Vague Information Research Articles

Data analysis for panoramic X-ray selection: Fermatean fuzzy type correlation coefficients approach

An interval-valued Fermatean hesitant fuzzy set (IVFHFS) not only can be regarded as the union of some interval-valued Fermatean fuzzy sets (IVFFSs) but also represent the Fermatean hesitant fuzzy elements (FHFEs) in the form of interval values. So IVFHFSs are extensions of FHFSs and IVFFSs, which are powerful tools to represent more complicated, uncertain, and vague information. This paper focuses on the four kinds of correlation coefficients for FHFSs and extends them to the correlation coefficients and the weighted correlation coefficients for IVFHFSs. In the processing, we develop the least common multiple expansion (LCME) methods to solve the problem that the cardinalities of Fermatean hesitant fuzzy elements (FHFEs) (or interval-valued Fermatean hesitant fuzzy elements (IVPHFEs)) are different. In addition, we propose score functions and accuracy functions of FFEs (or IVFFEs) to rank all the FFEs (or IVFFEs) in an FHFE (or an IVFHFE). Especially, score functions and accuracy functions of IVFFEs are both presented as interval numbers. Then use the comparison method of interval numbers to compare two revised IVFHFEs in order to keep the original fuzzy information as far as possible. What is more, we define the local correlations and local informational energies which can depict the similarity between two IVFHFEs more meticulously and completely. At last, the numerical examples show the feasibility and applicability of the proposed methods in multiple criteria decision-making (MCDM) and clustering analysis.

Multi-Attribute Group Decision Making Based on Spherical Fuzzy Zagreb Energy

Based on picture fuzzy sets (PFSs), we use a mathematical model to tackle such types of problems when a person has opinions like yes, no, abstain, and refusal. The spherical fuzzy model is more flexible and practical than the picture fuzzy model, as it enhances the space of uncertainty. It broadens the space of vague information evaluated by decision makers since graphs are the pictorial representation of information. Graphs are a tool to represent a network. To handle some real-world problems, spherical fuzzy graphs can be used more effectively as compared to picture fuzzy graphs (PFGs). In this article, we expand the notion of fuzzy Zagreb indices of the fuzzy graph to the spherical fuzzy Zagreb indices of the spherical fuzzy graph (SFG). The spherical fuzzy Zagreb matrix of SFG and Zagreb energy of SFG are defined with examples. Additionally, we develop several lower and upper bounds of the spherical Zagreb energy of SFG. In addition, we present an application of SFG by computing its Zagreb energy in the decision-making problem of choosing the best location for business purposes.

Household Solid Waste Generation and Management Systems in Rural Areas of Bhutan

Globally, waste is accepted as an emerging issue and it concerns everyone, locally, nationally, and globally. However, studies on waste generation, composition, and management systems are often focused on urban areas. Similarly, Bhutan lacks information and reliable data about rural household solid waste generation and its composition at large and there is vague information on its management systems. This paper presents precise information on waste generation, composition, and management systems in rural areas in three administrative units called Gewogs namely Khatoed, Semjong, and Phangkhar under Gasa, Tsirang, and Zhemgang districts respectively. These study areas are located in different regions of the country with varying geographic locations, natural resources, altitudes, cultural diversity, lifestyles, and consumption patterns. The study envisioned narrowing the data gap, creating a yardstick for similar studies to be undertaken henceforth, and delivering sufficient and reliable information for informed decision-making for the successful progression of waste prevention and management programs to achieve"Zero Waste Bhutan", the vibrant vision of the country towards attaining zero waste society. On average, each household generated about 0.17 kg per day in the study areas and the per capita waste generation was 0.039 kg, 0.037 kg, and 0.032 kg a day in Khatoed, Semjong, and Phangkhar Gewogs respectively. On average, at least 72.9% practiced reduction, 86.8% reported that they reused, and 29.4% practiced recycling in their daily lives. At least 95.7%, 73.7%, and 82.1% of the household under Semjong, Phangkhar, and Khatoed Gewogs respectively practiced waste segregation into wet and dry at the source.

