 171-176.

17. Haleh, H., Hamidi, A. (2011). A fuzzy MCDM model for allocating orders to suppliers in a supply chain under uncertainty over a multi-period time horizon. Expert Systems with Applications, 38(8), 9076-9083.

18. Esogbue, A., Theologidu, M. & Guo, K. (1992). On the application of fuzzy sets theory to the optimal flood control problem arising in water resources systems. Fuzzy Sets and Systems, 48(2), 155-172.

19. Saaty, T. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill, New York.

20. Velasquez, M., Hester, Patrick. T. (2013). An Analysis of Multi-Criteria Decision Making Methods. International Journal of Operations Research, 10(2), 56–66.

21. Konidari, P. & Mavrakis, D. (2007). A multi-criteria evaluation method for climate change mitigation policy instruments. Energy Policy, 35(12), 6235-6257.

22. Saaty, Thomas L. (1999). Decision Making for Leaders: The Analytic Hierarchy Process for Decisions in a Complex World. Pittsburgh, Pennsylvania: RWS Publications.

23. Okeola, O. & Sule, B. (2012). Evaluation of management alternatives for urban water supply system using Multi-criteria Decision Analysis. Journal of King Saud University – Engineering Sciences, 24(1), 19-24.

24. Ambrasaite, I., Barfod, M. & Salling, K. (2011). MCDA and risk analysis in transport infrastructure appraisals: The Rail Baltica case. Procedia Social and Behavioral Sciences, 20, 944-953.

25. Wang, T. (2012). The interactive trade decision-making research: An application of novel hybrid MCDM model. Economic Modelling, 29(3), 926-935.

26. Fishburn, P. (1967). Conjoint measurement in utility theory with incomplete product sets. Journal of Mathematical Psychology, 4(1), 104-119.

27. Ananda, J. & Herath, G. (2005). Evaluating public risk preferences in forest land-use choices using multi-attribute utility theory. Ecological Economics, 55(3), 408-419.

28. Canbolat, Y., Chelst, K. & Garg, N. (2007). Combining decision tree and MAUT for selecting a country for a global manufacturing facility, Omega. 35(3), 312-325.

29. Gomez-Limon, J., Arriaza, M. & Riesgo, L. (2003). An MCDM analysis of agricultural risk aversion. European Journal of Operational Research, 151(3), 569-585.

30. Kailiponi, P. (2010). Analyzing evacuation decisions using multi-attribute utility theory. Procedia Engineering, 163-174.

31. Loetscher, T. & Keller, J. (2002). A decision support system for selecting sanitation systems in developing countries. Socio-Economic Planning Sciences, 36(4), 267-290.

32. Zabeo, A., Pizzol, L., Agostini, P., Critto, A., Giove, S. & Marcomini, A. (2011). Regional risk assessment for contaminated sites Part 1: Vulnerability assessment by multi-criteria decision analysis. Environment International, 37(8), 1295-1306.

33. Agnar, Aamodt & Enric, Plaza (1994). Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Artificial Intelligence Communications, 1, 39-52.

34. Li, H. & Sun, J. (2008). Ranking-order case-based reasoning for financial distress prediction. Knowledge-Based Systems, 21(8), 868-878.

35. Daengdej, J., Lukose, D. & Murison, R. (1999). Using statistical models and case-based reasoning in claims prediction: experience from a real-world problem. Knowledge-Based Systems, 12(5-6), 239-245.

36. Farrell, M.J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, 120, 253–281.

37. Sickles, R. & Zelenyuk, V. (2019). Measurement of Productivity and Efficiency: Theory and Practice. Cambridge. Cambridge University Press. doi:10.1017/9781139565981

38. Chen, Y., Larbani, M. & Chang, Y. (2009). Multiobjective data envelopment analysis. Journal of the Operational Research Society, 60(11), 1556-1566.

39. Chauhan, N., Mohapatra, P. & Pandey, K. (2006). Improving energy productivity in paddy production through benchmarking-An application of data envelopment analysis. Energy Conversion and Management, 47(9-10), 1063-1085.

40. Kuah, C. & Wong, K. (2011). Efficiency assessment of universities through data envelopment analysis. Procedia Computer Science, 3(2011), 499-506.

41. Qin, X., Huang, G., Chakma, A., Nie, X. & Lin, Q. (2008). A MCDM-based expert system for climate-change impact assessment and adaptation planning – A case study for the Georgia Basin, Canada. Expert Systems with Applications, 34(3),
2164-2179.

42. Figueira, José, Salvatore, Greco, Matthias, Ehrgott. (2005). Multiple Criteria Decision Analysis: State of the Art Surveys. New York. Springer Science + Business Media, Inc. ISBN 0-387-23081-5.