At every phase of the project management process various risks originate owing to occurrence of uncertain events. In this research, we are analyzing potentialities of the Z-numbers in improving the quality of risk assessment. Risk assessment uses probability theory, theory of possibilities, fuzzy approach, Z-number based approach etc. Combined risk measure based on probability and consequence is calculated by applying the disjoint events probability formula or as a product of events. Reliability of relevant information unaccounted in this approach and this circumstance limits the descriptive power of the approach. Suggested by L. Zade a bicomponent Z-number Z = (A, B) represents in a unified way a restriction on the values of the uncertain variable (A) and its certainty (B) and allows to take into account the reliability of information. Prediction identical to (High, Very Sure) can be formalized as a Z-evaluation "X is Z (A,B)", where X is random variable of Risk Likelihood, A and B are fuzzy sets, describing soft constraints on a risk likelihood and a partial reliability, respectively. Usually, A and B are sense-based and in effect are imprecise.Z-number describes a probability of threat as: Likelihood =Z1(High,Very Sure),where A is expressed by linguistic terms High, Medium, Low, and B is expressed by terms Very Sure, Sure and so on. Similarly, Сonsequence measure is described as Сonsequence measure = Z2 (Low, Sure).Risk levels (Z12) is calculated as the product of the likelihood (Z1) and consequence measures (Z2).Effectiveness of the approach illustrated by examples. A general and computationally effective approach suggested to computation with Z-numbers allows using Z-information for the solving decision-making problem which can be utilized for risk factors estimation.. 1 Application of the Z-number based approach for a project risks assessment increases adequacy of the risks representation due to better approximation of the combined effects

The growing importance of the tourism sector to the global economy contributes to the increase of research in tourism risks assessment. In view of this tendency, the results of research in the field of the risk analysis on tourists’ travels in various countries during the last decades have been analyzed. Commonly used in these studies statistical methods allow to reveal and identify country-specific tourism risks and threats. But it is necessary to underline that relevant statistical data on risks are available not in all cases and countries. Moreover, in most cases, the reliability of the information available is questionable. In order to improve the reliability and quality of the tourist risk assessment, it is proposed to consider tourist travel as a project. The proposed project approach to tourist risk analysis provides an opportunity to go beyond assessment based on available country-specific inferior statistical data and allows to develop a more flexible and versatile method for risk evaluation. Common risk factors and sub-factors for tourists were identified for further risk assessment using suggested by L. Zadeh Z-number. A bi-component Z-number Z = (A, B) with perception-based and imprecise parts A and B, allows taking into account the reliability of the information. Risk experts deal with the prediction like this one “very likely that the level of threat N is medium” or “extremely likely that this factor is very important”. This prediction can be formalized as a Z-number based evaluation and a pack of Z-valuations is considered as Z-information. Experts evaluate identified risk factors and sub-factors and their importance weight using Z-numbers.

Background: One of the vital issues in promoting the sustainable tourism industry in developing countries, including Azerbaijan, is the well-grounded selection of tourism sites. Applying traditional approaches as a solution to this task, does not provide a relevant result in all cases in these countries due to local specifics of the tourism, the incompleteness of statistical data, the high-level uncertainty of the internal and external environment, and the questionable reliability of the available information. Methods: Since the statistical data are limited, and conventional formalization tools used for uncertainty description do not consider the reliability degree of the data, it is suggested to make decisions based on the Z-extension of fuzzy logic. A Delphi panel with the expert group is conducted to obtain the information required for the model development. Fuzzy Z-information-based TOPSIS and PROMETHEE methods are applied for the problem solution. Within these approaches Z-number-based procedures of the decision matrix normalization, defining the distance between solutions and the preference function, and swing weights determination are realized. Direct computations with Z-numbers are implemented.  Results: By applying Z-number-based multi-criteria decision-making methods, five potential regions of Azerbaijan have been evaluated for six criteria. The criteria reflect government policy to the development of the regions, economical, geographical, environmental factors, and infrastructure of the locations. Derived solutions are comparable in sense of sites ranking, and similar results were obtained using both methods. Direct calculations allow obtaining results based on the linguistic Z-evaluations of experts without distorting transformations. Conclusion: The managerial decision-making problems in the tourism sector, raised due to the aforementioned barriers, can be successfully resolved by applying Z-number-based multi-criteria approaches. The obtained results allow increasing a range of the decision-making tasks under a high degree of uncertainty to be solved for sustainable development studies and other areas.

The objective of this paper is to study the specifics of the selection of renewables for regions of Azerbaijan with diverse conditions. Information is obtained through the analysis of the regions’ conditions and experts’ opinions. Analysis reveals that geographical position, diversity of natural resources, and a variety of other factors of the five economic regions of the country require subdivision of these regions in the selection of renewables. Given that the selection of renewables is a multi-criteria decision-making (MCDM) task under a high degree of uncertainty, Z-number-based models have been developed, and Z-extension of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been used. Solutions have been derived based on direct calculations with Z-numbers. In this paper, results obtained for two regions are presented. In the case of one region, for the first part (mountains and foothill) of the Karabakh economic region, renewables are ranked as hydro, solar, and wind. For the second part (plain), the ranking is as follows: solar, hydro, and wind. For the Guba-Khachmaz economic region, the rankings of renewables for parts of the region are also different: the wind is preferable for the seaside, and solar is more appropriate for the foothills. Results show that in the case of uneven distribution of renewables and significant differences in factors influencing decision-making, it is necessary to subdivide economic regions and use different models for the selection of renewables.