Sustainable urban mobility plans (SUMPs) have become increasingly popular in cities with environmental problems to reduce pollution, often caused by urban transportation. Therefore, this study aims to provide a practical framework for the selection decisions of final measures and policies to be carried out to achieve SUMP workspace goals using a fuzzy multi-criteria decision-making (MCDM) methodology. Alternatives are created with the strategic “pillar”, which was first adopted by the Podgorica city council. With the measurements in this pillar, the main criteria and the recommended measures for these measures create sub-criteria. Secondly, a Fuzzy Full Consistency Method (F-FUCOM) was used to determine the weights of the main and sub-criteria. The Fuzzy Combined Compromise Solution (F-CoCoSo) method was then applied to rank the alternatives. “The implementation and assurance of the SUMP” and “establishing a system for regular data collection, monitoring and evaluation of selected mobility indicators” were decided as the most important main and sub-criteria with weights of 0.286 and 0.1079, respectively. The findings suggest that the comprehensive planning for sustainable urban mobility alternative is first and the valorization of cycling potential alternative is second. A comprehensive sensitivity analysis confirms the validity, robustness, and effectiveness of the proposed framework. The applied methodology has the potential to assist decision makers in the process of developing SUMPs.

Overall bank performance in a particular year or period is important to all banking industry stakeholders, as it indicates their success or failure relative to predetermined targets. Due to conflicting criteria and uncertainties, assessing bank performance is a complicated decision-making problem. The current paper proposes the Fuzzy Level Based Weight Assessment (F-LBWA), the Fuzzy Logarithm Methodology of Additive Weights (F-LMAW), and the Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) combination as a practical and robust decision-making tool to cope with many complex ambiguities. In the first phase, the suggested hybrid Multi-Criteria Decision-Making (MCDM) approach estimates the weight coefficients of the performance criteria with the aid of a combined version of the F-LBWA and F-LMAW methods. In the second phase, the MARCOS method determines the ranking performance of the decision alternatives. The introduced model is tested and validated on a case study assessing publicly traded bank performance in Pakistan. The findings obtained from the sensitivity analysis revealed that the presented F-LBWA-F-LMAW-MARCOS approach produces consistent solutions and is a reliable and effective procedure in rational decision-making.

The aim of this study is to propose a model of stability and sensitivity for the studies used in the implementation of multi-criteria decision making (MCDM). In this context, it is proposed to use the steps "sensitivity analysis based on the variation of criterion weight, sensitivity analysis based on sequence reversal feature and comparison analysis with results from different sorting methods" for stability and sensitivity analysis. In the implementation part of the method, the Fragile Five countries were used as criteria for the unemployment rate, state budget, GDP growth, inflation, current account balance, risk premium for these countries. The criteria were weighted with MEREC and the ordering of alternatives was carried out with WISP. In the application phase of the method, it was found that the model was sensitive to changes in weight coefficients with different weights assigned to criteria over 22 scenarios. It has been observed that the model provides valid results in a dynamic environment through 4 different scenarios created in the sequence inversion step of the model. For the reliability of the MEREC-WISP-based model, a ranking comparison was made with some commonly used methods such as PIV, CoCoSo, COPRAS, WEDBA, TOPSIS and SAW and the results were found to have a high correlation.

The aim of this study is to develop a fuzzy MCDM based framework for security management in smart cities. The study focuses on the assessment of security risks and the selection of appropriate security measures. To address the uncertainties and complexities in the assessment of security risks and measures, this research employs two different fuzzy methods: weighting the risk factors using the Fuzzy weight by envelope and slope (F-WENSLO) method and selecting the most appropriate security measures using the Fuzzy Ranking Alternatives with Weights of Criterion (F-RAWEC) method. The proposed approach is applied to a case study on identifying and assessing potential security risks in a smart city infrastructure. The risk factors used in the study are identified as flash floods, traffic accidents and transportation security, robbery and public safety threats, cybersecurity threats, health crises and pandemics, and energy infrastructure and outages. Security systems are defined as physical security systems, emergency response and crisis management systems, software and cybersecurity systems, monitoring and surveillance systems, and sensor-based early warning systems. The results show that physical security measures play a critical role in preventing and responding to incidents, while emergency response and crisis management systems perform less well. It is also stated that software and cybersecurity systems provide an effective solution against digital threats, but their integration with other systems is important. The results show that the fuzzy-based model significantly increases the accuracy in prioritizing risks and provides a systematic method for selecting the most effective security measures. This study contributes to the literature by presenting an innovative decision-making tool for security management in smart cities.