Project risk management is one of the project management knowledge areas that identifies, analyzes and deals with project risks. One of the important factors influencing the decision-making of a project-based organization is the level of risk tolerance of organization. This study focuses on the factors affecting the level of risk tolerance of project-based organizations. For this purpose, in the first step, the potential factors affecting risk tolerance are extracted by reviewing the related literature. In the next step, the factors affecting the organization's risk tolerance level are identified by using the Fuzzy Delphi method in several steps. The most effective factors are identified by experts? judgment using a questionnaire. Then, the relationships between these factors are determined by using the Interpretive Structural Modeling (ISM) method. The intensity of these relationships and the intensity of the effect of the factors are investigated by using the Fuzzy DEMATEL method. Finally, the factors are ranked based on their weights by utilizing the Fuzzy DEMATEL method. In this study, 13 external and internal factors are ranked using questionnaires based on the experts? opinions. Four external factors include political conditions and international relations, the conditions of the capital markets such as stock market, investment security and government support. These factors have significant influence on the other factors as well as the project-based organization. The findings of this study direct project managers to accurately identify the risk tolerance level of the key project stakeholders in order to efficiently plan and implement project risk management and achieve project goals.

Selecting a tractor is one of the most complex investment decisions an agricultural producer faces. There are numerous types of tractors on the market, each differing in technical, economic, and ecological characteristics. The aim of this research is to demonstrate how multi-criteria analysis methods can aid in this decision-making process, using a practical example of selecting an optimal tractor for the Myzeqe area in Albania. In this study, a decision-making model was developed based on a hybrid fuzzy methodology, combining the fuzzy LOPCOW (Logarithmic Percentage Change-Driven Objective Weighting) and fuzzy MABAC (Multi-Attributive Border Approximation Area Comparison) methods. The findings show that the determination of criterion weights is less crucial, with the T15 tractor exhibiting the best overall indicators. This research primarily contributes to developing a methodology in agriculture that enhances production outcomes.

The objective of this study was to provide decision-making assistance in selecting electric vehicles (EVs). The multi-criteria decision-making methods (MCDM), criteria importance through inter-criteria correlation (CRITIC) and evaluation by distance from ideal solution of alternatives (EDISA), along with the technical specifications of EVs, were employed to facilitate the decision on purchasing an EV. A total of 14 minivans were analysed based on 10 criteria. The findings from the CRITIC method indicated that the most significant criteria are battery charging and vehicle consumption. The EDISA method indicated that EV11 exhibited the best characteristics and represented a prudent purchase decision. Nevertheless, the ultimate decision must consider additional factors beyond just the technical specifications, as numerous elements affect the final choice, necessitating an examination of other attributes of the EV.

This paper presents a hybrid multicriteria decision-making (MCDM) model that integrates the fuzzy DIBR II (Defining Interrelationships Between Ranked Criteria II) method with the MABAC (Multi-Attributive Border Approximation Area Comparison). The proposed model addresses the problem of selecting an appropriate flood protection method for Arilje, Republic of Serbia. Flooding in this region results from the overflow of the Veliki Rzav river, which lacks constructed water structures for flood protection. The study considers three alternative flood protection solutions: sand-filled bags, mobile freestanding plastic systems, and mobile freestanding metal systems. The fuzzy DIBR II method was used to define the weighting coefficients of the criteria within a group decision-making framework. Next, the MABAC method was applied to rank the proposed alternatives. Finally, the results were validated through sensitivity analysis and comparative analysis. The validation confirmed that the developed hybrid model produces stable and reliable results.

One of the complex decision-making problems, which requires consideration of several criteria, is the choice of a smartphone. This paper presents an approach that combines user review analysis with machine learning and multi-criteria decision making (MCDM) methods to identify and evaluate alternatives. Based on the processed reviews, the Random Forest algorithm was used to identify the criteria that most influence the selection of smartphones. The weights of the criteria were determined using the Defining Interrelationships Between Ranked criteria II (DIBR II) method, improved by the application of triangular fuzzy numbers for better processing of the subjective and imprecise nature of the data. For the final selection of the optimal alternative, the Weighted Aggregated Sum Product Assessment (WASPAS) method was applied in a fuzzy environment, which enables the combination of additive and multiplicative approaches in ranking. The methodological justification of the proposed approach was confirmed by a sensitivity analysis, through 15 scenarios of changes in the weight coefficients of the criteria, which showed that small oscillations in the weights do not significantly affect the final ranking, especially not in the first two positions. The validation was additionally supported by a comparative analysis with four other decision-making methods in a fuzzy environment, which confirmed the stability and consistency of the results. The proposed approach provides an empirically grounded and methodologically robust framework for solving decision-making problems under conditions of multi-criteria evaluation and uncertainty, and can be applied to a wide range of similar problems in different fields.

Smart technologies are increasingly used in agriculture, with drones becoming one of the key tools in agricultural production. This study aims to evaluate affordable drones for agricultural use in the Posavina region, located in northern Bosnia and Herzegovina. To determine which drones deliver the best results for small and medium-sized farms, ten criteria were used to evaluate eight drones. Through expert evaluation, relevant criteria were first established and then used to assess the drones. The selected drones are designed for crop monitoring and are priced under EUR 2000. Using the fuzzy A-SWARA (Adapted Step-wise Weight Assessment Ratio Analysis) method, it was determined that the most important criteria for drone selection are control precision, flight autonomy, and ease of use, all of which are technical attributes. The fuzzy MARCOS method revealed that the best-performing drones are also the most affordable. The drones D5, D4, and D8 demonstrated the best results. These findings were confirmed through comparative analysis and sensitivity analysis. Their features are not significantly different from those of more expensive models and can, therefore, be effectively used for smart agriculture. This study demonstrates that drones can be a valuable tool for small farms, helping to enhance agricultural practices and productivity.

More and more investments are being made in energy conversion projects from renewable energy sources (RESs), and a large number of investors are entering this sector. The focus of this study is the decision-making by the investor BD Green Energy in the Brčko District of Bosnia and Herzegovina. In order to choose the RES system that would realize this investment in the most efficient way, expert decision-making based on the fuzzy–rough approach and the Bonferroni mean operator was used. Determining the importance of the criteria was conducted using the fuzzy–rough SiWeC (simple weight calculation) method. The results of this method showed that all used criteria have similar importance for the investor. RES system selection was conducted using the fuzzy–rough CoCoSo (combined compromise solution) method. The results of this method showed that investing in photovoltaic (PV) energy is the best for the investor. This research provided guidance on how investors should make investment decisions in RES systems with incomplete information and uncertainty in the decision-making process.

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Abstract Energy production, supply and consumption are global issue with many economic, environmental and social implications. Mentioned issue is even more expressed in remote rural areas, in particular in developing countries, as are the countries of the Western Balkans (WB). Renewable energy sources (RES) could represent optimal energy alternative for sustainable performing of agricultural and other activities, as well as for improving the current state of living conditions in rural communities. The main goal of research is to mark the most suitable RES alternative (six alternatives) for wider implementation in rural space of WB. The applied methodology framework implies experts’ opinion (engagement of eight experts) and the use of multi-criteria decision-making methods (MCDM), (specifically fuzzy-rough LMWA and fuzzy-rough CRADIS methods) under the predefined criteria (nine criteria). Derived results show that the implementation of the solar energy plants could play an optimal solution, while as the relatively unsuitable alternative could be marked the use of energy potential of watercourses. Gained final result, i.e. ranking order of the considered alternatives is additionally verified by the appliance of other MCDM methods, while the sensitivity analysis was also performed.