Evaluation Based on Distance from Average Solution Method for Multiple Criteria Group Decision Making under Picture 2-Tuple Linguistic Environment

A novel integrated MADM method for design concept evaluation

Design concept evaluation plays a significant role in new product development. Rough set based methods are regarded as effective evaluation techniques when facing a vague and uncertain environment and are widely used in product research and development. This paper proposed an improved rough-TOPSIS method, which aims to reduce the imprecision of design concept evaluation in two ways. First, the expert group for design concept evaluation is classified into three clusters: designers, manufacturers, and customers. The cluster weight is determined by roles in the assessment using a Multiplicative Analytic Hierarchy Process method. Second, the raw information collection method is improved with a 3-step process, and both design values and expert linguistic preferences are integrated into the rough decision matrix. The alternatives are then ranked with a rough-TOPSIS method with entropy criteria weight. A practical example is shown to demonstrate the method's viability. The findings suggest that the proposed decision-making process is effective in product concept design evaluation.

Cloud vendor selection for the healthcare industry using a big data-driven decision model with probabilistic linguistic information

The role of cloud services in the data-intensive industry is indispensable. Cision recently reported that the cloud market would grow to 55 billion USD, with an active contribution of the cloud to healthcare around 2025. Inspired by the report, cloud vendors expand their market and the quality of services to seek growth globally. The rapid growth of the cloud sector in the healthcare industry imposes a challenge: making a rational choice of a cloud vendor (CV) out of a diverse set of vendors. Typically, the healthcare industry 4.0 sees the issue as a large-scale group decision-making problem. Previous studies on a CV selection face certain challenges, such as (i) a lack of the ability to handle multiple users' views, as well as experts'/users' complex linguistic views; (ii) the confidence level associated with a view is not considered; (iii) the transformation of multiple users' views into holistic data is lacking; and (iv) the systematic prioritization of CVs with minimum human intervention is a crucial task. Motivated by these challenges and circumventing them, a new big data-driven decision model is put forward in this paper. Initially, the data in the form of complex expressions are collected from multiple cloud users and are further transformed into a holistic decision matrix by adopting probabilistic linguistic information (PLI). PLI represents complex linguistic expressions along with the associated confidence levels. Later, a holistic decision matrix is formed with the missing values imputed by proposing an imputation algorithm. Furthermore, the criteria weights are determined by using a newly proposed mathematical model and partial information. Finally, the evaluation based on the distance from average solution (EDAS) approach is extended to PLI for the rational ranking of CVs. A real-time example of a CV selection for a healthcare center in India is exemplified so as to demonstrate the usefulness of the model, and the comparison reveals the merits and limitations of the model.

Fermatean fuzzy CRITIC-EDAS approach for the selection of sustainable third-party reverse logistics providers using improved generalized score function

In today's world, the demand for sustainable third-party reverse logistics providers (S3PRLPs) becomes an increasingly considerable issue for industries seeking improved customer service, cost reduction and sustainability perspectives. However, the assessment and selection of right S3PRLP is a complex uncertain decision-making problem due to involvement of numerous conflicting attributes, imprecise human mind and lack of information. Recently, Fermatean fuzzy set (FFS) has been recognized as one of the suitable tools to tackle the uncertain and inaccurate information. In this paper, we introduce a hybrid methodology based on CRITIC and EDAS methods with Fermatean fuzzy sets (FFSs) to solve the S3PRLP selection problem in which the attributes and decision makers' weights are completely unknown. In this framework, CRITIC approach is applied to calculate the attribute weight and EDAS method is used to evaluate the priority order of S3PRLP options. To do this, a new improved generalized score function (IGSF) is developed with its elegant properties. Also, a formula is discussed to calculate the decision makers' weights based on the developed IGSF. Next, developed framework is applied to assess a case study of S3PRLP selection problem with Fermatean fuzzy information, which elucidates the usefulness and practicality of the proposed method. Finally, comparative study is implemented to show the strength of introduced framework with extant approaches. The outcomes of the work confirm that the introduced approach is more feasible and well-consistent with the other extant approaches.

