Soft computing-based new interval-valued hesitant fuzzy multi-criteria group assessment method with last aggregation to industrial decision problems

In classical group decision-making (GDM) analysis, ratings of potential alternatives and weights of criteria or decision makers (DMs) are known precisely. However, DMs for dealing with uncertain situations can define their opinions in linguistic variables based on fuzzy sets in industrial selection problems. In this respect, an interval-valued hesitant fuzzy set (IVHFS) is the suitable and capable theory that could help the DMs by assigning some interval-valued membership degrees for a candidate or option under a set. This paper introduces a novel interval-valued hesitant fuzzy distance-based group decision (IVHF-DBGD) model by a group of DMs, in which the best potential alternative can be appraised and selected among the conflicting criteria. In the proposed IVHF-DBGD model, the weight of each criterion is determined by extended IVHF-entropy method along with the DMs’ opinions about the criteria’ weights. Also, the weight of each DM is computed by a new IVHF-order preference method with the relative closeness. Moreover, this paper introduces a new IVHF-collective index to discriminate among potential alternatives in the selection process. Finally, the computational results with a robot selection from the literature indicate that the proposed IVHF-DBGD model is the suitable group decision-making tool for industrial selection problems.

Uncertain data is a challenge to decision-making (DM) problems. Multi-criteria group decision-making (MCGDM) problems are among these problems that have received much attention. MCGDM is difficult because the existing alternatives frequently conflict with each other. In this article, we suggest a novel hybrid model for an MCGDM approach based on modified rough bipolar soft sets (MRBSs) using a well-known method of technique for order of preference by similarity to ideal solution (TOPSIS), which combines MRBSs theory and TOPSIS for the prioritization of alternatives in an uncertain environment. In this technique, we first introduce an aggregated parameter matrix with the help of modified bipolar soft lower and upper matrices to identify the positive and negative ideal solutions. After that, we define the separation measurements of these two solutions and compute relative closeness to choose the best alternative. Next, an application of the proposed technique in the MCGDM problem is introduced. Afterward, an algorithm for this application is developed, which is illustrated by a case study. The application demonstrates the usefulness and efficiency of the proposal. Compared to some existing studies, we additionally present several merits of our proposed technique. Eventually, the paper handles whether additional studies on these topics are needed.

Interval-valued intuitionistic fuzzy mathematical programming method for hybrid multi-criteria group decision making with interval-valued intuitionistic fuzzy truth degrees

<abstract><p>Entropy is an important tool of information measurement in the fuzzy set and its inference. The research on information measurement based on interval-valued Pythagorean fuzzy sets mostly involves the distance formula for interval-valued Pythagorean fuzzy numbers, but seldom involves the measurement of fuzziness. In view of this situation, we have aimed to propose new interval-valued Pythagorean fuzzy entropy and weighted exponential entropy schemes. Based on the interval-valued Pythagorean fuzzy weighted averaging operator, a strategy based on weighted exponential entropy is proposed to solve the multi-criteria group decision-making (MCGDM) problem in the interval-valued Pythagorean environment. Two examples illustrate that this paper provides a feasible new method to solve the MCGDM problem in an interval-valued Pythagorean fuzzy (IVPF) environment. Finally, by comparing with the existing methods, it is concluded that the entropy measure of IVPF schemes and the corresponding MCGDM method can select the optimal solution of the practical problem more precisely and accurately. Therefore, the comparative analysis shows that the proposed measurement method has the characteristics of flexibility and universality.</p></abstract>

The consistency of interval-valued fuzzy preference relations (IFPRs) is a prerequisite for the application of IFPRs in real problems. To support the application of IFPRs, various types of consistency of IFPRs have been developed. They all satisfy some fixed mathematical conditions under the assumption that decision makers are perfectly rational. In practice, this assumption is usually violated because decision makers generally have bounded rationality. Considering the bounded rationality of decision makers, this article develops a new type of consistency of IFPRs called triangular bounded consistency, which is based on the historical preferences of decision makers. A triangular framework is designed to describe the three IFPRs of any three alternatives. The strict transitivity of IFPRs is defined as the restricted max-max transitivity of IFPRs, which is reconstructed in the triangular framework. In this situation, under the assumption that the preferences of a decision maker are consistent in similar circumstances, the triangular bounded consistency of IFPRs is defined by use of the historical IFPRs of decision makers. Based on the developed consistency, the process of determining the optimal estimations of missing IFPRs in an incomplete IFPR matrix is developed. A problem of selecting suppliers of simulation systems is analyzed using the multicriteria group decision-making (MCGDM) process with the triangular bounded consistency of IFPRs to demonstrate the application of the developed consistency in MCGDM.