Interval-valued spherical fuzzy MABAC method based on Dombi aggregation operators with unknown attribute weights to select plastic waste management process

The widespread use of plastic products worldwide generates an enormous amount of waste. Plastic is a substance that is not biodegradable and is permanent in the environment. The environment and living beings are negatively affected by this waste if it is not managed properly. However, the assessment and selection of effective plastic waste management methods is a complex and uncertain decision-making problem due to the involvement of multiple conflicting attributes, the imprecise human mind, and a lack of information. Recently, interval-valued spherical fuzzy (IVSF) sets have been recognized as one of the suitable tools to handle vague information. In this paper, we introduce a hybrid multi-attribute group decision-making methodology based on the entropy method, deviation-based method, and Multi-Attribute Border Approximation area Comparison (MABAC) method with IVSF sets to choose the most effective plastic waste management process in which the decision experts’ and attribute weights are completely unknown. In this model, the entropy and deviation-based methods are utilized to calculate the decision experts’ and attribute weights, respectively. Also, the MABAC method is used to evaluate the priority order of the plastic waste management options. To do this, Dombi weighted averaging and Dombi weighted geometric operators are developed in the IVSF environment. In the proposed model, the Dombi weighted averaging operator is used to fuse several decision experts’ information, and the Dombi weighted geometric operator is used to determine the border approximation area matrix. Then, to confirm the applicability of the suggested model, we look at a numerical example of choosing the most effective method for managing plastic waste with IVSF information. In the numerical example, we consider six alternatives and four important attributes with ten sub-attributes by consulting four decision experts. The result of this study shows that ‘Recycling (A2)’ is the most effective method to reduce plastic waste. Finally, we compare the proposed method with the existing ones to demonstrate its validity.

Intuitionistic fuzzy EM-SWARA-TOPSIS approach based on new distance measure to assess the medical waste treatment techniques

Due to the potential harm to public health as well as the environment, medical waste treatment have emerged as a critical concern, especially in developing nations. The selection of a suitable medical waste treatment technique (MWTT) is now characterized as a critical multi-criteria decision-making (MCDM) problem because of the incorporation of several competing criteria. This study provides a hybrid MCDM technique for assessing MWTTs concerning social, environmental, economic, and technical criteria in an intuitionistic fuzzy environment. The intuitionistic fuzzy set (IFS) is a potent tool for expressing ambiguous and vague information efficiently and has proven effective for MCDM problems. In IFS environment a new distance measure and an entropy measure (EM) are introduced by employing the generalized Csiszar f-divergence measure, which are further used to construct the MCDM algorithm. The introduced entropy measure is used to examine the objective weights of criteria, and the distance measure is employed to construct the technique for order performance by similarity to ideal solution (TOPSIS) for the assessment of the alternatives. Furthermore, the significance of the criteria for the proposed MCDM technique is evaluated by integrated weights (combining objective and subjective weights). The Stepwise Weight Assessment Ratio Analysis (SWARA) technique is used to obtain the subjective weights for criteria. Thus, a hybrid MCDM technique, namely IF-EM-SWARA-TOPSIS, is developed. To exhibit the effectiveness and applicability of the suggested framework, a MWTT selection problem in Indian context is depicted. The comparative study and sensitivity assessment are conducted to show the sustainability and stability of the proposed IF-EM-SWARA-TOPSIS technique for ranking the preference order of the MWTTs. Moreover, the practical implications of the proposed method are discussed.

Sustainable Supplier Selection using Neutrosophic Multi-Criteria Decision Making Methodology

Sustainable supplier selection is an important part of supply chain management since it encourages ethical and eco-friendly procedures. This study examines the most important criteria and factors for judging the sustainability performance of suppliers and presents a thorough overview of sustainable supplier selection. A decision-making framework for sustainable supplier selection is created via a review of relevant research, case studies, and best practices. Key factors for assessing suppliers include environmental performance, social responsibility, and economic viability, all of which are included in the framework. The results stress the need to take into account suppliers' energy efficiency, waste management, and social responsibility initiatives including fair labor practices and community involvement. Other essential considerations for long-term sustainability include economic viability and supply chain resilience. Organizations may improve their environmental impact, reduce supply chain risks, and boost overall performance by using sustainable supplier selection practices. This study used the TOPSIS method to rank sustainable suppliers. The TOPSIS method is employed with the single-valued neutrosophic set to deal with vague information. A case study in a food company is conducted to show the best supplier

Normal [formula omitted] functions-based fuzzy information measures with applications in MADM and pattern recognition