A new spherical aggregation function with the concept of spherical fuzzy difference for spherical fuzzy EDAS and its application to industrial robot selection

In this article, a new fully fuzzy approach is developed for the evaluation based on distance from average solution (EDAS) for multi-criteria decision-making (MCDM) using spherical fuzzy sets (SFSs). The proposed approach avoids the current limitations and drawbacks of distance-based methods in general and the EDAS method in particular using spherical fuzzy information namely, early defuzzification, the flaws of distance measures, and the undefined spherical fuzzy subtraction and division operations. First, the approach employs the score function only in the final step for ranking. Second, the concept of the spherical fuzzy difference is introduced to make up for the subtraction operation which is the backbone of EDAS and as a substitute for distance measures. The spherical fuzzy difference is utilized to indicate any increase or decrease in the membership degree, the non-membership degree, and the hesitancy degree in the performance of an alternative for a criterion than that of its peer in the average solution. Then, the weighted spherical differences are calculated. The total weighted spherical differences from the average solution of each alternative for the assessment criteria are aggregated in the appraisal score. Due to a flaw in the extant aggregation operators, their results might be misleading. Therefore, an aggregation function is introduced that guarantees a balanced and fair aggregation. The appraisal scores are defuzzified, and the alternative with the highest appraisal score is the best. Two practical examples in MCDM are solved and a comparative study is presented to demonstrate and validate the algorithm.

Models for MADM with hesitant interval-valued fuzzy information under uncertain environment

This paper is a research of interval-valued fuzzy and Muirhead Mean algorithms. We deduced new algorithms named as hesitant interval-valued fuzzy Muirhead Mean (HIVFMM) and hesitant interval-valued fuzzy Muirhead Mean (HIVFWMM) with Muirhead Mean algorithms based on Hesitant interval-valued fuzzy set (HIVFS). Firstly, we introduced some concepts and operation laws of HIVFS and the formula form of MM, then we combined them both and gave the proof process of properties and theorems, a mathematic model applying to MADM and a numerical example was given to illustrate the effectively and practically.

Interval-valued intuitionistic fuzzy multiple attribute decision making and their applications

In this paper, we expand the Muirhead mean (MM) operator and dual Muirhead mean (DMM) operator with interval-valued intuitionistic fuzzy numbers (IVIFNs) to propose the interval -valued intuitionistic fuzzy Muirhead mean (IVIFMM) operator, interval-valued intuitionistic fuzzy weighted Muirhead mean (IVIFWMM) operator, interval-valued intuitionistic fuzzy dual Muirhead mean (IVIFDMM) operator and interval-valued intuitionistic fuzzy weighted dual Muirhead mean (IVIFWDMM) operator. Then the MADM methods are proposed with these operators. In the end, we utilize an applicable example for green supplier selection in green supply chain management to prove the proposed methods.

An intelligent framework for analyzing supply chain resilience of firms in China: a hybrid multicriteria approach

Purpose

The paper aims to identify the most supply chain resilient company suitable for the customized preferences of partner firms in the context of the Chinese supply chain framework during the COVID-19 pandemic.

Design/methodology/approach

A hybrid multicriteria model, i.e. Fuzzy Analytical Hierarchy Process (AHP), was used to assign weights to each criterion, which was subsequently analyzed by three approaches, namely Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory), and Evaluation Based on Distance from Average Solution (EDA), to rank the top ten companies in descending order of supply chain resilience. Further, sensitivity analysis is performed to identify the consistency in ranking with variation in weights. The rankings are validated by a novel Ensemble Ranking algorithm and by supply chain domain experts.

Findings

The rankings suggest the company “China Energy Construction Group Tianjin Electric Power Construction Co., Ltd” is the most feasible and resilient company, presenting interesting findings to partner firms, and Bosch is the least reliable supply chain company of the ten firms considered, thus presenting interesting findings to partner companies.

Practical implications

“Crisis Management Beforehand” is most critical in the current pandemic scenario. This implies that companies need to first prioritize taking proactive steps in crisis management followed by the need to minimize the “Expected impact of pandemic.” Performance factors also need to be regulated (sales, supply chain rank and financial performance) to maintain the company's overall reputation. Considering the consistent performance of the China Energy Construction Group Tianjin Electric Power Construction Co., Ltd., it is recommended as the most reliable supply chain firm to forge strategic partnerships with other supply chain stakeholders like suppliers and customers. On the other hand, Bosch is not recommended as a supply chain reliable company and needs to improve its crisis management capabilities to minimize the pandemic impact.

Originality/value

The paper aims to identify the most supply chain resilient company suitable for the customized preferences of partner firms in the context of the Chinese supply chain framework during the COVID-19 pandemic. The rankings suggest the company “China Energy Construction Group Tianjin Electric Power Construction Co., Ltd” is the most feasible and resilient company, presenting interesting findings to partner firms, and Bosch is the least reliable supply chain company of the ten firms considered, thus presenting interesting findings to partner companies.

