MADM Problems Research Articles

A ranking framework for the selection of IoT cloud platforms using hybrid multi-attribute decision-making method

PurposeThe Internet of Things (IoT) cloud platforms provide end-to-end solutions that integrate various capabilities such as application development, device and connectivity management, data storage, data analysis and data visualization. The high use of these platforms results in their huge availability provided by different capabilities. Therefore, choosing the optimal IoT cloud platform to develop IoT applications successfully has become crucial. The key purpose of the present study is to implement a hybrid multi-attribute decision-making approach (MADM) to evaluate and select IoT cloud platforms.Design/methodology/approachThe optimal selection of the IoT cloud platforms seems to be dependent on multiple attributes. Hence, the optimal selection of IoT cloud platforms problem is modeled as a MADM problem, and a hybrid approach named neutrosophic fuzzy set-Euclidean taxicab distance-based approach (NFS-ETDBA) is implemented to solve the same. NFS-ETDBA works on the calculation of assessment score for each alternative, i.e. IoT cloud platforms, by combining two different measures: Euclidean and taxicab distance.FindingsA case study to illustrate the working of the proposed NFS-ETDBA for optimal selection of IoT cloud platforms is given. The results obtained on the basis of calculated assessment scores depict that “Azure IoT suite” is the most preferable IoT cloud platform, whereas “Salesman IoT cloud” is the least preferable.Originality/valueThe proposed NFS-ETDBA methodology for the IoT cloud platform selection is implemented for the first time in this field. ETDBA is highly capable of handling the large number of alternatives and the selection attributes involved in any decision-making process. Further, the use of fuzzy set theory (FST) makes it very easy to handle the impreciseness that may occur during the data collection through a questionnaire from a group of experts.

Selecting optimal celestial object for space observation in the realm of complex spherical fuzzy systems

The sensible selection of celestial objects for observation by the James Web Space Telescope (JWST) is pivotal for the precise decision-making (DM) process, aligning with scientific priorities and instrument capabilities to maximize valuable data acquisition to address key astronomical questions within the constraints of limited observation time. Aggregation operators are valuable models for condensing and summarizing a finite set of data of imprecise nature. Utilization of these operators is critical when addressing multi-attribute decision-making (MCDM) challenges. The complex spherical fuzzy (CSF) framework effectively captures and represents the uncertainty that arises in a DM problem with more precision. This paper presents two novel aggregation operators, namely the complex spherical fuzzy Yager weighted averaging (CSFYWA) operator and the complex spherical fuzzy Yager weighted geometric (CSFYWG) operator. Many fundamental structural properties of these operators are delineated, and thereby an improved score function is suggested that addresses the limitations of the existing score function within the CSF system. The newly defined operators are applied to formulate an algorithm for MADM problems to tackle the challenges of ambiguous data in the selection process. Moreover, these strategies are effectively applied to handle the MADM problem of selecting the optimal astronomical object for space observation within the CSF context. Additionally, a comparative analysis is also performed to demonstrate the validity and superiority of the proposed techniques compared to the existing strategies.

Group decision making based on cubic fermatean Einstein fuzzy weighted geometric operator

Cubic Fermatean fuzzy sets offer a flexible and powerful tool for decision-making, permitting decision-makers to incorporate uncertainty and reluctance into their decision-making process, and enabling them to make more informed and effective decisions in complex and uncertain situations. We present cubic fermatean fuzzy set and operational laws. We offer some cubic fermatean fuzzy numbers, as well as some cubic fermatean fuzzy aggregation operators, such as the cubic fermatean Einstein fuzzy weighted geometric operator, the cubic fermatean Einstein fuzzy ordered weighted geometric operator, and the cubic fermatean Einstein fuzzy hybrid weighted geometric operator. To demonstrate the solution to complicated real-life problems, an algorithm for the MADM problem is established under a system of cubic fermatean fuzzy information. To check the possibility of proposed aggregation approaches, we provided a numerical example to choose the desirable best options by using conceived approaches. To disclose the suppleness and applicability of designed methods, sensitive study, and comparative study are illustrated by complementary findings of existing methods with presently developed approaches.

