The Logarithm Methodology of Additive Weights (LMAW) method is a very young method and in its basic form is defined for crisp values. In this paper, the LMAW method was improved by being modified with triangular fuzzy numbers. The modification significantly improved the capacity of the LMAW method to consider uncertainty in decision making. The special importance of the method is reflected in a relatively simple mathematical apparatus due to which it is possible to define, with high quality, weight coefficients of criteria and rank alternative solutions in uncertain environments. The method was tested in solving the problem of the location selection for a landing operations point (LOP) in combat operations of the army. The validation of the obtained results was performed: (1) by means of comparison with the Fuzzy Simple Additive Weighting (FSAW) Method, the Fuzzy Multi-Attributive Border Approximation area Comparison (FMABAC), the fuzzy Višekriterijumsko KOmpromisno Rangiranje (FVIKOR), the fuzzy COmpressed PRoportional ASsessment (FCOPRAS), and the fuzzy Multi Attributive Ideal-Real Comparative Analysis (FMAIRCA); (2) by means of sensitivity analysis by changing the weight coefficients of criteria; and (3) using simulation software. In comparison with other methods, the quality of the ranking of alternative solutions was confirmed, which highlighted the special importance of the fuzzy LMAW method relative to that of certain standard methods, respectively, the ones that are often used and confirmed in practice. On the other hand, the sensitivity analysis, including the changing of the weight coefficients of criteria, showed that the model could tolerate smaller errors in defining the weight coefficients of criteria, and it provided stable results. Finally, the validation of results achieved with the use of simulation software confirmed the obtained output results. The output results confirmed the quality of the modified method.

This study considers the problem of selecting an anti-tank missile system (ATMS). The mentioned problem is solved by applying a hybrid multi-criteria decision-making model (MCDM) based on two methods: the DIBR (Defining Interrelationships Between Ranked criteria) and the MABAC (Multi-Attributive Border Approximation area Comparison) methods. The methods are modified by applying rough numbers, which present a very suitable area for considering uncertainty following decision-making processes. The DIBR method is a young method with a simple mathematical apparatus which is based on defining the relation between ranked criteria, that is, adjacent criteria, reducing the number of comparisons. This method defines weight coefficients of criteria, based on the opinion of experts. The MABAC method is used to select the best alternative from the set of the offered ones, based on the distance of the criteria function of every observed alternative from the border approximate area. The paper has two main innovations. With the presented decision-making support model, the ATMS selection problem is raised to a higher level, which is based on a proven mathematical apparatus. In terms of methodology, the main innovation is successful application of the rough DIBR method, which has not been treated in this way in the literature so far. Additionally, an analysis of the literature related to the research problem as well as to the methods used is carried out. After the application of the model, the sensitivity analysis of the output results of the presented model to the change of the weight coefficients of criteria is performed, as well as the comparison of the results of the presented model with other methods. Finally, the proposed model is concluded to be stable and multi-criteria decision-making methods can be a reliable tool to help decision makers in the selection process. The presented model has the potential of being applied in other case studies as it has proven to be a good means for considering uncertainty.

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.

In military operations, the proficient overcoming of water barriers is paramount, with sub-optimal execution potentially leading to significant human and equipment casualties. In this context, global armed forces accord considerable emphasis to the selection of appropriate mechanisms for water obstacle overcoming. This study elucidates the adoption of a Multi-Criteria Decision-Making (MCDM) approach for the selection of optimal pontoon bridge sets for military applications. Criteria identification was undertaken by seven distinguished experts, leading to the determination of weight coefficients using the Defining Interrelationships Between Ranked criteria II (DIBR II) method. Expert assessments were subsequently aggregated utilizing the Normalized Weighted Bonferroni Mean (NWBM) operator. The Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA) method, operationalized within the Fermatean Fuzzy (FF) environment, was harnessed for the discernment of the best alternative. An analysis of the sensitivity of the study's findings with respect to variations in criteria weighting, coupled with a comparative exploration, led to the inference that the proposed MCDM model boasts stability. However, it was noted that the model exhibits sensitivity to shifts in criteria weight coefficients, underscoring its utility as a valuable aid for decision-makers, especially in the domain of pontoon bridge set selection.

A project always needs the participation of suppliers to provide the goods and services needed by the project. As the size of the project increases, the need to use suppliers also increases. Choosing the appropriate suppliers for the project is always a difficult task, and considering different criteria along with the existence of multiple alternatives always increases the difficulty and complexity of the supplier selection problem. This study deals with the supplier selection problem, which is one of the important issues in projects and organizations in the downstream field of oil, gas, and petrochemicals. The purpose of this paper was to investigate and improve the supplier selection procedure in this field by considering real-world uncertain conditions. To this end, the fuzzy set theory and gray numbers were taken into account. In the present study, the criteria were first determined by expert judgment; then, the fuzzy best–worst method (FBWM) was exploited to rank the criteria; finally, the suppliers were prioritized and ranked by using the gray COCOSO method The results showed that the highest and the lowest weights were obtained for the “price” and “attention to environmental issues” criteria, respectively. Also, the selected supplier was a supplier with a high score in all criteria. One of the practical benefits of this research is to provide a solution to speed up, facilitate, and improve the process of selecting suppliers for companies in the downstream field of oil, gas, and petrochemicals.

Today, companies face complexities and uncertainties that make it difficult to manage various risks. One of the important tools for achieving success in water supply projects is the proper implementation of risk management processes and activities throughout the project’s make-span. Risk identification and assessment are two important steps in project risk management. In this research, the Fuzzy DEMATEL and Fuzzy ANP as well as Artificial Neural Network methods are exploited for the analyzing and ranking of environmental risks of water supply projects. Risks are classified and then prioritized by the Fuzzy ANP and Artificial Neural Network methods into four main categories, including technical, organizational, project management, and external risks. The weight of each of the technical, organizational, project management, and external risks using the ANP method was obtained as 0.31, 0.26, 0.25, and 0.18, respectively, and the following weights were obtained using the Artificial Neural Network: 0.42, 0.27, 0.22, and 0.09, respectively. The results show that although the exact weights differed between methods, especially for technical and external risks, the overall prioritization of risk categories followed a broadly consistent pattern. In addition, the risk associated with the suppliers obtained the highest weight among the external risks; the risk associated with the high cost of materials gained the highest weight among the organizational risks; the risk associated with the requirements acquired the highest weight among the technical risks; and finally, the risk associated with communication achieved the highest weight among the project management risks. The method presented in this research helps project managers and decision-makers in the water supply industry to make a better and more realistic risk assessment by considering the mutual effects of project risks.