Group Decision-making Technique Research Articles

Comprehensive Analysis Using Probabilistic Linguistic Group Decision-Making and MEREC Technique With Sustainable Development Evaluation in Higher Education

University administrators should organize and coordinate various affairs, scientifically utilize resources such as budget, teaching equipment, and faculty, continuously improve teaching quality, and ensure the sustainable development of educational management. The sustainable development evaluation in higher education management is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Exponential TODIM (ExpTODIM) and PROMETHEE technique was employed to put forward the MAGDM issues. The probabilistic linguistic term sets (PLTSs) are employed as a technique for characterizing uncertain information during the sustainable development evaluation in higher education management. In this paper, the probabilistic linguistic ExpTODIM-MABAC (PL-ExpTODIM-MABAC) technique is constructed to put forward the MAGDM under PLTSs. The MEREC technique is employed to obtain the weight values under PLTSs. Finally, a numerical example for sustainable development evaluation in higher education management is put forward to validate the ExpTODIM-MABAC technique.

Selection and application of building material suppliers based on the 2-tuple linguistic neutrosophic number combined grey relational analysis method

The quality of building materials will affect the implementation effect of construction projects. To ensure the service capacity of building materials, it is necessary to do a good job in selecting suppliers. In the specific evaluation of building material suppliers, after evaluation, suppliers with poor quality are excluded to ensure the quality of material supply, reasonably improve the construction effect of the building project, meet the construction needs of the building project, and improve the quality of the building project. The selection and application of building material suppliers (BMSs) is a multiple-attribute group decision-making (MAGDM) technique. In this study, the 2-tuple linguistic neutrosophic number combined grey relational analysis (2TLNN-CGRA) technique is constructed based on the classical grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNNSs). Finally, a numerical example for building material supplier selection was constructed and some comparisons is constructed to illustrate the 2TLNN-CGRA technique. The main contribution of this study is constructed: (1) the 2TLNN-CGRA technique is implemented to cope with the MAGDM under 2TLNSs; (2) the 2TLNN-CGRA technique is implemented in line with the 2TLNN Hamming distance (2TLNNHD) and 2TLNN Euclidean distance (2TLNNED) simultaneously under 2TLNSs; (3) the numerical example for building material supplier selection is implemented to show the 2TLNN-CGRA technique; and (4) some efficient comparative studies are constructed with several existing decision techniques.

Multi-attribute group decision-making for supplier selection based on Dombi aggregation operators under the system of spherical fuzzy Hamy mean

Supplier selection is a very crucial process within a business or commercial enterprise because it depends upon different components like reliability, customer need, services, cost and reputation. A suitable supplier is familiar with developing a relationship between customer needs and business. To serve this purpose, the multiple attribute group decision-making (MAGDM) technique is a well-known and efficient aggregation model used to evaluate flexible optimal options by considering some appropriate criteria or attributes. Experts face some sophisticated challenges during the decision-making process due to uncertain and ambiguous information about human opinions. To address such conditions, we explore the notion of spherical fuzzy sets (SFS) and their reliable operations. Some flexible operational laws of Dombi t-norms are also developed in light of spherical fuzzy (SF) information. Combining the theory of Hamy mean (HM) models and Dombi aggregation tools, some robust strategies are also studied in this research work. The main objectives of this article are to propose some dominant strategies in the presence of SF information including spherical fuzzy Dombi Hamy mean (SFDHM), spherical fuzzy Dombi weighted Hamy mean (SFDWHM), spherical fuzzy Dombi Dual Hamy mean (SFDDHM) and spherical fuzzy Dombi weighted Dual Hamy mean (SFDWDHM) operators. The MAGDM techniques are utilized to evaluate the flexibility of our derived methodologies under considering SF information. An experimental case study is utilized to evaluate a notable supplier enterprise under consideration of our developed methodologies. Finally, a comprehensive overview of our research work is also presented.