Geometric picture fuzzy numbers and three-dimensional copulas with the non-linear programming approach

This study presents a possible relationship between two main objects, which are three-dimensional copulas (3D-Cs) and geometric picture fuzzy numbers (GPFNs). This opens up a potential field for future studies for these two objects that three-dimensional copulas can become useful tools for handling uncertainty information in the form of a picture fuzzy set (PFS). Specifically, we define a GPFN as a base element of the PFS and a defined domain of three-dimensional copulas that contains a set of GPFNs, then we show some examples of three-dimensional copulas identified on this domain. In this framework, we present the theorems related to these two objects. At the same time, we provide some examples for three-dimensional semi-copulas, three-dimensional quasi-copulas, and three-dimensional empirical copulas defined on D, which is a defined domain of a three-dimensional copula and contains a set of GPFNs D g * . In addition, we also introduce a new approach to non-linear programming problems.

Assisting Users in Decisions Using Fuzzy Ontologies: Application in the Wine Market

Nowadays, wine has become a very popular item to purchase. There are a lot of brands and a lot of different types of wines that have different prices and characteristics. Since there is a lot of options, it is easy for buyers to feel lost among the high number of possibilities. Therefore, there is a need for computational tools that help buyers to decide which is the wine that better fits their necessities. In this article, a decision support system built over a fuzzy ontology has been designed for helping people to select a wine. Two different possible architecture implementation designs are presented. Furthermore, imprecise information is used to design a comfortable way of providing information to the system. Users can use this comfortable communication system to express their preferences and provide their opinion about the selected products. Moreover, mechanisms to carry out a constant update of the fuzzy ontology are exposed.

Green supplier selection with an uncertain probabilistic linguistic MABAC method

In recent years, with the increased voice for protecting the environment by the people all over the world, the governments also have actively adopted more and more measures to further promote environmental conservation and sustainable development. Traditional procurement approaches have not well updated to the current needs of the society, especially for the retail industry which is in relation to the national economy due to numerous products and different suppliers being involved. Therefore, the need for green procurement is more important. The qualified green supplier selection is the core of green procurement, which is the utmost importance in the business competition throughout the supply chain in today’s strong business competition. Thus, in order to obtain the optimal green supplier, integration of Entropy weights and multi-attributive border approximation area comparison (MABAC) under uncertain probabilistic linguistic sets (UPLTSs) has offered a novel integrated model, in which information Entropy is utilized for calculating objective weights with UPLTSs to acquire the final ranking result of green supplier. Besides, so as to indicate the applicability of devised method, it is confirmed by a numerical case for green supplier selection. Some comparative studies are made with some existing methods. The proposed method can also serve for selecting suitable alternative successfully in other selection problems.

An EDAS Method for Multiple Attribute Group Decision-Making under Intuitionistic Fuzzy Environment and Its Application for Evaluating Green Building Energy-Saving Design Projects

Multiple attribute group decision-making (MAGDM) methods have a significant influence on decision-making in a variety of strategic fields, including science, business and real-life studies. The problem of evaluation in green building energy-saving design projects could be regarded as a type of MAGDM problem. The evaluation based on distance from average solution (EDAS) method is one of the MAGDM methods, which simplifies the traditional decision-making process. Symmetry among some attributes that are known and unknown as well as between pure attribute sets and fuzzy attribute membership sets can be an effective way to solve MAGDM problems. In this paper, the classical EDAS method is extended to intuitionistic fuzzy environments to solve some MAGDM issues. First, some concepts of intuitionistic fuzzy sets (IFSs) are briefly reviewed. Then, by integrating the EDAS method with IFSs, we establish an IF-EDAS method to solve the MAGDM issues and present all calculating procedures in detail. Finally, we provide an empirical application for evaluating green building energy-saving design projects to demonstrate this novel method. Some comparative analyses are also made to show the merits of the method.

An Emergency Decision-Making Method for Probabilistic Linguistic Term Sets Extended by D Number Theory

Emergency decision-making has become as one of the hot issues in recent years. The aim of emergency decision-making is to reduce the casualties and property losses. All the processes of emergency decision-making are full of incompleteness and hesitation. The problem of emergency decision-making can be regarded as one of the multi-attribute decision-making (MADM) problems. In this manuscript, a new method to solve the problem of emergency decision-making named D-PLTS is proposed, based on D number theory and the probability linguistic term set (PLTS). The evaluation information given by experts is tidied to be the form of PLTS, which can be directly transferred to the form of the D number, no matter whether the information is complete or not. Furthermore, the integration property of D number theory is carried out to fuse the information. Besides, two examples are given to demonstrate the effectiveness of the proposed method. Compared with some existing methods, the D-PLTS is more straightforward and has less computational complexity. Allocation weights that are more reasonable is the future work for the D-PLTS method.

