Support of Decision-Making in IT-project Risk Assessment under Z-numbers

Student: Goleva Ekaterina

Educational Programme: System and Software Engineering (Master)

Year of Graduation: 2016

Many IT-projects fail or face with challenges. Reducing number of such projects requires analysis and managing risks before they occurred. The accuracy of the analysis depends on the converting assessments given by experts without strong deviation from the initial expert assessment and with taking into consideration all available information. Fuzzy Analytical Hierarchy Process (Fuzzy AHP) was chosen as a method for risk assessment. It allows comparing risks against set of criteria and assigning to them priorities. Risks priorities will help to understand which risks have to be prevented in the first turn. In addition, assessing risks by Fuzzy AHP is suitable for the projects and companies with different scope and area. The approach based on Z-numbers to interpret expert judgment into the numerical form was chosen for expressing expert judgments in the convenient for the analysis form. Fuzzy AHP is using fuzzy numbers for calculations, whereas the expert judgment expressed in the form of Z-number consists of two fuzzy sets, none of those cannot be excluded from the analysis. For that reason, every Z-number has to be converted into a single fuzzy set. Methods of cognitive psychology, namely, prototype and exemplar models of categorization were proposed for use to convert Z-number into regular fuzzy set. Object of the study is converting of Z-number into convenient for risk analysis form (into fuzzy set). This study aims to provide approach to decision-making in IT-projects by ranging existing risks on the base of obtained expert judgments expressed as statements in natural language. Results of the study cover methods of converting Z-number into a regular fuzzy set by utilizing both prototype and exemplar approaches well-known in the field of cognitive psychology and Window Forms application allowing to provide visualization of calculations used in conversion mentioned above Keywords: Z-number, fuzzy set, prototype and exemplar models of categorization, Fuzzy AHP

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