Strategic management has applications in many areas of social life. One of the basic steps in the process of strategic management is formulating a strategy by choosing the optimal strategy. Improving the process of selecting the optimal strategy with MCDM methods and theories that treat uncertainty well in this process, as well as the application of other and different selection criteria, is the basic idea and goal of this research. The improvement of the process of the aforementioned selection in the defense system was carried out by applying a hybrid model of multicriteria decision-making based on methods defining interrelationships between ranked criteria (DIBR) and multiattributive ideal-real comparative analysis (MAIRCA) modified by triangular fuzzy numbers–“DIBR–DOMBI–Fuzzy MAIRCA model.” The DIBR method was used to determine the weight coefficients of the criteria, while the selection of the optimal strategy, from the set of offered methods, was carried out by the MAIRCA method. This was done in a fuzzy environment with the aim of better treatment of imprecise information and better translation of quantitative data into qualitative data. In the research, an analysis of the model’s sensitivity to changes in weight coefficients was performed. Additionally, a comparison of the obtained results with the results obtained using other multicriteria decision-making methods was conducted, which validated the model and confirmed stable results. In the end, it was concluded that the proposed MCDM methodology can be used for choosing a strategy in the defense system, that the results of the MCDM model are stable and valid, and that the process has been improved by making the choice easier for decision makers and by defining new and more comprehensive criteria for selection.

Crop insurance is used to reduce risk in agriculture. This research is focused on selecting an insurance company that provides the best policy conditions for crop insurance. A total of five insurance companies that provide crop insurance services in the Republic of Serbia were selected. To choose the insurance company that provides the best policy conditions for farmers, expert opinions were solicited. In addition, fuzzy methods were used to assess the weights of the various criteria and to evaluate insurance companies. The weight of each criterion was determined using a combined approach based on fuzzy LMAW (the logarithm methodology of additive weights) and entropy methods. Fuzzy LMAW was used to determine the weights subjectively through expert ratings, while fuzzy entropy was used to determine the weights objectively. The results of these methods showed that the price criterion received the highest weight. The selection of the insurance company was made using the fuzzy CRADIS (compromise ranking of alternatives, from distance to ideal solution) method. The results of this method showed that the insurance company DDOR offers the best conditions for crop insurance for farmers. These results were confirmed by a validation of the results and sensitivity analysis. Based on all of this, it was shown that fuzzy methods can be used in the selection of insurance companies.

The subject of this research is the evaluation of electric cars and the choice of car that best meets the set research criteria. To this end, the criteria weights were determined using the entropy method with two-step normalization and a full consistency check. In addition, the entropy method was extended further with q-rung orthopair fuzzy (qROF) information and Einstein aggregation for carrying out decision making under uncertainty with imprecise information. Sustainable transportation was selected as the area of application. The current work compared a set of 20 leading EVs in India using the proposed decision-making model. The comparison was designed to cover two aspects: technical attributes and user opinions. For the ranking of the EVs, a recently developed multicriteria decision-making (MCDM) model, the alternative ranking order method with two-step normalization (AROMAN), was used. The present work is a novel hybridization of the entropy method, full consistency method (FUCOM), and AROMAN in an uncertain environment. The results show that the electricity consumption criterion (w = 0.0944) received the greatest weight, while the best ranked alternative was A7. The results also show robustness and stability, as revealed through a comparison with the other MCDM models and a sensitivity analysis. The present work is different from the past studies, as it provides a robust hybrid decision-making model that uses both objective and subjective information.

Multi-criteria decision-making (MCDM) methods have gained increased attention in sustainable engineering, where complex decision-making problems require consideration of multiple criteria and stakeholder perspectives. This review paper provides a comprehensive overview of the different MCDM methods, their applications in sustainable engineering, and their strengths and weaknesses. The paper discusses the concept of sustainable engineering, its principles, and the different areas where MCDM methods have been applied, including energy, manufacturing, transportation, and environmental engineering. Case studies of real-world applications are presented and analyzed, highlighting the main findings and implications for engineering practice. Finally, the challenges and limitations of MCDM methods in sustainable engineering are discussed, and future research directions are proposed. This review contributes to the understanding of the role of MCDM methods in sustainable engineering and provides guidance for researchers and practitioners.

The objective of this paper is to introduce some new logarithm operational laws for intuitionistic fuzzy sets. Some structure properties have been developed and based on these, various aggregation operators, namely confidence logarithmic intuitionistic fuzzy Einstein weighted geometric (CLIFEWG) operator, confidence logarithmic intuitionistic fuzzy Einstein ordered weighted geometric (CLIFEOWG) operator, confidence logarithmic intuitionistic fuzzy Einstein hybrid geometric (CLIFEHG) operator, confidence logarithmic intuitionistic fuzzy Einstein weighted averaging (CLIFEWA) operator, confidence logarithmic intuitionistic fuzzy Einstein ordered weighted averaging (CLIFEOWA) operator, confidence logarithmic intuitionistic fuzzy Einstein hybrid averaging (CLIFEHA) operator have been presented. To show the validity and the superiority of the proposed operators, we compared these methods with the existing methods and concluded from the comparison and sensitivity analysis our proposed techniques are more effective.