Fuzzy and non-standard fuzzy information measures act upon vague information entailed in linguistic variables or uncertain numerical data following some goal for which they discover the “best alternative” or “correct pattern”. This paper introduces the concept of normalKN functions instrumental to generate a class of fuzzy knowledge measures. In addition to the axiomatic approach, we investigate various general methods to obtain normalKN functions from the notions of auto-morphisms, restricted equivalence functions, and restricted dissimilarity functions. Further, aggregatingnormal KNfunctions, we propose three knowledge measures of a fuzzy set and exploit their significance in practical applications. This concerns Multiple Attribute Decision Making (MADM) problems, the issue of appropriate weight computation, and the understanding of structured linguistic variables. Additionally, we consider the real case study for the ranking of universities. A novel methodology to transform a given fuzzy knowledge measure into a family of similarity measures of fuzzy sets is also suggested. Furthermore, we discuss an application of the proposed similarity measures in the context of pattern recognition and the MADM problem using artificial and real data sets. The comparative study of the proposed measures shows their advantage over existing ones because of specific theoretical and practical aspects.

Indeterminacy Handling of Adaptive Neuro-fuzzy Inference System Using Neutrosophic Set Theory: A Case Study for the Classification of Diabetes Mellitus

Early diabetes diagnosis allows patients to begin treatment on time, reducing or eliminating the risk of serious consequences. In this paper, we propose the Neutrosophic-Adaptive Neuro-Fuzzy Inference System (N-ANFIS) for the classification of diabetes. It is an extension of the generic ANFIS model. Neutrosophic logic is capable of handling the uncertain and imprecise information of the traditional fuzzy set. The suggested method begins with the conversion of crisp values to neutrosophic sets using a trapezoidal and triangular neutrosophic membership function. These values are fed into an inferential system, which compares the most impacted value to a diagnosis. The result demonstrates that the suggested model has successfully dealt with vague information. For practical implementation, a single-value neutrosophic number has been used; it is a special case of the neutrosophic set. To highlight the promising potential of the suggested technique, an experimental investigation of the well-known Pima Indian diabetes dataset is presented. The results of our trials show that the proposed technique attained a high degree of accuracy and produced a generic model capable of effectively classifying previously unknown data. It can also surpass some of the most advanced classification algorithms based on machine learning and fuzzy systems.

A general model of ambiguous sets to a single-valued ambiguous numbers with aggregation operators

The ambiguous sets have recently been proposed to represent inherent uncertainties, imprecision, and vague information. In this study, we generalize the concept of the ambiguous set to a single-valued ambiguous number (SVAN). We propose many comparison functions and operational laws for SVANs. This study also introduces three new aggregation operators, the ambiguous weighted geometric operator (AWGO), the ambiguous ordered weighted geometric operator (AOWGO), and the ambiguous hybrid geometric operator (AHGO), for aggregating the input information in the form of SVANs. We discuss some significant properties of the operators along with numerical examples. We develop a method based on the AWGO and AHGO for solving multi-criteria group decision-making (MCGDM) problems. Finally, a real-time example of a MCGDM problem associated with the selection of cars by customers is presented, and its solution is recommended using the proposed method.

Performance evaluation of technological service platform: A rough Z-number-based BWM-TODIM method

The continuous development of advanced manufacturing has triggered a strong demand for technological service platforms. To better support the development of the technological service platform, it is necessary to conduct a performance evaluation for the technological service platform. However, the existing evaluation methods fail to fully consider the ambiguity and uncertainty of decision-makers’ judgments. Meanwhile, conventional methods require quite a few pairwise comparisons when determining the criteria weights. This paper proposes a novel approach that integrates rough Z-number, the best worst method (BWM), and TODIM (an acronym in Portuguese of Interactive and Multi-Criteria Decision Making) to address these issues. The proposed method can significantly reduce the number of pairwise comparisons. It also shows a significant advantage in dealing with vague information and handling the reliability of the evaluation. Moreover, the TODIM method helps capture the decision-makers' risk attitude towards uncertainty. Finally, a case study of seven technological service platforms is conducted to validate the feasibility and effectiveness of the proposed method.

Pythagorean Fuzzy Sets-based VIKOR and TOPSIS-based multi-criteria decision-making model for mitigating resource deletion attacks in WSNs