TODIM-GRA based multiple-attribute group decision-making technique for risk assessment of intangible cultural heritage tourism development under interval-valued pythagorean fuzzy circumstances

Intangible cultural heritage can be said to be an important component of tourism resources. With the rapid development of society in today’s era, tourism development and intangible cultural heritage protection have gradually attracted attention from Chinese society, and in recent years, it has attracted high attention from relevant departments of the Chinese government. Tourism development has a “dual” impact on the protection of intangible cultural heritage, with both positive and negative impacts. The risk assessment of intangible cultural heritage tourism development is a MAGDM problems. Recently, the TODIM and GRA technique has been employed to manage MAGDM issues. The interval-valued Pythagorean fuzzy sets (IVPFSs) are employed as a tool for characterizing uncertain information during the risk assessment of intangible cultural heritage tourism development. In this paper, the interval-valued Pythagorean fuzzy TODIM-GRA (IVPF-TODIM-GRA) technique is construct to manage the MAGDM under IVPFSs. Finally, a numerical case study for risk assessment of intangible cultural heritage tourism development is employed to validate the proposed technique.

Aczel–Alsina T-norm based group decision-making technique for the evaluation of electric cars using generalized orthopair fuzzy aggregation information with unknown weights

Data management and finding precise outcomes from large amounts of information are among the biggest challenges for scientists. The technique of multi-attribute group decision-making (MAGDM) is a valuable tool for investigating fuzzy data precisely. The key objective of the paper is to redefine the q-rung orthopair (q-RO) fuzzy set (FS) (q-ROFS) in the term of interval-valued and proposed new aggregation operators (AOs) based on the Aczel-Alsina (AA) t-norm (TN) and t-conorm (TCN) operations. The AA operational laws are a generalized form of existing TNs and TCNs and give more reliable results because they can fluctuate in their parametric values. The concept of interval-valued enlarges the space of membership degree (MD) and non-membership degree (NMD) for decision-makers. By taking qth power, the interval-valued q-ROFS (IV-q-ROFS) structure. The IV-q-ROFS can handle the uncertainty and vagueness in data, then interval-valued intuitionistic FS (IV-IFS) and interval-valued Pythagorean FS (PyFS) (IV-PyFS) and provide accurate results. The thought of power AOs (PAOs) makes the relationship between weight vectors and reduces the chances of uncertainty in aggregated results. By taking advantage of PAOs, this article is devoted to introducing the interval-valued q-ROF Aczel-Alsina power-weighted averaging (IV-q-ROFAAPWA) and interval-valued q-ROF Aczel-Alsina power-weighted geometric (IV-q-ROFAAPWG) operators. The fundamental axioms of AOs, idempotency, boundedness, and monotonicity, are also discussed. To illustrate the importance of suggested AOs, the real-life problem of electric car selection was solved by applying the MAGDM method using the proposed IV-q-ROFAAPWA and IV-q-ROFAAPWG operators. The comparison of proposed AOs with currently present AOs is also part of the article. We finally constructed solid conclusions.

Pythagorean fuzzy Aczel Alsina Hamy mean aggregation operators and its applications to multi-attribute decision-making process

Multiple-attribute group decision-making (MAGDM) technique is often used to make decisions when several optimal options are under consideration. It can be difficult to select a reasonable optimal option for the decision maker under consideration of insufficient information. The theory of Hamy mean (HM) operators are used to express correlation among different input arguments and provide a smooth approximation during the decision-making process. Recently, Aczel Alsina aggregating expressions gained a lot of attention from numerous mathematicians under different fuzzy circumstances. This article aims to illustrate the notion of a Pythagorean fuzzy (PyF) set (PyFS) with some restricted constraints, such as a sum of the square of truth membership value and falsity membership value. We developed a series of new approaches under consideration of the HM tools, including PyF Aczel Alsina Hamy mean (PyFAAHM), and PyF Aczel Alsina weighted Hamy mean (PyFAAWHM) operators. Further, we also extend the theory of Dual Hamy mean (DHM) operators and derived a series of new methodologies such as PyF Aczel Alsina Dual Hamy mean (PyFAADHM) and PyF Aczel Alsina weighted Dual Hamy mean (PyFAAWDHM) operators. To demonstrate the flexibility of our derived approaches, we illustrate an application of a multinational company considering the MAGDM technique. An experimental case study is also illustrated to evaluate a reasonable option from a group of options. We see the advantages and compatibility of our findings by comparing the results of existing approaches with the results of currently discussed methodologies.