Cosine and cotangent similarity measures for intuitionistic fuzzy hypersoft sets with application in MADM problem

Intuitionistic fuzzy hypersoft sets (IFHSSs) are a novel model that is projected to address the limitations of Intuitionistic fuzzy soft sets (IFSSs) regarding the entitlement of a multi-argument domain for the approximation of parameters under consideration. It is more flexible and reliable as it considers the further classification of parameters into their relevant parametric valued sets. In this paper, we proposed some trigonometric (cosine and cotangent) similarity measures and their weighted trigonometric similarity measures (SMs). Trigonometric Similarity measures (SMs) for intuitionistic fuzzy hypersoft sets (IFHSSs) are significantly implied to check the similarity measures and help to determine the similarity between different factors. Also, in order to evaluate the validity of the significant study and apply the results to a daily life problem. We use them to solve problems involving the selection of renewable energy sources. According to several technical contributing factors, the analysis identifies the ideal location for the implementation of the energy production units. Future case studies with many features and additional bifurcation along with multiple decision-makers can use the suggested methodologies. Also, several existing structures, such as fuzzy, Pythagorean fuzzy, Neutrosophic theories, etc., can be utilized with the suggested method.

L-valued covering-based rough sets and corresponding decision-making applications

Considering L to be a complete residuated lattice, by introducing the notion of L-valued covering on an L-set (as a universe), and then an L-valued neighborhood based on it, we present the concept of L-valued covering-based rough sets. We mainly address the following issues in this paper: Firstly, we present four types of L-valued neighborhood operators and study some of their respective properties. Secondly, we construct L-valued lower (resp., upper) approximation operators and then discuss some of their properties. Finally, we try to propose a kind of MADM problem based on an L-valued covering-based rough set model (for L=[0,1]).

Dynamic bipolar fuzzy aggregation operators: A novel approach for emerging technology selection in enterprise integration

<abstract> <p>Emerging technology selection is crucial for enterprise integration, driving innovation, competitiveness, and streamlining operations across diverse sectors like finance and healthcare. However, the decision-making process for technology adoption is often complex and fraught with uncertainties. Bipolar fuzzy sets offer a nuanced representation of uncertainty, allowing for simultaneous positive and negative membership degrees, making them valuable in decision-making and expert systems. In this paper, we introduce dynamic averaging and dynamic geometric operators under bipolar fuzzy environment. We also establish some of the fundamental crucial features of these operators. Moreover, we present a step by step mechanism to solve MADM problem under bipolar fuzzy dynamic aggregation operators. In addition, these new techniques are successfully applied for the selection of the most promising emerging technology for enterprise integration. Finally, a comparative study is conducted to show the validity and practicability of the proposed techniques in comparison to existing methods.</p> </abstract>

Prioritized Aggregation Operators of GTHFNs MADM Approach for the Evaluation of Renewable Energy Sources

In this paper, firstly, we propose two new GTHFNs-prioritized aggregation operators called generalized trapezoidal hesitant fuzzy number prioritized weighted average operator and generalized trapezoidal hesitant fuzzy number prioritized weighted geometric operator. Secondly, we investigate the fundamental properties of the operators in detail such as idempotency, boundedness and monotonicity. Thirdly, we propose a method based on the developed GTHF-numbers prioritized aggregation operators for solving an MADM problem with GTHF-numbers. Fourthly, we give a numerical example of the developed method. Finally, a comparative analysis is given with some existing methods in solving an MADM problem with GTHF-numbers.

Retracted: Some Generalized T-Spherical and Group-Generalized Fuzzy Geometric Aggregation Operators with Application in MADM Problems

Retracted: Some Generalized T-Spherical and Group-Generalized Fuzzy Geometric Aggregation Operators with Application in MADM Problems

Ranking method of the generalized intuitionistic fuzzy numbers founded on possibility measures and its application to MADM problem

Ranking method of the generalized intuitionistic fuzzy numbers founded on possibility measures and its application to MADM problem

Optimizing Emergency Plane Selection in Civil Aviation Using Extended Dombi Hybrid Operators

Airports located in densely populated areas often face challenges due to asymmetrical traffic patterns. Efficient management and careful planning are required to handle the disproportionate flow of passengers, aircraft, and ground services. The significance of symmetry and asymmetry in civil aviation extends to international regulations and agreements. By harmonizing standards and practices among different nations, it is possible to achieve symmetry in safety measures and operational procedures, thereby promoting a unified and secure global aviation system. Conversely, asymmetry in regulations, infrastructure development, or technological advancements among countries can create obstacles in establishing a cohesive and equitable international aviation framework. This article discusses the weaknesses of the existing score function in handling the MADM problem in an Interval-Valued Pythagorean Fuzzy (IVPF) environment. To tackle this issue, an enhanced score function is developed as a solution. The article proposes the IVPF Dombi hybrid arithmetic and IVPF Dombi hybrid geometric operators based on IVPF information. Furthermore, the article proves some fundamental properties of these operators. In the context of recently introduced techniques using IVPF settings, an effective method is developed for selecting the best airline. Additionally, a comparative investigation is carried out to demonstrate the legitimacy and viability of this unique strategy in comparison to earlier approaches.

