Renewable Energy Transition Task Solution for the Oil Countries Using Scenario-Driven Fuzzy Multiple-Criteria Decision-Making Models: The Case of Azerbaijan

The renewable energy transition of oil- and gas-producing countries has specific peculiarities due to the ambivalent position of these countries in the global energy market, both as producers and consumers of energy resources. This task becomes even more challenging when the share of oil and gas in the country’s GDP is very high. These circumstances pose serious challenges for longterm energy policy development and require compromising decisions to better align the existing and newly created energy policies of the country. The scale, scope, and pace of changes in the transition process must be well balanced, considering the increasing pressure of economic and environmental factors. The objective of this paper is to develop models that allow the selection of the most appropriate scenario for renewable energy transition in an oil- and gas-producing country. The distinguishing feature of the proposed model is that alternatives in the decision matrix are presented as scenarios, composed of a set of energy resources and the level of their use. Linguistic descriptions of the alternative scenarios are formalized in the form of fuzzy statements. For the problem solution, four different Multiple-Criteria Decision-Making (MCDM) methods were used: the fuzzy simple additive weighting (F-SAW) method, the distance-based fuzzy TOPSIS method (Technique of Order Preference Similarity to the Ideal Solution), the ratio-analysis-based fuzzy MOORA method (Multi- Objective Optimization Model Based on the Ratio Analysis), and the fuzzy multi-criteria optimization and compromise solution method VIKOR (Serbian: VIekriterijumsko Kompromisno Rangiranje). This approach is illustrated using the example of the energy sector of Azerbaijan. The recommended solution for the country involves increasing natural gas (NG) moderately, maintaining hydro, and increasing solar notably and wind moderately

Selection of Renewables for Economic Regions with Diverse Conditions: The Case of Azerbaijan

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 renewable

Multicriteria Decision-Making Under High-Level Uncertainty In Tourism: Z-Numbers Based Approaches

The objective of this paper is to study the applicability and effectiveness of the decision-making models in the tourism sector under high-level uncertainty, formalized by Z-information. The topicality of this issue is significantly increased after the outbreak of the pandemic. Fuzzy multi-criteria decision-making (MCDM) models applied in the tourism area are partially solving this problem. But in these models, researchers are not paying due attention to the reliability of the information. One approach available for the formalization of such high-level uncertainty is the use of bi-component Z-number = (A, B). Components of the Z-numbers are expressed by perception-based fuzzy numbers. Part A defines the value of the uncertain variable and part B defines the confidence in this value. This approach allows considering the fuzzy-probabilistic nature of the information used for decision-making in tourism. In the paper, we are describing in detail the Z-numbers-based approach for the tourism destination selection task solution under high-level uncertainty. The model has been developed for the water sports tourism destination selection in Turkey. Initial information for model construction was derived via surveys. For the solution of this task, the Z-TOPSIS method is used. Results of the task solution illustrate the efficiency of the Z-numbers-based model for destination selection and the applicability of the approach for other MCDM tasks in tourism

Selection Of The Hotel Suppliers Under High-Level Uncertainty

The objective of this paper is to develop a model for supplier selection under high-level uncertainty in the decision-making environment. The pandemic seriously affected the economic well-being of the hospitality industry, decreased travel and tourists` numbers, undermined hospitality service delivery systems and their financial stability. Business structures and relationships, developed in the industry during several decades of stability, have been destroyed fully or partially, and service quality is deteriorating. One of the consequences of the pandemic is supply chains disruptions, caused by the inabilities of suppliers to provide services according to customer requirements. Given that, it is necessary to solve the problem of supplier selection for the tourism sector taking into consideration specifics of the pandemic and post-pandemic conditions. During pandemic and post-pandemic recovery, internal and external environments of the business tasks are characterized by the high-level of uncertainty, insufficiency, and subjectivity of the available information. Supply chain management task is a classic example of such tasks, and it is necessary to develop an approach that can operate with uncertainties and subjectivity of various nature, inherent to this decision-making problem. In such circumstances, traditional probabilistic or fuzzy methods may not always be relevant for formalizing uncertainties, and the use of perception-based dual-natured (fuzzy & probabilistic) Z-numbers may be more appropriate. Z-number-based multi-criteria decision-making (MCDM) method ZVIKOR was used to select alternatives (suppliers) for the hotels. The criteria for the supplier selection were determined by Delphi analysis. The supplier selection task is solved on the example of hotels in Turkey and Azerbaijan. Results of the research illustrate the applicability of the approach for solving MCDM problems in the tourism sector under conditions of highlevel uncertainty

Z-Numbers-Based Approach to Hotel Service Quality Assessment

In this study, we are analyzing the possibility of using Z-numbers for measuring the service quality and decision-making for quality improvement in the hotel industry. Techniques used for these purposes are based on consumer evalu- ations - expectations and perceptions. As a rule, these evaluations are expressed in crisp numbers (Likert scale) or fuzzy estimates. However, descriptions of the respondent opinions based on crisp or fuzzy numbers formalism not in all cases are relevant. The existing methods do not take into account the degree of con- fidence of respondents in their assessments. A fuzzy approach better describes the uncertainties associated with human perceptions and expectations. Linguis- tic values are more acceptable than crisp numbers. To consider the subjective natures of both service quality estimates and confidence degree in them, the two- component Z-numbers Z = (A, B) were used. Z-numbers express more adequately the opinion of consumers. The proposed and computationally efficient approach (Z-SERVQUAL, Z-IPA) allows to determine the quality of services and iden- tify the factors that required improvement and the areas for further development. The suggested method was applied to evaluate the service quality in small and medium-sized hotels in Turkey and Azerbaijan, illustrated by the example

Selection of Renewables for Economic Regions with Diverse Conditions: The Case of Azerbaijan

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