The focus of this study is on the significance of location in establishing distribution centers. The key question when selecting a location is regarding which location would contribute the most to the growth of a company’s business through the establishment of distribution centers. To answer this question, we conducted research in the Brčko District of BiH in order to determine the best location for a distribution center using expert decision-making based on linguistic values. In order to use these values when selecting locations, a fuzzy set was formed using the IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) and fuzzy CRADIS (Compromise Ranking of Alternatives from Distance to the Ideal Solution) methods. The IMF SWARA method was utilized to determine the weights of the criteria, and the fuzzy CRADIS method was employed to rank the locations based on expert ratings. The location for the construction of distribution centers at Bodarište was rated the worst, while the McGowern Base location was rated the best. Based on these findings, the research question was answered, and it was demonstrated that fuzzy methods could be utilized in the selection of distribution center locations. Hence, we recommend that future research be performed on the application of fuzzy methods in the expert selection of potential sites for distribution centers.

Climate changes and the number of people in the world are increasingly affecting the environment. In order to reduce this impact, there are more and more alternatives to cars with internal combustion. Currently, the most used alternative is electric cars. This research aimed to rank electric cars according to their characteristics. It was selected 13 criteria according to which 20 alternatives were ranked. For this purpose, it was used two methods, DNMEREC (Double normalization Method based on the Removal Effects of Criteria) used to determine criterion weights objectively and DNCRADIS (Double normalization Compromise Ranking of Alternatives from Distance to Ideal Solution) method used to rank alternatives. Here, classical methods for multi-criteria decision-making (MCDM) are extended to contribute to a more stable ranking of alternatives. Unlike similar approaches, the same normalization has been used here, but in two ways, which represents an innovative approach in MCDM. The results of this approach have shown that the best-ranked alternative is A6 (Sono Sion), while the worst-ranked alternative is A2 (Smart EQ forfour). These results were confirmed with a comparative analysis of the results obtained using other MCDM methods and sensitivity analysis. The validation of the results and the application of the Spearman correlation coefficient have shown that the ranking of the alternatives is uniform and more stable when double normalization is applied than when classical methods with their normalization are used. In addition, this decision-making provides support to potential buyers for choosing electric cars.

The present age is moving through Industry 4.0 with massive technological developments. Supply chains have become digital, keeping sync with consumer demands and preferences. The recent pandemic has reinforced the need of embracing digital technologies in managing supply chains effectively. Therefore, it is necessary that supply chains adopt 5G mobile technologies. In this regard, the present study aims to discern the critical issues for the successful adaptation of 5G technologies for supply chain management (SCM) in developing countries such as India. The success factors for the adaptation of 5G in Indian supply chains are derived from the discussions made in the related past work regarding the challenges of implementing 5G technology. Then, the listed factors are finalised through initial rounds of face-to-face discussions with a focus group of five experts. Then, a q-rung-orthopair-fuzzy (qROFS)-based rating scale is used to rate the success factors. A new qROF-weighted-neutrality-average (q-ROFWNA)-based full-consistency method (FUCOM) approach for multicriteria decision-making (MCDM) problems involving group decision making is utilised to find out the critical success factors. Based on the comparative analysis of 17 success factors (grouped into four main factors), the spectrum availability, awareness of technology and usage, the development of supporting technologies and smart cities, and skill development are found to be the top five critical factors for the successful adaptation and implementation of 5G technologies in SCM. We further carry out a sensitivity analysis and validation test and observe that our model provides a reliable and stable solution.

This study deals with the selection of a sustainable supplier on the example of the agribusiness company Mamex from Bosnia and Herzegovina. The main problem of this research is the selection of a sustainable supplier as a part of the sustainable strategy of the Mamex company. One of the prerequisites is that suppliers must present sustainability principles in business by having an appropriate certificate. The results of the selection of sustainable suppliers are completed using a new hybrid fuzzy approach with the methods IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) and fuzzy TRUST (multi-normalization multi-distance assessment) CRADIS (compromise ranking of alternatives from distance to ideal solution) methods. The innovative approach is reflected in the use of a combination of these methods, especially by combining the TRUST and CRADIS methods into one method. The IMF SWARA method shows that the most important main criterion is the economic criterion, while the least important is the social criterion. By applying the fuzzy TRUST CRADIS method, it is found that out of the observed six suppliers, the second supplier has the best indicators. These results are confirmed by other fuzzy methods: MABAC (multi-attributive border approximation area comparison), WASPAS (weighted aggregated sum product assessment), fuzzy SAW (simple additive weighting), MARCOS (measurement of alternatives and ranking according to compromise solution), ARAS (a new additive ratio assessment), and TOPSIS (technique for order preference by similarity to an ideal solution). This research shows that applying more normalization when ranking alternatives reduces the influence of individual normalizations, and this approach should be used in future research.