In Wireless Sensor Networks (WSNs), resource depletion attacks that focusses on the compromization of routing protocol layer is identified to facilitate a major influence over the network. These resource depletion attacks drain the batter power of the sensor nodes drastically with persistent network disruption. Several protocols were established for handling the impact of Denial of Service (DoS) attack, but majority of them was not able to handle it perfectly. In specific, thwarting resource depletion attack, a specific class of DoS attack was a herculean task. At this juncture, Multicriteria Decision Making Model (MCDM) is identified as the ideal candidate for evaluating the impact introduced by each energy depletion compromised sensor nodes towards the process of cooperation into the network. In this paper, A Pythagorean Fuzzy Sets-based VIKOR and TOPSIS-based multi-criteria decision-making model (PFSVT-MCDM) is proposed for counteracting with the impacts of resource depletion attacks to improve Quality of Service (QoS) in the network. This PFSVT-MCDM used the merits of Pythagorean Fuzzy Sets information for handling uncertainty and vagueness of information exchanged in the network during the process of data routing. It utilized VIKOR and TOPSIS for exploring the trust of each sensor nodes through the exploration of possible dimensions that aids in detecting resource depletion attacks. The experimental results of PFSVT-MCDM confirmed better throughput of 21.29%, enhanced packet delivery fraction of 22.38%, minimized energy consumptions 18.92%, and reduced end-to-end delay of 21.84%, compared to the comparative resource depletion attack thwarting strategies used for evaluation.

An advanced stratified decision-making strategy to explore viable plastic waste-to-energy method: A step towards sustainable dumped wastes management

Dumping waste and dumping sites pose significant environmental issues with serious implications for human health, the environment, and wildlife. Dumping grounds can also cause difficulties such as leaching, which occurs when waste chemicals seep into the soil and damage groundwater resources. The vast quantity of dumped waste plastic is a major source of energy in the form of gas, fuel, and electricity. The selection of the most appropriate waste-to-energy (WtE) technology affects the dumped waste because it allows for reduced greenhouse gas emissions, diverts dumpsites, generates energy, supports the circular economy, and lowers post-recycling pollution. This complex selection problem requires careful consideration of uncertainties from multi-perspective analysis, vague information, and imprecision in decisions. The stratified fuzzy multi-criteria decision making approach is suitable for addressing different states of events, uncertain data, and sustainable choice selection. Hence, we improved the intuitionistic analytical hierarchical process with a stratified targeting concept for multi-level criteria importance and coupled it with weighted aggregated sum product assessment for ranking WtE techniques. The preferences of experts are characterized using intuitionistic fuzzy preference relations. An empirical study on dumped plastic waste and dumpsite management discovered that plasma technology becomes a more feasible option by satisfying stratification and is more effective in clearing landfills by recovering energy. The comparative analysis, sensitive analysis, and Spearman’s rank correlation validate the proposed methodology, and this method seems to be a new dimension to handle ecological issues.

Connectivity index of directed rough fuzzy graphs and its application in traffic flow network

The directed rough fuzzy graph (DRFG) is a fusion of rough and fuzzy theory, as it deals with incomplete and vague information simultaneously. Connection or the strength of connectivity $$(\mathcal{S}\mathcal{C})$$ is vital in the realm of circuits or networks that are linked to the real world. As a result, $$\mathcal{S}\mathcal{C} $$ is one of the most essential aspects of a directed rough fuzzy network system. The neighborhood connectivity index $$(\mathcal {NCI})$$ is one such parameter that has a variety of applications in network theory. In this study, our main objective is to present a new topological index $$\mathcal {NCI}$$ based on DRFGs to solve complicated problems. Motivated by the modeling of networks, the strength of vertices to their neighboring vertices, and the efficiency of DRFGs to solve complex problems, we aim to study the NCI of DRFGs. In this paper, we successfully introduce a notion NCI based on DRFGs to deal with the uncertainties that arise in real-world problems. Based on the strength of vertices to neighboring vertices, we provide several lower and upper bounds for the $$\mathcal {NCI}$$ of DRFGs with reference to other graph invariants such as the number of vertices, edges, and degree distance. When we study $$\mathcal {NCI}$$ in operations for DRFGs with a large number of vertices, the degree of vertices in a DRFG provides a confusing picture. Therefore, a mechanism to determine the $$\mathcal {NCI}$$ for DRFG operations is therefore required. Therefore, generalized formulas for the $$\mathcal {NCI}$$ of DRFGs obtained by operations such as union, composition, and Cartesian product are also developed. An algorithm for obtaining the $$\mathcal {NCI}$$ of DRFGs is also proposed. Further, an application of the $$\mathcal {NCI}$$ of DRFGs in traffic flow networks was discussed to identify the busiest intersection using the proposed algorithm. Finally, we illustrate a comparative analysis and analysis table of the established approach ( $$\mathcal {NCI}$$ ) with existing techniques (connectivity index $$(\mathcal{C}\mathcal{I})$$ and Wiener index $$(\mathcal{W}\mathcal{I})$$ ) are shown to demonstrate the validity of the presented approach.