In recent years, hesitant fuzzy sets (HFSs) and neutrosophic sets (NSs) have become a subject of great interest for researchers and have been widely applied to multi-criteria group decision-making (MCGDM) problems. In this paper, multi-valued neutrosophic sets (MVNSs) are introduced, which allow the truth-membership, indeterminacy membership and falsity-membership degree have a set of crisp values between zero and one, respectively. Then the operations of multi-valued neutrosophic numbers (MVNNs) based on Einstein operations are defined, and a comparison method for MVNNs is developed depending on the related research of HFSs and Atanassov’s intuitionistic fuzzy sets (IFSs). Furthermore, the multi-valued neutrosophic power weighted average (MVNPWA) operator and the multi-valued neutrosophic power weighted geometric (MVNPWG) operator are proposed and the desirable properties of two operators are also discussed. Finally, an approach for solving MCGDM problems is explored by applying the power aggregation operators, and an example is provided to illustrate the application of the proposed method, together with a comparison analysis.

Multi-criteria group decision making (MCGDM) is the important part in decision-making process, which has been used in many industries. Coordinating differing opinions and ultimately reaching group consensus in a group decision-making process has become an important area of research. This paper uses probabilistic uncertain linguistic term sets (PULTSs) to express the uncertainty of evaluation information, and proposes a group consensus reaching method based on the opinion dynamics model which exhaustively considers how decision-makers’ (DMs) viewpoints can influence each other and evolve over time in MCGDM environments. First, we gathers the group’s preference information regarding the alternatives and their stubbornness to peer influence. Next, an influence matrix is determined based on the authority index of the DMs, and a probabilistic uncertain linguistic Friedkin–Johnsen model (PUL-FJ) is constructed. Then, a group consensus reaching method based on the PUL-Friedkin-Johnsen model is proposed to address the feedback mechanism in the consensus-reaching process (CRP). Finally, we proposes a novel approach for ranking. To better achieve group decision-making, we constructs an improved PUL similarity measure that based on the Wasserstein distance. Additionally, this paper proposes a new approach for expert weight, resulting in a comprehensive expert weight that balances individual expertise of the different criteria and group consensus. In the end, an example is provided, and the method’s feasibility is validated through sensitivity analysis and comparative analysis.

A generalized TODIM evaluation approach based on the novel score function and trust network under interval-valued hesitant fuzzy environment

Purpose – In today's competitive global marketplace, performance management has been identified as a key strategic consideration towards achieving an efficient supply chain management. The task of estimating supply chain performance extent is seemed a complex problem entitled with multiple subjective performance measures and metrics; subjected to decision-making environment which involves an inherent vagueness, inconsistency and incompleteness associated with decision-makers (DMs) (expert panel) commitment towards assessment of various subjective (quantitative) evaluation indices. Consequently, it becomes difficult towards making a comparative study on performances of alternative supply chains. It is, therefore, indeed essential to conceptualize and develop an efficient appraisement platform helpful for benchmarking of alternative supply chains based on their performance extent. The paper aims to discuss these issues. Design/methodology/approach – The work explores the concept of grey numbers combined with multi-objective optimization by ratio analysis (MOORA) in perceptive to evaluate best alternative from among available alternative supply chains. Findings – The method has been found fruitful to facilitate such a multi-criteria group decision-making (MCGDM) problem under uncertain environment and provides an appropriate compromise ranking order with respect to available possible alternatives. Originality/value – Supply chain performance appraisement provides necessary means by which an organization can assess whether its supply chain is performing well, whether it has been improved or degraded as compared to the past record. The purpose of this research is to develop and to empirically test a multiple-indices hierarchical appraisement model for benchmarking of supply chain performance and its impact on competitiveness of manufacturing industries.

Selecting suitable suppliers is a critical decision for organizations, and multi-criteria group decision-making (MCGDM) has been proven as an effective technique in selecting the most appropriate supplier. However, most existing supplier selection methods do not fully explore the trust relationships among decision-makers (DMs), fail to effectively tackle complex and uncertain information, and neglect the interdependencies among criteria. To overcome these obstacles, we develop a social trust network (STN) based hybrid supplier selection approach that adopts the best-worst method (BWM) and the preference ranking organization method for enrichment evaluation (PROMETHEE) under interval-valued Pythagorean fuzzy (IVPF) environment. The main contributions are as follows: Firstly, a novel STN is constructed and extended to the IVPF setting to fully capture both direct and indirect trust relationships among DMs. Secondly, a new trust propagation operator is designed to determine DMs’ weights more objectively. Thirdly, the BWM method, known for its high consistency and efficiency, is adapted to the IVPF environment to derive optimal criterion weights and the preference relation-based PROMETHEE method is also integrated to rank the candidate suppliers. Fourthly, the IVPF weighted Hamy mean (IVPFWHM) operator is employed to aggregate DMs’ preferences and evaluations while explicitly capturing the interrelationships among criteria. The approach is practically applied to a real-world decarbonized and sustainable supplier selection case to validate the efficacy in complex and uncertain group decision-making scenarios while incorporating social trust dynamics. The sensitivity and comparison analyses are also conducted to validate the robustness and benefits of the method.

