Multi-criteria Group Decision-making Approach Research Articles

Group heterogeneity in multi member decision making model with an application to warehouse location selection in a supply chain

Group decision making (GDM) is more effective in extracting the real case scenarios of the decision problems to add competitive advantages in a supply chain. Group members from wider spectrum of the environment naturally command variation in knowledge level to their respective domain. The degree of heterogeneity of the decision makers in a group plays a crucial role in realistic assessment of both alternatives and selection criteria. This paper proposes a new Multi criteria GDM approach in adroit exploitation of the group heterogeneity during evaluation process and restrict the biasness of information while decision making. The importance of the heterogeneous degree of expertise is established through pair wise preference comparison matrix. To overcome the biasness, the consistency check mechanism of analytical hierarchy process (AHP) is employed. A real case example on warehouse location selection in a supply chain is illustrated to demonstrate the validity and effectiveness of the proposed approach. In order to ensure the applicability, compatibility and validity of the proposed approach, comparative study is carried out with the proven and established MCDM methodologies SAW, MOORA, TOPSIS, VIKOR, ELECTRE II, COPRAS and PROMETHEE. ANOVA, Sensitivity analysis (SA) and other investigations find the proposed approach as a rational, robust, effective and precise decision making aid to the supply chain managers.

An Adjustable Approach to Multi-Criteria Group Decision-Making Based on a Preference Relationship Under Fuzzy Soft Information

Group decision-making is a collaborative process to find optimal alternative based on an aggregation judgment. Various techniques have been suggested to solve decision-making problems; however, the rapid growth of uncertainties in industry and organizations highlights the application of fuzzy set theory and soft set theory in this area. In this regard, fuzzy soft model can be considered as an efficient tool in decision-making. To date, different algorithms have been proposed for solving collective decision-making problems based on fuzzy soft set theory. In order to reach the process of consensus, the existing methods have mostly used the t-norms, such as “AND” operator which can be successfully applied to individual decision-making problems including multi-source datasets. However, such approaches fail to consider multi-observer problems in group decision-making processes. Additionally, in the selection step, the existing methods lack a comprehensive priority approach; they focus on a hierarchical preference which ignores incomparable alternatives. To overcome these issues, this paper proposes an adjustable multi-criteria group decision-making approach based on a preference relationship of fuzzy soft sets. First, we construct two topological spaces over the set of objects, and then, develop a preference relationship of objects by using open sets of these two topologies. A multi-phase method is then designed to rank objects in multi-criteria group decision-making problems based on such preference relationship. We also extend the proposed algorithm to weighted case in order to have a higher level of adaptability with real-world problems. Dataset from “ www.booking.com ” Web site is applied to show the capability of this new method in comparison with results from the well-known literature approaches.

Probability multi-valued neutrosophic sets and its application in multi-criteria group decision-making problems

This paper introduces probability multi-valued neutrosophic sets (PMVNSs) based on multi-valued neutrosophic sets and probability distribution. PMVNS can serve as a reliable tool to depict uncertain, incomplete, inconsistent and hesitant decision-making information and reflect the distribution characteristics of all provided evaluation values. This paper focuses on developing an innovative method to address multi-criteria group decision-making (MCGDM) problems in which the weight information is completely unknown and the evaluation values taking the form of probability multi-valued neutrosophic numbers (PMVNNs). First, the definition of PMVNSs is described. Second, an extended convex combination operation of PMVNNs is defined, and the probability multi-valued neutrosophic number weighted average operator is proposed. Moreover, two cross-entropy measures for PMVNNs are presented, and a novel qualitative flexible multiple criteria method (QUALIFLEX) is developed. Subsequently, an innovative MCGDM approach is established by incorporating the proposed aggregation operator and the developed QUALIFLEX method. Finally, an illustrative example concerning logistics outsourcing is provided to demonstrate the proposed method, and its feasibility and validity are further verified by comparison with other existing methods.

Hesitant Uncertain Linguistic Z-Numbers and Their Application in Multi-criteria Group Decision-Making Problems

This paper introduces hesitant uncertain linguistic Z-numbers (HULZNs) based on Z-numbers and linguistic models. HULZNs can serve as a reliable tool to depict complex and uncertain decision-making information and reflect the hesitancy of DMs. This paper focuses on the development of an innovative method to address multi-criteria group decision-making (MCGDM) problems in which the weight information is incompletely known. Handling qualitative information requires the effective support of quantitative tools, after which the linguistic scale function is employed to deal with linguistic information. First, the operations and distance of HULZNs are defined. Then, two power aggregation operators for HULZNs are proposed. Subsequently, a new MCGDM approach is developed by incorporating the power aggregation operators and the VIKOR model. Finally, an illustrative example of ERP system selection is provided for demonstration, and the feasibility and validity of the proposed method are further verified by sensitivity analysis and comparison with an existing method.