Uncertain probability, regular probability interval and relative proximity

This work firstly discusses two layers of meanings about uncertain information from which the uncertain probability information is naturally introduced which can generalize some other uncertain information such as interval information, intuitionistic fuzzy information and vague information. Two decision making models are presented, called relative opinion and selected opinions. To model their high dimensional counterparts, we introduce the concepts of uncertain probability matrix, regular probability interval and BUI of probability vector mainly based on some barycentric coordinates expressions. Some related properties, remarks, high dimensional cases of relative opinion and selected opinions with numerical examples are presented. Further, we technically define the concept of relative proximity for probability vector by which it is possible to define a type of unimodal function on the space of probability vectors, which otherwise is difficult to be done. Finally, some properties of relative proximity in relation to BUI of probability vector are analyzed. This work is suitable for both practitioners and theorists devoted to uncertain models.

Significance measures for rules in probabilistic-fuzzy inference systems based on fuzzy transforms

This paper defines a novel probabilistic-fuzzy inference system that considers fuzzy inputs and returns, as output, a probability distribution. In this way, it combines two different ways to represent uncertainty: the one modeled by fuzzy theory, that allows to represent reliable but vague information; and the one modeled by probability theory, that allows to represent undetermined but specific information. The novelty of this probabilistic-fuzzy inference system, with respect to the other existing in the literature, is that its inference engine combines quantile functions instead of distribution, probabilistic or density functions. Besides the formal definition of this novel kind of fuzzy inference systems, we propose: firstly, the construction of probabilistic-fuzzy rules by means of direct quantiles F-transforms; secondly, the definition of several significance measures for the obtained association rules; and finally, we present a set of experiments to validate all the assertions done throughout the paper.

Diagnosis and referrals to physical therapy among caregivers of children with genetic disorders: a qualitative inquiry

Purpose Genetic disorders are common in Saudi Arabia. Impaired motor development is one of the major characteristics associated with genetic disorders. Early identifications and referrals are key to receiving physical therapy. This study aims to explore caregivers of children with genetic disorders’ experience with early identification and referrals to physical therapy. Materials and methods An inductive qualitative design of 16 caregivers of children with genetic disorders was done to investigate the identification and referral process to physical therapy. A thematic analysis was used to analyze the data and multiple coders coded the data to increase the trustworthiness of the analysis. Results The analysis led to emergence of four main themes. Caregivers revealed their struggle with the detection process. They struggled with the vague information related to their children’s condition. They also expressed a desperate need for guidance to clarify the process for genetic testing, counseling, and rehabilitation. Although their overall experience with physical therapy was satisfactory, they encountered a number of issues related to scheduling appointments, delayed referrals, and unconfirmed diagnoses. Conclusion The results of this study could indicate that more efforts are required to expedite and elucidate the identification and referral of children with genetic disorders in Saudi Arabia. IMPLICATIONS FOR REHABILITATION The process of referring children with genetic disorders to physical therapy (PT) is not clearly understood by caregivers. Providing caregivers with information about the benefits of PT for children with genetic disorders is needed to encourage adherence to PT sessions and rehabilitation plan. Caregivers expressed the need for further education on the wide spectrum of genetic disorders. Caregivers experienced costly and lengthy genetic testing process that often lead to inconclusive results which could delay the referral process. Alternative solutions should be considered to provide these children with early access to rehabilitation services including PT. One of these solutions could be implementing regular screening and monitoring as well as parent education to aid in detecting delays and expedite the referral process.

Development of type-2 Pythagorean fuzzy set with its application to sustainable transport system selection

In resolving multicriteria decision-making (MCDM) problems, it is observed that the solution process is frequently affected by the available vague information together with intra-personal uncertainties associated with expert’s opinion. In most of the time fuzzy set or its existing variants fail to capture such uncertainties for making reasonable balance in decision making processes. To reduce the effects of uncertainties, this paper introduces a new variant of fuzzy set, viz., type-2 Pythagorean fuzzy set (T2PFS). The main advantage of using T2PFS in MCDM is that the membership and non-membership values of each argument are presented by Pythagorean fuzzy numbers rather than using fuzzy number or intuitionistic fuzzy number. In model formulation process, at first, T2PFS is explicitly formulated and some basic operations are defined. Subsequently, four aggregation operators are constructed to aggregate the arguments, and their properties are verified. Afterwards, two information measures and a score function are defined. Moreover, two MCDM algorithms based on the developed aggregation operators and TOPSIS are framed. To show the application potentiality of the proposed method, a case study relating to the selection of sustainable transport system in India is considered and solved. In the case study five different fuel-based vehicles, mostly used in India in recent times, are evaluated by the experts as transport alternatives with the consideration of thirteen sustainability related criteria. From the achieved result it is found that in future electric cars would become the most sustainable vehicle in India. Sensitivity analysis of the developed models and comparative study with existing fuzzy and non-fuzzy MCDM methods establish effectiveness of the developed models. This study may facilitate transport authority to select appropriate transport system in connection with sustainable development of a region.