Novel Multi-Criteria Group Decision Making Method for Production Scheduling Based on Group AHP and Cloud Model Enhanced TOPSIS

Optimized production scheduling can greatly improve efficiency and reduce waste in the steel manufacturing industry. With the increasing demands on the economy, the environment, and society, more and more factors need to be considered in the production scheduling process. Currently, only a few methods are developed for the comprehensive evaluation and prioritization of scheduling schemes. This paper proposes a novel MCGDM (multi-criteria group decision making) method for the ranking and selection of production scheduling schemes. First, a novel indicator system involving both qualitative and quantitative indicators is put forward. Diverse statistical methods and evaluation functions are proposed for the evaluation of quantitative indicators. The evaluation method of qualitative indicators is proposed based on heterogeneous data, cloud model theory, and group decision-making techniques. Then, a novel Group AHP model is proposed to determine the weights of all evaluation indicators. Finally, a novel cloud-model-enhanced TOPSIS (technique for order of preference by similarity to ideal solution) method is proposed to rank alternative production scheduling schemes. A practical example is presented to show the implementation details and demonstrate the feasibility of our proposed method. The results and comparative analysis indicate that our hybrid MCGDM method is more reasonable, flexible, practical, and effective in evaluating and ranking production scheduling schemes in an uncertain environment.

An ExpTODIM-MACONT Based Multiple-Attribute Group Decision-Making Technique for Smart Classroom Teaching Evaluation of Basic English Under Interval-Valued Pythagorean Fuzzy Circumstances

An ExpTODIM-MACONT Based Multiple-Attribute Group Decision-Making Technique for Smart Classroom Teaching Evaluation of Basic English Under Interval-Valued Pythagorean Fuzzy Circumstances

A hybrid weighting method with a new score function for analyzing investment priorities in renewable energy

The transition to renewable energy sources is a critical challenge that emerging economies are facing today. Investment decisions related to the renewable energy transition involve multiple criteria and objectives that need to be analyzed and evaluated before making informed choices. This study develops a score function, the partitioned dual Muirhead mean (PDMM), the q-rung orthopair fuzzy weighted PDMM (q-ROFWPDMM), and a hybrid weighting technique based on Shannon entropy and decision-making trial and evaluation laboratory (DEMATEL) methods for q-rung orthopair fuzzy information. Additionally, a new multi-criteria group decision-making (MCGDM) technique is constructed using these developed approaches. This approach is applied to analyze the investment priorities of the renewable energy transition for emerging economies (E7 countries). It considers twelve criteria in four dimensions: (i) regulations to increase the renewable potential; (ii) supply of renewable materials; (iii) encouragement of private sector investors in the renewable industry; and (iv) awareness of renewable energy consumption. The results of the hybrid weight finding technique indicate that the “reducing market risk” criterion is the most important for investment priorities. The application of the novel MCGDM technique reveals compelling results, indicating that China has emerged as the frontrunner in effectively bolstering renewable energy investment projects. Moreover, a sensitivity analysis is presented, followed by a comparative analysis in which four conventional and three partition based aggregation operators are considered to show the effectiveness of the developed approach. The proposed approach provides a comprehensive analysis of investment priorities for the renewable energy transition, aiding decision-makers in making informed investment choices.

