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Item type:Publication, Enhancing multi-criteria decision-making with fuzzy logic: An advanced Defining Interrelationships Between Ranked II method incorporating triangular fuzzy numbers(Global Academic Excellence, 2024-03-15)In multi-criteria decision-making (MCDM), accurately quantifying qualitative data and simulating real-world scenarios remains a significant challenge, particularly in the presence of inherent imprecision and incompleteness of information. Fuzzy logic, recognized for its capacity to model uncertainty and ambiguity, emerges as a pivotal theory in decision-making processes. This study introduces an enhancement to the Defining Interrelationships Between Ranked Criteria II (DIBR II) method, employing triangular fuzzy numbers with variable confidence intervals for the determination of criteria weight coefficients-essential for assessing their significance and impact on final decisions. The enhanced method, hereafter referred to as the Fuzzy-DIBR II (F-DIBR II), is elaborated upon through a comprehensive description of its algorithmic steps, underscored by a numerical example that highlights its potential. Validation of F-DIBR II is undertaken via a comparative analysis against the traditional DIBR II approach, placing particular emphasis on its application within the Fuzzy Complex Proportional Assessment (COPRAS) framework, geared towards evaluating sustainable mobility measures. This focal point not only reaffirms the necessity of integrating fuzzy logic into the DIBR II methodology but also validates its practical applicability in addressing real-world issues. Contributions of this research extend beyond the theoretical enhancements of fuzzy theory within the MCDM landscape, offering tangible implications for the application of F-DIBR II in sustainable mobility analyses. The consistency in professional terminology throughout the study ensures clarity and coherence, aligning with the stringent standards of top-tier academic journals. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhancement of the Defining Interrelationships Between Ranked Criteria II method using interval grey numbers for application in the grey-rough MCDM model(Global Academic Excellence, 2024-05-06)Multi-Criteria Decision-Making (MCDM) represents a critical area of research, particularly in artificial intelligence, through the modeling of real-world decision-making scenarios.Numerous methods have been developed to address the challenges of integrating non-quantitative, incomplete, and imprecise information under conditions of uncertainty.This paper presents the enhancement of the Defining Interrelationships Between Ranked Criteria II (DIBR II) method by incorporating interval grey numbers, in accordance with the principles of Grey theory, its arithmetic operations, and the DIBR II methodology.The enhancement includes the of a conviction degree to reflect decision-makers' or experts' confidence in their assertions.The application of this enhanced method is demonstrated through an illustrative example, following the procedural steps.Additionally, its efficacy is validated in a real-world scenario involving the selection of Lean organization system management techniques, utilizing the Rough Multi-Attributive Border Approximation Area Comparison (Rough MABAC) method.The results indicate that the enhanced DIBR II method is effective in determining criteria weight coefficients, offering a more nuanced distribution compared to traditional crisp methods.Furthermore, when implemented in a multi-criteria model, it yields a more refined ranking of alternatives, contingent on the degree of confidence in the given claims. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A step-by-step hybrid approach based on multi-criteria decision-making methods and a bi-objective optimization model to project risk management(Regional Association for Security and crisis management, 2024-01-01)Project success and achieving project objectives and goals highly depend on effective and thorough risk management implementation. This study provides a comprehensive and practical methodology for project risk management. In this paper, firstly, the risks were collected by analyzing the historical documents and literature. Then, the collected risks were screened using brainstorming and categorized into five groups. Subsequently, a questionnaire was made and the identified risks were validated using the Fuzzy Delphi technique. Also, the relationships between risks were determined using the Interpretive Structural Modelling (ISM) method. Moreover, the weights of the criteria used to rank the risks were calculated through the Fuzzy Best-Worst Method. Subsequently, the major risks were determined using the fuzzy WASPAS method. Furthermore, a novel bi-objective mathematical programming model was developed and solved using the Augmented Epsilon-Constraint (AEC) method to choose the optimal risk response strategies for each critical risk. The results demonstrated that the proposed framework is effective in dealing with construction project risks. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis of key factors affecting risk tolerance in project-oriented firms using hybrid fuzzy DEMATEL-ISM: An empirical study(University of Belgrade, 2025-01-01)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..1