DECISION MAKING ON WEB PROJECT MANAGEMENT UNDER UNCERTAINTY
The relevance of the article is due to the fact that the methods and tools used for web project management today do not take into account: the impact of human factors on project management processes and its implementation at all stages of the web project life cycle and the presence of uncertainties in data used in adoption design solutions for web project management. Describes the methods and procedures for the formation of project solutions in the management of web projects in the case of incompleteness and inaccuracy of some characteristics of the project. The main factors of project decision-making are analyzed, the causes and nature of incompleteness and inaccuracy of design characteristics are determined, in connection with which the concept of uncertainty was introduced, the most common types of uncertainties that arise when managing web projects are described. The main ways to solve the problem of web project management taking into account the absence, incompleteness and / or inaccuracy of project data, each of which can be implemented by three methods. To solve this problem, an approach to the application of project specifications has been developed, which creates opportunities for their proper use in project management processes, reducing risks and ensuring the efficiency, quality and reliability of solutions. Procedures for reducing the level of incompleteness and inaccuracy of project characteristics based on fuzzy logic, the use of which involves the replacement of quantitative values by ambiguous linguistic estimates, which reflect the semantics of such estimates and the relationship between values, allowing the transition from absolute values to relative estimates in project decisions , as well as to eliminate the incompleteness and inaccuracy of the values of the calculated characteristics, to form a homogeneous system of measuring various decision-making factors for the web project. The proposed approach makes it possible to create tools for dealing with uncertainty and create a decision-making system for managing web projects.
2. Lande, D. Furashev, V., Braychevskiy, S., Grigor’yev, O. (2006). Osnovy modelirovaniya i otsenki yelektronnykh informatsionnykh potokov [Fundamentals of modeling and evaluation of electronic information flows]. Kií̈v: Ínzhiníring [in Russian].
3. Lande, D. (2006). Osnovy integratsii informatsionnykh potokov [Fundamentals of information flow integration]. Kií̈v: Ínzhiníring [in Russian].
4. Scalable Data Quality: A Seven Step Plan For Any Size Organization (2007). Melissadata Inc. White Papers. Retrieved from http://www.melissadata.com/dqt/whitepaper/scalable-data-quality-whitepaper.pdf.
5. Howell, G., Laufer, A., Ballard, G. (1993). Uncertainty and project objectives. Project Appraisal, 1, 37–43 (Vol. 8).
6. Deyt, K. Dzh. (2005). Vvedeniye v sistemy baz dannykh [Introduction to Database Systems] (12nd ed., Trans). Moskva: Izdatel’skiy dom «Vil’yams» [in Russian].
7. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8, 338–353 (Vol. 3).
8. Osgood, C. E. (1992). The nature and measurement of meaning. Psychological bulletin, 3, 197 (Vol. 49).
9. Chandrasekaran, S., Golub, G., Gu, M., Sayed, A. (1998). Parameter estimation in the presence of bounded data uncertainties. SIAM Journal on Matrix Analysis and Applications, 1, 235–252 (Vol. 19).
10. Zade, L. A. (2001). Rol’ myagkikh vychisleniy i nechetkoy logiki v ponimanii, konstruirovanii i razvitii informatsionnykh / intellektual’nykh sistem [The role of soft computing and fuzzy logic in understanding, design and development of information / intelligent systems] (Trans). Novosti Iskusstvennogo Intellekta, 2 (3), 7–11 [in Russian].