ECCANS: Enhanced CRITIC-based Context-Aware Network Selection algorithm for 5G HetNet

The ability of mobility management to provide seamless communication and better quality of service (QoS) to the multiservice multimode mobile node (MMMN), which can run multiple services simultaneously during roaming in ultra-dense heterogeneous networks with different radio access technologies, is a major challenge in implementing 5G and beyond. Ultra-dense heterogeneous networks (HetNet), restrictions on multiservice multimode mobile terminals, and user mobility all lead to a number of serious problems, such as the ping-pong effect, unnecessary handovers, and decreasing cell edge spectral efficiency. The dense deployment of small base stations also causes a high probability of frequent handovers in HetNet, which has led to a significant escalation in energy consumption resulting from unnecessary signaling, increased network overhead, and higher energy usage by network equipment. This increased energy requirement may lead to potentially increase the emissions of greenhouse gases to meet the energy production needs. Several handover decision algorithms have been proposed, but most of them deal with only running a single service at a time on a mobile node. In this paper, authors propose a new network selection model for MMMN which may run multiple services simultaneously. This model uses context-aware information about the user’s preferences, characteristics, and needs of the services, limitations of mobile terminal, and network conditions of an ultra-dense HetNet to choose the best network. The fundamental principle of this model is based on the comprehensive network criteria weight computed by the integration of Enhanced CRiteria Importance Through Inter-criteria Correlation (CRITIC), fuzzy analytic hierarchy process (FAHP), and group decision-making (GDM) techniques and the service priority of running services. For computing the objective weight of criteria, a novel improved CRITIC method has been proposed. At the same time, sigmoid utility functions and linear utility functions have been used to figure out the value of network criteria for running multiple services on the mobile node. Finally, the best network from HetNet for MMMN is selected using the technique for order performance by similarity to ideal solution (TOPSIS) and threshold by computing a comprehensive utility value network criteria decision matrix and comprehensive network criteria weight. The simulation results show that the proposed model increases the selection probabilities of an appropriate network according to the service requirements and network conditions. It also reduces unnecessary handovers and improves QoS while satisfying users’ preferences, service requirements, and terminal constraints, in solving the MMMN network selection problem.

Selection of best industrial waste management technique under complex non-linear Diophantine fuzzy Dombi aggregation operators

The harmful waste products, beverages, chemical agents that are discharged or emitted by industrial processes are categorized as industrial waste (IW). IW is one of the most severe problems facing governments around the world due to its environmental effect and severity. So the aim of this paper is to develop a method for selecting industrial waste management technique (IWMT) for industry. Dombi Norms were commonly referred to as Dombi operations, but their applications might be better expressed if they are presented with a new level of flexibility within the general parameter. Dombi operations have not yet been applied in non-linear Diophantine fuzzy sets (N-LDFSs) in a suitable form. Therefore we apply the Dombi operators on complex N-LDFS (CN-LDFS) and develop a generalized area of research in decision making problems (DMPs). The majority of decision-making challenges in the actual world contain a wide range of elements that must be taken into account. Making decision under such circumstances can frequently be challenging. To deal with such complicated issues, we require multi-attribute group decision-making (MAGDM) techniques. The aims to discuss MAGDM issues, this paper will introduce a new COmbinative Distance-based ASsessment (CODAS) methodology. In this paper, we put forward three novel aggregation operators (AOs): complex non-linear Diophantine fuzzy Dombi weighted averaging (CN-LDFDWA), complex non-linear Diophantine fuzzy Dombi order weighted averaging (CN-LDFDOWA), and complex non-linear Diophantine fuzzy Dombi hybrid weighted averaging (CN-LDFDHWA). These operators are used in the operational parameter to help a successful solution of the problem. Due to the growing energy and material shortages, as well as to comply with environmental laws and regulations and to take advantage of disposal economics in the current context, industrial waste (IW) should be taken into consideration as one of the potential resources in the steel industry. Further, the proposed decision support model applied to solve a case study regarding to IWMT for Pakistan steel mill corporation (PSMC) under complex N-LDF Dombi operators. The proposed method is more effective and reliable for decision making (DM) model, as compared to the existing method.

