Analyzing conflicts between multiple objects within fuzzy information systems (ISs) and providing effective conflict resolution are challenging tasks due to the complexity and uncertainty of the real world. This article proposes a three-way conflict analysis and resolution model for intuitionistic fuzzy ISs (IFISs) based on three-way concept analysis (3WCA). First, the straight and vertical distances between intuitionistic fuzzy values (IFVs) are defined to obtain an intuitionistic fuzzy probability-credibility distribution. Second, intuitionistic fuzzy optimistic and pessimistic formal contexts are proposed based on two novel binary relations of intuitionistic fuzzy information. Afterward, two intuitionistic fuzzy concept lattices are obtained to analyze the marginal conflict degrees among multiple objects via consistency, inconsistency, and uncertainty attribute sets. By introducing three pairs of thresholds, the trisections of object pairs, objects, and attributes are derived through the three-way decision (3WD) process. Specifically, the maximal alliance unit, minimal conflict unit, and feasible strategy set are identified according to the three-way conflict analysis results. Subsequently, the consistency measure extracted from the intuitionistic fuzzy distance matrix is utilized to rank all feasible strategies. Finally, the experimental results are conducted to verify the effectiveness, superiority, and feasibility of the proposed model.

Enhancing sustainability in urban areas relies heavily on achieving an optimal increase in building density. However, identifying suitable sites for high-rise urban developments could be challenging. One approach to tackle this problem involves the utilization of multi-indicator land suitability analysis in the process of land-use planning. This approach plays an essential role in mitigating the fragmentation of natural habitats, promoting social integration, reducing air pollution, and preserving water and natural resources at both local and regional levels. This study aims to evaluate infill land suitability for high-rise urban development in Ahvaz, Iran. Methodologically, this study introduces an integrated Boolean–fuzzy geographic information systems framework combined with the fuzzy best–worst method, which enhances decision reliability by systematically handling uncertainty in expert judgments while reducing inconsistency compared to conventional weighting methods such as analytic hierarchy process. The findings indicate that approximately 54 percent of the city’s land areas are unsuitable for high-rise urban development. Furthermore, among the remaining suitable areas, about 30 percent were identified as priorities for high-rise development. Priority areas are primarily located in densely populated neighborhoods with higher socioeconomic status and better access to services. This study is significant because analyzing the suitability of urban land plays a crucial role in formulating effective land-use policies that promote sustainable urban development. It also helps prioritize management actions and identify spatial development patterns that yield maximum efficiency.

Behavioral three-way decision based multi-attribute decision-making for credible hesitant fuzzy information systems

Foundational theories of hesitant fuzzy sets and hesitant fuzzy information systems and their applications for multi-strength intelligent classifiers

Among many other selection criteria, multi-attribute decision-making is a tool used in various industries, including risk control, numerical examination, cybercrime investigation, networking, etc. Aczel-Alsina aggregation operators are suitable to mitigate the effects of inconsistent data. In this article, we present a few novel methodologies to account for T-spherical fuzzy set data using Aczel-Alsina aggregation techniques. The T-spherical fuzzy Aczel-Alsina weighted average operator and the T-spherical fuzzy Aczel-Alsina ordered weighted average are two examples of these novel, unique approaches. The T-spherical fuzzy Aczel-Alsina weighted geometric and T-spherical fuzzy Aczel-Alsina ordered weighted geometric operators are novel strategies that we also introduce. We look at a few unique situations and noteworthy properties to show the persistence and effectiveness of the techniques that are provided. A method for leveraging the T-spherical fuzzy information system to resolve the electric vehicle selection problem is also described. Due to the high cost of petrol and the current financial difficulties faced by middle-class households, this problem is chosen. An experimental case study is also built. To calculate the accuracy and authority of the present developments, we contrast the outcomes of formerly used methods with the aggregation operators that are presently available.

An Overlap Function-Based Three-Way Model in Interval-Valued Hesitant Fuzzy Information Systems: A Case Study in Mine Siting

A distance-based three-way conflict analysis for fuzzy multiset

A prospect theory-driven three-way decision framework: Integrating prior probability tolerance and dominance relations in fuzzy incomplete information systems

Site selection for biomass-solar hybrid renewable energy facilities: Spatial modelling based on fuzzy logic-geographic information systems

Incremental attribute reduction for dynamic fuzzy decision information systems based on fuzzy knowledge granularity

Hesitant fuzzy information systems have been widely applied in decision-making due to their ability to handle uncertain information. In addition, dominance relationships are often taken into account in many practical decision-making problems. Therefore, it is of great significance to conduct research on hesitant fuzzy information systems involved with dominance relations. In this study, we introduce dominance relations in a hesitant fuzzy information system to make it a dominance-based hesitant fuzzy information system, which can provide a solid new idea for comparing hesitant fuzzy elements. Furthermore, a hesitant fuzzy dominance-based rough set model is constructed, and an attribute reduction method is designed to simplify the dominance-based hesitant fuzzy information system. Further, we propose lower and upper approximation discernibility matrices in the dominance-based hesitant fuzzy decision information system to extract decision rules. In addition, two numerical examples are given to demonstrate the effectiveness of the proposed attribute reduction methods.