Can Oncologists Prompt Patient Prognostic Awareness to Enhance Decision-Making? Data From the NEOetic Study.

Anti-neoplastic therapy improves the prognosis for advanced cancer, albeit it is not curative. An ethical dilemma that often arises during patients' first appointment with the oncologist is to give them only the prognostic information they can tolerate, even at the cost of compromising preference-based decision-making, versus giving them full information to force prompt prognostic awareness, at the risk of causing psychological harm. We recruited 550 participants with advanced cancer. After the appointment, patients and clinicians completed several questionnaires about preferences, expectations, prognostic awareness, hope, psychological symptoms, and other treatment-related aspects. The aim was to characterize the prevalence, explanatory factors, and consequences of inaccurate prognostic awareness and interest in therapy. Inaccurate prognostic awareness affected 74%, conditioned by the administration of vague information without alluding to death (odds ratio [OR] 2.54; 95% CI, 1.47-4.37, adjusted P = .006). A full 68% agreed to low-efficacy therapies. Ethical and psychological factors oriented first-line decision-making, in a trade-off in which some lose quality of life and mood, for others to gain autonomy. Imprecise prognostic awareness was associated with greater interest in low-efficacy treatments (OR 2.27; 95% CI, 1.31-3.84; adjusted P = .017), whereas realistic understanding increased anxiety (OR 1.63; 95% CI, 1.01-2.65; adjusted P = 0.038), depression (OR 1.96; 95% CI, 1.23-3.11; adjusted P = .020), and diminished quality of life (OR 0.47; 95% CI, 0.29-0.75; adjusted P = .011). In the age of immunotherapy and targeted therapies, many appear not to understand that antineoplastic therapy is not curative. Within the mix of inputs that comprise inaccurate prognostic awareness, many psychosocial factors are as relevant as the physicians' disclosure of information. Thus, the desire for better decision-making can actually harm the patient.

A new framework for green selection of material handling equipment under fuzzy environment

In the rapidly changing global circumstances, managements of industrial organizations are making decisions for their survival in business atmosphere in future. Decision makers in industries are steering their respective organizations towards for appropriate decision making satisfying the condition of ‘Go green’. Appropriate decision making in fuzzy environment is always a hard task. The current investigation explores a new multi criteria decision making approach for green selection of material handling equipment under fuzzy environment. The proposed technique has the capability of capturing effects of economical, environmental and social factors of benefit, non-benefit and target based criteria under uncertainty and vague information. The proposed method is illustrated with a suitable example on material handling equipment selection under fuzzy environment. The result clearly shows that the proposed technique is useful and effective in the decision making process regarding green material handling selection under fuzzy environment.

Flat Roofs Renovation Planning on Public Buildings Using Fuzzy Multi-Criteria Analysis

Renovation of flat roofs typically involves repairing or replacing the existing roof to improve its performance and extend its lifespan. The renovation process may include a range of tasks depending on the condition of the roof, such as repairing leaks, replacing damaged or deteriorated materials, adding insulation, or upgrading drainage systems. This research aim was to establish the priority of renovation of flat roofs of the public building based on the principles of multi-criteria analysis and fuzzy set theory, using the multi-criteria method PROMETHEE II in fuzzy logic form (F-PROMETHEE II). The proposed approach is adequate due to its ability to transform the uncertain and vague information received from an expert into a fuzzy number. This way, the objective outcome can be obtained, the criteria conflict removed and the alternatives ranking and mutual comparison enabled. It was necessary to analyze the existing literature, the flat roofs of a public building in terms of their current condition, and define the main goals and criteria for the roof renovation project. Based on the defined goals and criteria, the roofs are evaluated and ranked according to the priority for renovation. The planning process of renovation of flat roofs was carried out specifically on the building of the Faculty of Civil Engineering, Architecture and Geodesy in Split.