Hamy mean operator under complex picture fuzzy environment and its application to disaster management program

Disaster Management Program plays a critical role in coordinating and implementing strategies to address emergencies and disasters, ranging from natural events like hurricanes, earthquakes, and wildfire to human-made incidents such as industrial accidents or terrorist attacks. Simultaneously, it is widely studied as a typical multi-attribute decision-making (MADM) problem. This paper investigates the concept of complex picture fuzzy sets (CPFS), an extension of picture fuzzy sets (PFS), achieved by the inclusion of a phase term. The existence of phase terms expand the scope of CPFS from real line to a complex plane of unit disc and highlight its originality by demonstrating its capacity to handle both vagueness and periodicity simultaneously. In this paper, the complex picture fuzzy Hamy mean operator (CPFHM) and complex picture fuzzy dual Hamy mean operator (CPFDHM) is studied. The reason of selecting complex picture fuzzy Hamy mean operator (CPFHMO) is that it can find interrelationship among multi-input variables. Then the various properties of CPFHM and CPFDHM operator are described in depth. A multi-attributes group decision-making (MAGDM) technique for solving group decision-making problems is proposed based on these operators. The validity of the present technique is demonstrated by analyzing a disaster management problem. Furthermore we check the sensitivity of parameter k and apply the validity test on our proposed technique. Finally, a comprehensive comparison is provided between the proposed model and specific existing approaches, illustrating that the suggested decision model is superior and more advantageous than the existing employed methodologies.

Toward Effective Uncertainty Management in Decision-Making Models Based on Type-2 Fuzzy TOPSIS

Over the past century, there has been a dramatic increasing interest in the multi-criteria group decision-making (MCGDM) technique, with a considerable amount of studies published regarding it. One of the well-known approaches in the MCGDM paradigm is Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The integration of the TOPSIS method with fuzzy set theory has proven to be successful in various applications. Recently, a wide array of publications has proposed implementing a type-2 fuzzy set with TOPSIS. However, the additional degree of uncertainty represented by type 2 has largely been ignored, especially in a few specific mathematical operations in the model. We propose constructing interval type-2 fuzzy membership functions (IT2 MFs) using interval-based data gathered from a survey, where this is used to generate a new scale to represent ratings for each alternative. This procedure utilized all information gathered from decision makers. In addition, we present a complete algorithm for TOPSIS based on IT2 fuzzy sets (IT2 FSs) which preserve the interval-based form output. The output in the form of intervals offers decision makers (DMs) with more detailed information, enabling them to make more nuanced decisions. This can include cautious decisions when intervals are wider and overlapping. Although understanding the exact meaning of these intervals and their widths in a decision-making context is challenging, this paper introduces a systematic method for connecting input uncertainty to output uncertainty in the TOPSIS technique. This approach establishes a solid foundation for future research. Thus far, no other researchers have suggested a data-driven method that combines TOPSIS with fuzzification and provides intervals as the final output.

A multi-objective sustainable financial portfolio selection approach under an intuitionistic fuzzy framework

In recent decades, sustainable investing has caught on with investors, and it has now become the norm. In the age of start-ups, with scant information on the sustainability aspects of an asset, it becomes harder to pursue sustainable investing. To this end, this paper proposes a sustainable financial portfolio selection approach in an intuitionistic fuzzy framework. We present a comprehensive three-stage methodology in which the assets under consideration are ethically screened in Stage-I. Stage-II is concerned with calculating the sustainability scores, based on various social, environmental, and economic (SEE) criteria and an evaluation of the return and risk of the ethical assets. Intuitionistic fuzzy set theory is used to gauge the linguistic assessment of the assets on several SEE criteria from multiple decision-makers. A novel intuitionistic fuzzy multi-criteria group decision-making technique is applied to calculate the sustainability score of each asset. Finally, in Stage-III, an intuitionistic fuzzy multi-objective financial portfolio selection model is developed with maximization of the satisfaction degrees of the sustainability score, return, and risk of the portfolio, subject to several constraints. The ϵ-constraint method is used to solve this model, which yields various efficient, sustainable financial portfolios. Subsequently, investors can choose the portfolio best suited to their preferences from this pool of efficient, sustainable financial portfolios. A detailed empirical illustration and a comparison with existing works are given to substantiate and validate the proposed approach.