The outbreak of the novel coronavirus pneumonia and the turbulent international situation in recent years have seriously disrupted the normal operation of the entire supply chain (SC). As an emerging technology, blockchain is characterized by decentralization, reliability, transparency and traceability, which can be effectively applied to solve social, environmental and economic concerns and achieve sustainability of supply chain. However, whether blockchain is suitable for every function of a sustainable supply chain (SSC), or what function is best suited for the application of a set of blockchain criteria, can be viewed as a multi-criteria group decision-making (MCGDM) problem. This paper presents a combined MCGDM technique utilizing the social network analysis (SNA) and Multi-Attributive Border Approximation Area Comparison (MABAC), for selecting an appropriate function of SSCs to implement blockchain technology with Neutrosophic information. The framework gives quantitative consideration to the weight of relevant blockchain criteria and decision makers under high uncertainty. This study can also facilitate the effective allocation of resources and enhance the competitiveness of SSCs in the coordinated planning of various blockchain deployments.

Information system (IS) project selection is of critical importance to every organization in dynamic competing environment. The aim of this paper is to develop a hybrid multicriteria group decision making approach based on intuitionistic fuzzy theory for IS project selection. The decision makers’ assessment information can be expressed in the form of real numbers, interval-valued numbers, linguistic variables, and intuitionistic fuzzy numbers (IFNs). All these evaluation pieces of information can be transformed to the form of IFNs. Intuitionistic fuzzy weighted averaging (IFWA) operator is utilized to aggregate individual opinions of decision makers into a group opinion. Intuitionistic fuzzy entropy is used to obtain the entropy weights of the criteria. TOPSIS method combined with intuitionistic fuzzy set is proposed to select appropriate IS project in group decision making environment. Finally, a numerical example for information system projects selection is given to illustrate application of hybrid multi-criteria group decision making (MCGDM) method based on intuitionistic fuzzy theory and TOPSIS method.

This paper aims to develop and introduce a fuzzy Interval based Multi-criteria homogeneous Group Decision making technique (IMGD) to make appropriate decision under fuzzy environment. In fuzzy multi-criteria group decision making process, a group of decision makers often considers several subjective criteria for ranking a set of alternatives. Due to vague and imprecise information, decision makers generally utilize linguistic variables which are mandatorily converted into triangular or trapezoidal fuzzy numbers. The total process then becomes very complex and time consuming. The current investigation advocates fuzzy intervals instead of triangular or trapezoidal fuzzy numbers for simplification of the complex situation and ease of calculation. In this method, fuzzy intervals of performance ratings and weights assessed by homogeneous group decision makers under subjective criteria are converted into first mean fuzzy intervals then into normalized crisp numbers. The normalized crisp performance ratings and normalized crisp weights are combined together to determine initially individual contribution and then into aggregate contribution to each alternative for final ranking and selection of the alternative. The new model is demonstrated with an application to airports ranking and selection problem for better clarification and verification. The outcome of the proposed is validated with the results obtained by well-known existing MCDM techniques. The analysis shows that the proposed method is applicable, useful and effective for appropriate decision making under fuzzy MCDM environment.

Software requirements (SRs) selection is a multicriteria group decision making (MCGDM) problem whose objective is to select the SRs from the pool of the requirements on the basis of different criteria. In MCGDM, different decision makers have different opinions of the same requirement so it is difficult to decide which set of SRs to implement during the different releases of the software. During the MCGDM process, decision makers may use linguistic variables to specify preferences of requirements over other requirements. In real life applications, it has been observed that sometimes decision makers cannot evaluate the SRs due to their lack of knowledge and limited expertise related to the problem domain. In this situation, incomplete linguistic preference relations (LPRs) are constructed. In literature, SRs selection with incomplete LPRs is still an unresearched problem. Therefore, to address this issue, a method is presented for the selection of SRs with incomplete LPRs. Finally, the applicability of the proposed method is explained with the help of an example.

As a critical component in green supply chain activities, green supplier selection modelled as the typical multicriteria group decision-making (MCGDM) problem, has received tremendous attention both from scholars and practitioners. This article aims to advance an integrated approach for precisely addressing this urgent problem under Pythagorean fuzzy (PF) scenario. Firstly, analytic hierarchy process (AHP) is utilized for computing criterion weights that comprehensively reflect the intention of decision makers (DMs). Secondly, extend the Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) to PF scenario, and apply the newest magnitude comparison method and distance measure of PF sets in the $L_{p} -$ metric equation to accurately acquire the ranking indices, then obtain the different ranks of candidates along with the adjustment of DM's preference value to balance the group utility and individual regret. Thirdly, median ranking method (MRM) is employed to reach a consensus opinion for the differentiated ranks of candidates via the attribute-wise rank and linear programming model. Subsequently, a numerical example for an agri-food company in China is introduced to illustrate the feasibility of our AHP-VIKOR-MRM method. Finally, conduct the comparison with some available MCGDM methods with PF information to interpret the stability and practicability of our presented method.