Ranking Method of Intuitionistic Fuzzy Numbers and Multiple Attribute Decision Making Based on the Probabilistic Dominance Relationship

The uncertainty of intuitionistic fuzzy numbers (IFNs) is further enhanced by the existence of the degree of hesitation (DH). The shortcomings of existing researches are mainly reflected in the following situations: when comparing IFNs, the comparison rules of IFNs are difficult to apply to the comparison of any two IFNs, or the relevant methods do not fully consider the uncertainty expressed by DH. Thus, the rationality of the decision results needs to be improved. On the other hand, multi-attribute decision making (DADM) based on IFNs is often not objective due to the need to determine the attribute weight. Moreover, the strict condition of attribute aggregation of classical dominance relation makes it a method that fails considering the practical application. Aiming at the comparison problem of IFNs, this paper takes probability conversion as the starting point and proposes an IFN comparison method based on the area method, which can better deal with the comparison problem of “either superior or inferior” IFNs. In addition, aiming at the MADM problem of an intuitionistic fuzzy information system, we propose an intuitionistic fuzzy probabilistic dominance relation model and construct the MADM method under the probabilistic dominance relation. The series properties of IFNs and probabilistic dominance relation were summarized and proved, which theoretically ensured the scientificity and rigor of the method. The results show that the comparison and ranking method of IFNs proposed in this paper can be applied to the comparison of any two IFNs, and the dominance degree of IFNs is presented in the form of probability, which is more flexible and practical than the classical method. The probabilistic dominance relation method based on IFNs avoids the problem of determining attribute weights subjectively or objectively, and the decision maker can reflect decision preference by adjusting decision parameters to better match the actual problem. The application of this model to a campus express site evaluation further verifies the feasibility of the proposed method and the rationality of the results. In addition, various extension problems of the model and method proposed in this paper are discussed, which pave the way for future related research. This paper constructs a complete decision-making framework through theoretical analysis and application from practical problems, which provides a reference for enriching and improving uncertain decision-making theory and the MADM method.

Averaging aggregation operators under the environment of <i>q</i>-rung orthopair picture fuzzy soft sets and their applications in MADM problems

<abstract><p><italic>q</italic>-Rung orthopair fuzzy soft set handles the uncertainties and vagueness by membership and non-membership degree with attributes, here is no information about the neutral degree so to cover this gap and get a generalized structure, we present hybrid of picture fuzzy set and <italic>q</italic>-rung orthopair fuzzy soft set and initiate the notion of <italic>q</italic>-rung orthopair picture fuzzy soft set, which is characterized by positive, neutral and negative membership degree with attributes. The main contribution of this article is to investigate the basic operations and some averaging aggregation operators like <italic>q</italic>-rung orthopair picture fuzzy soft weighted averaging operator and <italic>q</italic>-rung orthopair picture fuzzy soft order weighted averaging operator under the environment of <italic>q</italic>-rung orthopair picture fuzzy soft set. Moreover, some fundamental properties and results of these aggregation operators are studied, and based on these proposed operators we presented a stepwise algorithm for MADM by taking the problem related to medical diagnosis under the environment of <italic>q</italic>-rung orthopair picture fuzzy soft set and finally, for the superiority we presented comparison analysis of proposed operators with existing operators.</p></abstract>

Robot sensors process based on generalized Fermatean normal different aggregation operators framework

<abstract><p>Novel methods for multiple attribute decision-making problems are presented in this paper using Type-Ⅱ Fermatean normal numbers. Type-Ⅱ Fermatean fuzzy sets are developed by further generalizing Fermatean fuzzy sets and neutrosophic sets. The Type-Ⅱ Fermatean fuzzy sets with basic aggregation operators are constructed. The concept of a Type-Ⅱ Fermatean normal number is compatible with both commutative and associative rules. This article presents a new proposal for Type-Ⅱ Fermatean normal weighted averaging, Type-Ⅱ Fermatean normal weighted geometric averaging, Type-Ⅱ generalized Fermatean normal weighted averaging, and Type-Ⅱ generalized Fermatean normal weighted geometric averaging. Furthermore, these operators can be used to develop an algorithm that solves MADM problems. Applications for the Euclidean distance and Hamming distances are discussed. Finally, the sets that arise as a result of their connection to algebraic operations are emphasized in our discourse. Examples of real-world applications of enhanced Hamming distances are presented. A sensor robot's most important components are computer science and machine tool technology. Four factors can be used to evaluate the quality of a robotics system: resolution, sensitivity, error and environment. The best alternative can be determined by comparing expert opinions with the criteria. As a result, the proposed models' outcomes are more precise and closer to integer number $ \delta $. To demonstrate the applicability and validity of the models under consideration, several existing models are compared with the ones that have been proposed.</p></abstract>