Probabilistic dual hesitant fuzzy MAGDM method based on generalized extended power average operator and its application to online teaching platform supplier selection

Probabilistic dual hesitation fuzzy set (PDHFS) is a new tool used to enhance fuzzy and imprecise information in a changing and complex decision-making environment. Compared with the existing fuzzy sets, PDHFS is more suitable for identifying uncertain evaluation data in complex and realistic decision-making situations. The extended power average (EPA) and generalized power average (GPA) operators provide more information for the information aggregation process, reflecting the mutual support among all aggregation values. To solve the evaluation problems related to decision analysis involving extremely complex information, especially in the case of extreme data in the consideration of decision problems. In this paper, the generalized extended power average (GEPA) operator is proposed in the PDHF environment. This research has the following four important contributions. First, we propose a probability splitting algorithm of normalizing PDHFE, and give some new basic operations, score function, distance measures and aggregation operators. Second, to better integrate the advantages of EPA and GPA operators, we developed the GEPA operator, weighted form (WGEPA) and ordered form (GEPOWA). Meanwhile, by adjusting the parameter in three types of operators, we can find that many existing operators are special forms of the three types of operators. Third, extended the three types of operators to the PDHF environment, the PDHFGEPA, PDHFWGEPA, PDHFGEPOWA operators and some special aggregation operators are obtained, and studied their valuable properties in detail. Finally, taking the application of PDHFWGEPA operator as an example, a new multi-attribute group decision-making (MAGDM) technique is developed and used to solve the problem of online teaching platform supplier selection (OTPSS). Through parameter sensitivity analysis, the flexibility and stability of MAGDM technique are illustrated, and compared with some existing decision-making methods, the effectiveness of MAGDM method is proved. In addition, this technique also provides some theoretical references for dealing with other complex MAGDM problems, and provides a new operator for the extension of GEPA operator in other fuzzy environments. The biggest advantage of several operators is that they can pay full attention to the special role of extreme data in decision-making.

Several similarity measures of probabilistic dual hesitant fuzzy sets and their applications to new energy vehicle charging station location

Probabilistic dual hesitant fuzzy set (PDHFS) can better reflect the hesitant attitude and probability information of decision-makers than existing fuzzy sets. Therefore, it is a more powerful and important tool to express and deal with uncertain information. The score function of fuzzy number plays a very important role in comparing two fuzzy numbers. Entropy measure is a very effective tool for weighting decision attributes in multi-attribute group decision-making problems. Firstly, depending on the detailed analysis of the already existed score function of probabilistic dual hesitant fuzzy element (PDHFE), we built a novel score function for it, it is more effective and convenient when comparing two elements. Secondly, probabilistic dual hesitant fuzzy entropy was built to weight decision attributes. Thirdly, a combined weighting method was proposed which can reflect both subjective and objective information for decision attribute weighting based on the principle of minimum discriminant information. Fourthly, we extended the classical Dice and Jaccard similarity measures to the probabilistic dual hesitant fuzzy environment, and an all-new multi-attribute group decision-making technique to solve the problem that the decision attribute information is probabilistic dual hesitant fuzzy element. Finally, the applicability and effectiveness of this technique are proved by numerical examples and comparative analysis.