Understanding the level of economic freedom is an important indicator for investors and policymakers. The index of economic freedom, which the Heritage Foundation releases annually, is the most significant of the methods used to measure this indicator in practice, as this index evaluates the degree of market openness over the degree of fiscal and regulatory restraint. The research presented in this paper was conducted in order to establish the level of economic freedom in the Balkan countries. For this purpose, a multi-criteria ranking of Balkan countries based on economic freedom criteria was used. The weight of the criteria was determined using the Entropy method, and the countries were ranked using the CRADIS (Compromise Ranking of Alternatives from Distance to ideal Solution) method. These methods employed a double normalisation approach, and according to the results of this application, Bulgaria has the best indicators of economic freedom, while Montenegro has the worst, with sensitivity analysis and validation of the results confirming these findings. The approach of using double normalisation contributes to decision-making stability since the results of different methods are uniform when compared to the use of the classical approach in the case of multi-criteria analysis methods.

According to applied TOPSIS multi-criteria decision-making method (Technique for Order of Preference by Similarity to Ideal Solution), in paper was selected adequate sales distribution channel in one enterprise active within the agro-food sector at the territory of Semberija (BiH). Decision makers, in this case represented by employed management in enterprise, have been evaluated five sales distribution channels in enterprise in line to previously defined criteria, i.e. products' characteristics, company's financial situation, consumer habits, production costs, geographic concentration, and products' assortment. The main goal of research was to create a model for optimal selection of sales channels, while derived results have been showed that the model "producer - seller (retailer) - consumer" is the most adequate towards the predefined selection criteria. In addition to the justified role of used method in selection of offered alternatives, article also represents a base that will serve in further research, whose focus would be on modernizing of existing and searching for new distribution channels in agricultural enterprises.

The hotel industry is undergoing significant change as a result of technological advancements and shifting guest preferences. Artificial intelligence (AI) is playing an increasingly important role in this transformation, allowing hotels to provide personalized, efficient, and sustainable experiences that meet the needs of modern travelers. This paper provides an overview of the role and application of AI in the hotel industry, highlighting its benefits and challenges as well as presenting case studies that show how hotels are currently utilizing AI technology. According to the review of literature, AI technology can be used to optimize hotel operations, improve the guest experience, and boost sustainability efforts. However, the use of AI technology in the hotel industry raises concerns about data privacy and security, job displacement, and the need for human supervision to ensure ethical and responsible deployment. Finally, the paper discusses the future directions of AI in the hotel industry, highlighting the emergence of voice technology and augmented reality as potential areas for future innovation. This paper adds to the growing body of literature on the use of AI in the hotel industry and provides useful insights for hotel managers, technology providers, and researchers.

This research is focused to identifying the most suitable plum variety for establishing a new orchard, aiming to achieve optimal outcomes. To accomplish this goal, multi-criteria decision-making model has been developed striving to support decision-making process. The gained results are based on experience of experts engaged in assessment of certain plum varieties. The analysis of collected expert ratings has involved mutual use of 3 methods, where the FUll COnsistency Method (FUCOM) and CRiteria Importance Through Intercriteria Correlation (CRITIC) methods were applied to determine the weights of selected criteria. The results of applying these methods demonstrated that the Maretability criterion is of highest importance. Besides, the fuzzy MARCOS method (Measurement Alternatives and Ranking according to Compromise Solution) has been applied to rank the plum varieties according to expert ratings. Derived results revealed that the ?Cacanska rodna? and ?Stanley? varieties were ranked as the most favorable, while the ?Prezident? variety was assessed as the most unfavorable. Further, gained research outcomes were corroborated by the sensitivity analysis and results validation. This research contributes to improvement of fruit growing in BiH by previous adequate selection of available plum varieties towards the establishing of new orchards and yielding optimal outcomes.

The primary objective of this research paper is to investigate the potential impact of emergency remote teaching on the academic performance of undergraduate students, while also assessing the consistency of academic outcomes across various courses during the emergency transition to distance learning. The research methodology involves the collection of students' accomplishments and course passing rates spanning four consecutive academic years, from 2018/2019 to 2021/2022. The study identifies three distinct teaching environments: traditional, distance learning, and hybrid. The academic year 2018/2019 is designated as a pre-pandemic reference point. The subsequent years mark a transition from distance learning to a hybrid teaching model and a return to the traditional in-class environment. The analytical framework includes descriptive and correlation analyses, supplemented by an analysis of variance to derive meaningful insights. It is essential to highlight that the results obtained are further validated by the passing rates for the academic year 2022/2023, reinforcing the contemporary relevance and credibility of the research findings.