Multiple attribute decision-making based on Fermatean fuzzy number

<abstract> <p>Multiple attribute decision-making concerns with production significant in our everyday life. To resolve the problems that decision makers might feel uncertain to choose the suitable assessment values among several conceivable ideals in the procedure. Fuzzy model, and its extensions are extensively applied to MADM problems. In this study, we proposed an innovative Schweizer-Sklar t-norm and t-conorm operation of FFNs, Fermatean fuzzy Schweizer-Sklar operators. They were used as a framework for the development of an MCDM method, which was illustrated by an example to demonstrate its effectiveness and applicability. Finally, a complete limitation study, rational examination, and comparative analysis of the presented approaches has been exhibited, we originate that our technique is superior in offering DMs a better decision-making choice and reducing the restrictions on stating individual partialities.</p> </abstract>

Probabilistic linguistic multi-attribute decision making approach based upon novel GMSM operators

<abstract><p>Probabilistic linguistic terms set (PLTS), a new tool for expressing uncertain decision information, is composed of all possible linguistic terms (LTs) and their related probabilities. It also increases the corresponding probability of LTs in hesitant fuzzy linguistic term set (HFLTS). On the other hand, aggregation operator is an important information fusion tool, the Maclaurin symmetric mean (MSM) operator can provide more flexibility and robustness in information fusion, and make it more suitable for solving MADM problems with independent attributes. This current study adopts the merits of PLTS and MSM operator, and then a novel probabilistic linguistic decision making approach are targeted. Firstly, the operations of two PLTSs are redefined based upon Archimedean t-norm (ATN) and Archimedean t-conorm (ATC); Secondly, the probabilistic linguistic generalized MSM operator (PLGMSM) is proposed based on ATN and ATC, some properties of PLGMSM are investigated, then some special PLGMSM operators have been studied in detail when the parameters take different values and the generator of ATN takes different functions. Thirdly, the weighted probabilistic linguistic generalized MSM operator (WPLGMSM) is studied along with some properties of PLGMSM, some special WPLGMSM operators have been also investigated in detail when the parameters take different values and the generator of ATN takes different functions. Finally, on the basis of our proposed aggregation operators, the aggregated-based decision making approach is designed and an example is supplied to manifest the effectiveness of the proposed approach. Furthermore, some comparison analyses with extant decision approaches are carried out to illustrate the validity and feasibility of the proposed approach.</p></abstract>

Aggregation Operators Based on Algebraic t-Norm and t-Conorm for Complex Linguistic Fuzzy Sets and Their Applications in Strategic Decision Making

Aggregation operators perform a valuable and significant role in various decision-making processes. Averaging and geometric aggregation operators are both used for capturing the interrelationships of the aggregated preferences, even if the preferences are independent. The purpose of this paper is to analyze the theory of complex linguistic fuzzy (CLF) sets and their important laws, such as algebraic laws, score values, and accuracy values, and to describe the relationship between the score and accuracy values with the help of their properties. Additionally, based on the proposed CLF information, we introduce the theory of CLF weighted averaging (CLFWA), CLF ordered weighted averaging (CLFOWA), CLF hybrid averaging (CLFHA), CLF weighted geometric (CLFWG), CLF ordered weighted geometric (CLFOWG), and CLF hybrid geometric (CLFHG) operators. The fundamental properties and some valuable results of these operators are evaluated, and their particular cases are described. Based on the presented operators, a technique for evaluating the “multi-attribute decision-making” (MADM) problems in the consideration of CLF sets is derived. The superiority of the derived technique is illustrated via a practical example, a set of experiments, and significant and qualitative comparisons. The illustration results indicate that the derived technique can be feasible and superior in evaluating CLF information. Further, it can be used for determining the interrelationships of attributes and the criteria of experts. Moreover, it is valuable and capable of evaluating the MADM problems using CLF numbers.