Supplier Selection of Medical Consumption Products with a Probabilistic Linguistic MABAC Method

In order to obtain an optimal medical consumption product supplier, the integration of combined weights and multi-attributive border approximation area comparison (MABAC) under probabilistic linguistic sets (PLTSs) has offered a novel integrated model in which the CRiteria Importance Through Intercriteria Correlation (CRITIC) method is employed for calculating the objective weights of various attributes and the MABAC method with PLTSs is used to acquire the final ranking result of a medical consumption product supplier. Additionally, so as to indicate the applicability of the devised method, this model is confirmed by a numerical case for the supplier selection of medical consumption products. Some comparative studies are made with some existing methods. The proposed method can also successfully select suitable alternatives in other selection problems.

Dual Hesitant q-Rung Orthopair Fuzzy Hamacher Aggregation Operators and their Applications in Scheme Selection of Construction Project

The q-rung orthopair fuzzy set (q-ROFS), which is the extension of intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS), satisfies the sum of q-th power of membership degree and nonmembership degree is limited 1. Evidently, the q-ROFS can depict more fuzzy assessment information and consider decision-maker’s (DM’s) hesitance. Thus, the concept of a dual hesitant q-rung orthopair fuzzy set (DHq-ROFS) is developed in this paper. Then, based on Hamacher operation laws, weighting average (WA) operator and weighting geometric (WG) operator, some dual hesitant q-rung orthopair fuzzy Hamacher aggregation operators are developed, such as the dual hesitant q-rung orthopair fuzzy Hamacher weighting average (DHq-ROFHWA) operator, the dual hesitant q-rung orthopair fuzzy Hamacher weighting geometric (DHq-ROFHWG) operator, the dual hesitant q-rung orthopair fuzzy Hamacher ordered weighted average (DHq-ROFHOWA) operator, the dual hesitant q-rung orthopair fuzzy Hamacher ordered weighting geometric (DHq-ROFHOWG) operator, the dual hesitant q-rung orthopair fuzzy Hamacher hybrid average (DHq-ROFHHA) operator, and the dual hesitant q-rung orthopair fuzzy Hamacher hybrid geometric (DHq-ROFHHG) operator. The precious merits and some particular cases of above mentioned aggregation operators are briefly introduced. In the end, an actual application for scheme selection of construction project is provided to testify the proposed operators and deliver a comparative analysis.

Dual Hesitant Pythagorean Fuzzy Heronian Mean Operators in Multiple Attribute Decision Making

On account of the indeterminacy and subjectivity of decision makers (DMs) in complexity decision-making environments, the evaluation information over alternatives presented by DMs is usually fuzzy and ambiguous. As the generalization of intuitionistic fuzzy sets (IFSs), the Pythagorean fuzzy set (PFS) is more useful in expressing fuzzy and ambiguous information. Meanwhile, in order to consider human hesitance, dual hesitant Pythagorean fuzzy sets (DHPFSs) are presented, which can be more valid for handling real multiple attribute decision-making (MADM) problems. To fuse the information in DHPFSs more effectively, in this article, some dual hesitant Pythagorean fuzzy Heronian mean operators, which can consider the relationships between arguments being fused, are defined and studied. Evidently, the new proposed operators can obtain more exact results than other existing methods. In addition, some important properties of these Heronian mean (HM) operators are discussed. Subsequently, the defined aggregation operators are used in MADM with dual hesitant Pythagorean fuzzy numbers (DHPFNs), and the MADM model is developed. In accordance with the defined operators and the built model, the dual hesitant Pythagorean fuzzy generalized weighted Heronian mean (DHPFGWHM) operator and dual hesitant Pythagorean fuzzy generalized geometric weighted Heronian mean (DHPFGGWHM) operator are applied to deal with the green supplier selection in supply chain management, and the availability and superiority of the proposed operators are analyzed by comparing them with some existing approaches. The method presented in this paper can effectively solve the MADM problems in which the decision-making information is expressed by DHPFNs and the attributes are interactive.