Fused Decision Rules of Multi-Intuitionistic Fuzzy Information Systems Based on the D-S Evidence Theory and Three-Way Decisions

Attribute reduction based on intuitionistic fuzzy dominance mutual information in intuitionistic fuzzy information systems

Attribute Reduction of Type-2 Fuzzy Information Systems Based on $$\sigma $$ Self-information

Conflict is ubiquitous in human society and has a profound impact on various fields such as the economy, politics, law, and military. Many scholars have focused on exploring the internal mechanisms and potential solutions to conflicts. Notably, describing agents’ attitudes is an effective way to construct a conflict model. However, in decision-making, agents’ attitudes on issues are often vague and ambiguous. Pythagorean fuzzy set can deal with fuzzy information more accurately than intuitionistic fuzzy set. On the basis of this understanding, we investigate the conflicts from the perspective of Pythagorean fuzzy set. Firstly, we use Pythagorean fuzzy numbers to express the attitudes of agents on issues, and subsequently establish a Pythagorean fuzzy conflict information system. Secondly, we classify agents into three categories by a pair of thresholds to establish a trisected agent set model with risk preference. Thirdly, we construct a three-way conflict analysis model based on multi-granulation Pythagorean fuzzy decision-theoretic rough set and discuss both global and local conflicts by combining conflict analysis with multi-granulation decision-theoretic rough set. Finally, we discuss the relationships and properties of the proposed conflict analysis models.

Solar power is an alternative energy derived from the sun. Solar power is more environmentally friendly and sustainable than burning fossil fuels which releases harmful greenhouse gas emissions. Therefore, this study aims to evaluate a reliable solar panel based on certain characteristics by incorporating the theory of the decision-making process. To serve this goal, this study discusses a well-known aggregation model of the q-rung orthopair fuzzy set, which is a broader and flexible environment of fuzzy sets and intuitionistic fuzzy sets used to handle unpredictable information of human opinions. The key components of this article are to demonstrate some realistic operations of Sugeno–Weber triangular norms considering q-rung orthopair fuzzy information. These operations provide authentic estimated information during the decision-making process. We developed a class of new aggregation operators using the q-rung orthopair fuzzy information system, including q-rung orthopair fuzzy Sugeno–Weber power weighted average and q-rung orthopair fuzzy Sugeno–Weber power weighted geometric operators. Some realistic characteristics and special cases are also demonstrated to show the compatibility of the proposed methodologies. An innovative approach to the multi-attribute decision-making problem is utilized to resolve different real-life applications considering various criteria or attributes. To show the intensity and applicability of the proposed approaches, we explored a numerical example for efficient solar panel selection based on the proposed methodologies. Furthermore, we presented a comprehensive comparison technique to compare the findings of the existing methods with the proposed aggregation approaches. Finally, the proposed research work is summarized, and the future prospects are discussed.

In this article, we modeling a set of data which represent the temperatures per day in the governorates of Iraq, which were taken from the General Authority for Meteorology and Seismic Monitoring for the year (2021) under variety statistical models, namely, linear, logarithmic, inverse, quadratic, cubic, compound , S, power, Growth , exponential, and logistic model by using classical principle and fuzzy principle by building a fuzzy information system under vary values of Alfa-cuts to generate membership values to the set of Temperatures to obtain a classical set that takes into account the inaccuracy in data collection , significance of the models was testing by the probabilistic value Sig. to reach to the best model that represents the data of Temperatures , we are compare among them by using mean square error (MSE). We are concluded that the use of the principle of fuzziness in the fitting of the models led to an increase in the accuracy of these models, and the mean squares error (MSE) for all the models that have been fitted is reduced on whether the data are traditional. We are also note that the best model in representing the Temperatures data with is the is power model to having it the lowest (MSE) among all the models, followed by the, Compound, Exponential Growth models at all

In the existing conflict analysis models, they used a triangular fuzzy number on [0, 1] to describe the range of an agent’s attitude towards an issue, but there are still some shortcomings in describing the specific attitude and degree of conflict represented by the triangular fuzzy number. In this paper, the conflict analysis model is extended, improved and perfected. Firstly, the expectation of triangular fuzzy number is used in the [-1, 1] triangular fuzzy information system to reasonably express the specific attitudes represented by a triangular fuzzy number. Secondly, the weights of each issue are obtained by using the Sugeno measure, which determines the total attitude of the agent towards all issues. Thirdly, the relationship between agents is obtained with the help of the weighted distance of triangular fuzzy numbers. Finally, the thresholds α and β are calculated by means of triangular fuzzy decision theory rough sets.

Ontologies are used to semantically enrich different types of information systems (IS), ensure a reasoning on their content and integrate heterogeneous IS at the semantical level. On the other hand, fuzzy theory is employed in IS for handling the uncertainty and fuzziness of their attributes, resulting in a fully fuzzy IS. As such, ontology- and fuzzy-based IS (i.e. ontology and fuzzy IS) are being developed. So, in this paper, we present a bibliometric analysis of the ontology and fuzzy IS concept to grasp its main ideas, and to increase its body of knowledge by providing a concept map for ontology and fuzzy IS. The main results obtained show that by adding ontologies and fuzzy theory to traditional ISs, they evolve into intelligent ISs capable of managing fuzzy and semantically rich (ontological) information and ensuring knowledge recognition in various fields of application. This bibliometric analysis would enable practitioners and researchers gain a comprehensive understanding of the ontology and fuzzy IS concept that they can eventually adopt for development of intelligent IS in their work.

Exploring static rebalancing strategies for dockless bicycle sharing systems based on multi-granularity behavioral decision-making