Sistem Penentuan Keputusan Seleksi Pemilihan Asisten Dosen Sistem Informasi Dengan Penerapan Metode TOPSIS

The selected lecturer assistant can help the lecturer as a liaison with students so that the lecturer assistant can affect the quality of student learning outcomes. It's just that the acceptance of teaching assistants that were previously less effective and efficient because they still use manual rules and procedures, so they can spend time in their determination, besides that the assessment is still subjective or still looks at the closest family to one candidate in the acceptance of teaching assistants and there are no requirements The right specialty in determining lecturer assistants, causing problems, namely difficulties in determining a suitable lecturer assistant and having skills in his field, In making the wrong decision it results in distrust of the accepted lecturer assistant so that it can have an impact on decreasing students' understanding and skills of the subject. Therefore, a system is needed to solve a problem by providing decision making, so that it can help decision makers in determining the graduation of the right lecturer assistant. The TOPSIS method is applied by the author in this journal, because the TOPSIS method is able to overcome MADM problems so it is very practical in completing a decision, besides that the alternative TOPSIS method selected is obtained from the calculation process and after that ranking is carried out, so that from the process the best alternative is obtained from criteria that have been determined, so that it can be used as a solution in decision making. Because the system takes the value from the best criteria, the accuracy obtained in this system exceeds 90%.

Generalized hesitant fuzzy numbers and their application in solving MADM problems based on TOPSIS method

Generalized hesitant fuzzy numbers and their application in solving MADM problems based on TOPSIS method

Similarity Measures and Their Applications in Multiple Attribute Decision- Making Under Cubic Hesitant Environment

Background: Cubic Hesitant Fuzzy Set (CHFS) is a hybrid set that can express uncertain and hesitancy fuzzy information simultaneously. Objective: In this paper, we introduced three trigonometric similarity measures (e.g., cosine, tangent, and cotangent similarity measures) to measure the degree of similarity between the alternative and the ideal set under the CHFS environment. Various desirable characteristics of the cubic hesitant fuzzy set are studied. Then, we developed multiple attribute decision-making methods based on the weighted cosine, tangent, and cotangent similarity measures of CHFSs. Methods: In this research, we presented the similarity measures of CHFSs based on the cosine, tangent, and cotangent functions. Then, illustrative examples of construction project management with CHFS information are presented to show the effectiveness and feasibility of the proposed Multiattribute Decision-making (MADM) method under CHFS environments. Result: Based on the weighted similarity measures between each alternative and the ideal set, this method provides the ranking order according to the values of their similarity measure. The best alternatives can be easily identified from the ranking order obtained. Conclusion: Based on the comparison of the decision results obtained, the tangent and cotangent similarity measures are better in similarity identification than the cosine similarity measure for solving MADM problems under a cubic hesitant environment.

Cq-ROFRS: covering q-rung orthopair fuzzy rough sets and its application to multi-attribute decision-making process

Pythagorean fuzzy sets (briefly, PFSs) were created as an upgrade to intuitionistic fuzzy sets (briefly, IFSs) which helped to address some problems that IFSs couldn’t solve. The definition of q-rung orthopair fuzzy sets (briefly, q-ROFS) is then declared to generalize and solve PFS and IFS failures. Using the concept of PF beta -neighborhood, Zhan et al. defined the description of the covering through the Pythagorean fuzzy rough set (briefly, CPFRS). Hussain et al. also developed the concept of q-ROF beta -neighborhood to build the concept of covering through q-rung orthopair fuzzy rough sets (Cq-ROFRS). To enhance the results in Zhan et al.’s and Hussain et al.’s method and in a related context, the concept of PF complementary beta -neighborhood is constructed. Hence, using PF beta -neighborhood and PF complementary beta -neighborhood, three novel kinds of CPFRS are investigated and the related characteristics are analyzed. The interrelationships between Zhan et al.’s approach and our approaches are also discussed. Besides, the concept of q-ROF complementary beta -neighborhood is examined. Three new Cq-ROFRS models are differentiated using the principles of q-ROF beta -neighborhood and q-ROF complementary beta -neighborhood. As a result, the related properties and relationships between these various models and Hussain et al.’s model are established. Because of these correlations, we may consider our approach to be a generalization of Zhan et al.’s and Hussain et al’s approaches. Finally, we developed applications to solve MADM problems using CPFRS and Cq-ROFRS, as well as variances of the two methods using numerical examples are presented.