Picture fuzzy set-based multi-criteria decision-making approach and applications

Industry 4.0 and the latest developments in technology have revealed some new concepts such as digitalization, internet of things, and smart systems. The efforts of businesses to create value in global competition have rapidly increased the interest in these terms. Therefore, considering the needs of the age, it has become inevitable for businesses to adapt and implement these new technologies. On the other hand, this process has led to the emergence of important decision problems for decision makers, where there are very complex, uncertain, and ambiguous situations. In solving such problems, using fuzzy set theory or approaches that integrate various fuzzy set extensions and multi-criteria decision making (MCDM) techniques can provide a suitable evaluation framework for decision makers to make more rational decisions. In this thesis, it is aimed to develop an MCDM model based on picture fuzzy set (PFS) theory. PFS is an important tool used to identify uncertain, ambiguous, and inconsistent information in solving complex decision problems and to eliminate such situations in decision-making processes. Unlike other fuzzy set extensions, it offers decision makers a wider choice area, enabling it to be handled with a more flexible approach suitable for the nature of the decision problem of interest. Within this scope, the COBRA (COmprehensive Distance Based RAnking) technique was expanded based on the PFS theory, and then a new PSI-PF-COBRA decision making model was proposed by integrating it with the PSI (Preference Selection Index) technique in the study. The proposed model not only makes it easier to define uncertain information about the decision problem, but also ensures that experts can be included in the process by considering various weighted characteristics (age, experience, position, etc.). In the study, the basic algorithm of the model was explained in detail on an example selected from the literature, and then applied to "determining the smart contract language selection criteria" and "mathematical software selection" problems to show its applicability in real life problems. As a result, it is thought that determining the expert weights according to the PSI technique and integrating them into the PF-COBRA method will bring an important novelty to the field. In this thesis, it is aimed to develop an MCDM model based on picture fuzzy set (PFS) theory. PFS is an important tool used to identify uncertain, ambiguous, and inconsistent information in solving complex decision problems and to eliminate such situations in decision-making processes. Unlike other fuzzy set extensions, it offers decision makers a wider choice area, enabling it to be handled with a more flexible approach suitable for the nature of the decision problem of interest. Within this scope, the COBRA (COmprehensive Distance Based RAnking) technique was expanded based on the PFS theory, and then a new PSI-PF-COBRA decision making model was proposed by integrating it with the PSI (Preference Selection Index) technique in the study. The proposed model not only makes it easier to define uncertain information about the decision problem, but also ensures that experts can be included in the process by considering various weighted characteristics (age, experience, position, etc.). In the study, the basic algorithm of the model was explained in detail on an example selected from the literature, and then applied to "determining the smart contract language selection criteria" and "mathematical software selection" problems to show its applicability in real life problems. As a result, it is thought that determining the expert weights according to the PSI technique and integrating them into the PF-COBRA method will bring an important novelty to the field.