Algorithm for Energy Resource Selection Using Priority Degree-Based Aggregation Operators with Generalized Orthopair Fuzzy Information and Aczel–Alsina Aggregation Operators

Many aggregation operators are studied to deal with multi-criteria group decision-making problems. Whenever information has two aspects, intuitionistic fuzzy sets and Pythagorean fuzzy sets are employed to handle the information. However, q-rung orthopair fuzzy sets are more flexible and suitable because they cover information widely. The current paper primarily focuses on the multi-criteria group decision-making technique based on prioritization and two robust aggregation operators based on Aczel–Alsina t-norm and t-conorm. This paper suggests two new aggregation operators based on q-rung orthopair fuzzy information and Aczel–Alsina t-norm and t-conorm, respectively. Firstly, novel q-rung orthopair fuzzy prioritized Aczel–Alsina averaging and q-rung orthopair fuzzy prioritized Aczel–Alsina geometric operators are proposed, involving priority weights of the information. Several related results of the proposed aggregation operators are investigated to see their diversity. A multi-criteria group decision-making algorithm based on newly established aggregation operators is developed, and a comprehensive numerical example for the selection of the most suitable energy resource is carried out. The proposed aggregation operators are compared with other operators to see some advantages of the proposed work. The proposed aggregation operators have a wider range for handling information, with priority degrees, and are based on novel Aczel–Alsina t-norm and t-conorm.

TOPSIS Technique of MCDM under Cubic Intuitionistic Fuzzy Soft Set Environment

A powerful tool for dealing with ambiguity, attributive values, fuzziness, and inconsistency is the cubic intuitionistic fuzzy soft set. In this paper, multi-criteria group decision-making (MCGDM) technique called extended TOPSIS based on distance and similarity measures is presented. Furthermore, a real-world problem is resolved by applying CIFS-set to support the theoretical visualization. Arguably, the proposed technique works well and has practical uses.

An integrated group decision-making technique under interval-valued probabilistic linguistic T-spherical fuzzy information and its application to the selection of cloud storage provider

<abstract> <p>Cloud storage is crucial in today's digital era due to its accessibility, scalability, cost savings, collaboration and enhanced security features. The selection of a reliable cloud storage provider is a significant multi-attribute group decision-making (MAGDM) problem that involves intrinsic relationships among the various alternatives, attributes and decision DMs. Due to the uncertain and incomplete nature of the evaluation data for cloud storage providers, i.e., quality of service and user feedback, the identification of appropriate cloud storage providers with accurate service ranking remains an open research challenge. To address the above-mentioned challenge, this work proposes the concept of interval-valued probabilistic linguistic T-spherical fuzzy set (IVPLt-SFS). Then, some basic operations and a score function are defined to compare two or more IVPLt-SF numbers (IVPLt-SFNs). For information fusion, two aggregation operators for IVPLt-SFN are also developed. Next, an extended TOPSIS method-based group decision-making technique under interval-valued probabilistic linguistic T-spherical fuzzy information is established to solve the MAGDM problem. Finally, a numerical example is given to illustrate the practicability and usefulness of the designed approach and its suitability as a decision-making tool for selecting a cloud storage provider. Comparative and sensitivity analysis confirmed that this paper enriches the theory and methodology of the selection problem of cloud storage provider and MAGDM analysis.</p> </abstract>

Extended fuzzy $ N $-Soft PROMETHEE method and its application in robot butler selection

This paper extends the literature on fuzzy PROMETHEE, a well-known multi-criteria group decision-making technique. The PROMETHEE technique ranks alternatives by specifying an allowable preference function that measures their deviations from other alternatives in the presence of conflicting criteria. Its ambiguous variation helps to make an appropriate decision or choose the best option in the presence of some ambiguity. Here, we focus on the more general uncertainty in human decision-making, as we allow N-grading in fuzzy parametric descriptions. In this setting, we propose a suitable fuzzy N-soft PROMETHEE technique. We recommend using an Analytic Hierarchy Process to test the feasibility of standard weights before application. Then the fuzzy N-soft PROMETHEE method is explained. It ranks the alternatives after some steps summarized in a detailed flowchart. Furthermore, its practicality and feasibility are demonstrated through an application that selects the best robot housekeepers. The comparison between the fuzzy PROMETHEE method and the technique proposed in this work demonstrates the confidence and accuracy of the latter method.