Simplified Neutrosophic Linguistic Multi-criteria Group Decision-Making Approach to Green Product Development

For many companies, green product development has become a key strategic consideration due to regulatory requirements and market trends. In this paper, the life cycle assessment technique is used to develop an innovative multi-criteria group decision-making approach that incorporates power aggregation operators and a TOPSIS-based QUALIFLEX method in order to solve green product design selection problems using neutrosophic linguistic information. Differences in semantics as well as the risk preferences of decision-makers are considered in the proposed method. The practicality and effectiveness of the proposed approach are then demonstrated through an illustrative example, in which the proposed method is used to select the optimum green product design, followed by sensitivity and comparative analyses.

A Neutrosophic Normal Cloud and Its Application in Decision-Making

In this study, a neutrosophic normal cloud (NNC) and several other related concepts, including a backward cloud generator, two aggregated operators, and an NNC distance measurement, are proposed. Using these concepts, we also construct a multi-criteria group decision-making approach to single-value neutrosophic environments. In the proposed approach, all evaluations provided by decision-makers are aggregated via the backward cloud generator. The resulting NNC reflects the distribution of customer evaluations. An empirical example and a comparative study are also provided in order to illustrate and validate the proposed approach. The results of an empirical case study using data from Tmall.com indicate that the proposed approach could be effectively applied to practical problems.

Multi-Criteria Project Selection Using an Extended VIKOR Method with Interval Type-2 Fuzzy Sets

Project selection can be a real problem of the multi-criteria group decision making if a group of decision makers express their preferences depending on the nature of the alternatives and different criteria with respect to their knowledge about them. The purpose of the project selection process is to analyze project viability and to approve or reject project proposals based on established criteria. Such decisions are often complex, because they require the identification, consideration and analysis of many tangible and intangible factors. This paper presents a multi-criteria group decision-making approach for project selection problem in the type-2 fuzzy environment. The proposed method is an extended version of Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method with interval type-2 fuzzy numbers; it is called type-2 fuzzy VIKOR (T2F-VIKOR). A stepwise procedure is used for ranking and evaluating the alternatives in the developed method, and the best solution is selected considering both the beneficial and nonbeneficial criteria. An illustrative example is presented to show the applicability of the proposed approach in the project selection problems, and the results are analyzed. The results are compared with some existing methods to show the validity of the extended method. We also utilize six sets of criteria weights for analyzing the stability of the proposed method. These analyses show that the obtained results of the proposed method are relatively consistent with other methods and have good stability in different criteria weights.

Multi-criteria Group Decision-Making Approach Based on 2-Tuple Linguistic Aggregation Operators with Multi-hesitant Fuzzy Linguistic Information

In practice, when the frequency of a linguistic term in evaluation information is greater than one, a consensus has been reached and this frequency is regarded to be one by default. However, in case that the frequencies of several elements in a dataset are not identical, their respective importance that was initially distinguishable will become indifferent in a routine processing, which may distort the original intention of decision-makers. As an extension of hesitant fuzzy linguistic term sets (HFLTSs) and hesitant fuzzy linguistic sets (HFLSs), multi-hesitant fuzzy linguistic term sets (MHFLTSs) are thus proposed on the basis of multi-hesitant fuzzy sets (MHFSs) in this paper, and each multi-hesitant fuzzy linguistic term element (MHFLTE) can contain inconsecutive and repeatable linguistic terms. Besides, inspired by the 2-tuple linguistic model and linguistic scale functions, the transformation functions among linguistic terms, 2-tuples, and numerical data are constructed in a generalized form to cope with different semantics. According to practical conditions, decision-makers’ preferences, or experts’ suggestions, various functions can be chosen after some necessary adjustments. In this way, the original essence of vague evaluations can be retained properly and the accuracy and reliability of final results could be further improved. Furthermore, the basic operations and comparison method of MHFLTEs are given on the basis of the transformation functions, and some 2-tuple linguistic aggregation operators of MHFLTEs are also provided to deal with problems where the criteria weights are known or unknown. Based on these prerequisites, a multi-criteria group decision-making approach is established to process multi-hesitant fuzzy linguistic information. The validity and effectiveness of the proposed approach are finally demonstrated through illustration and comparison analysis.

Developing an MCDM method for robot selection with interval type-2 fuzzy sets

Industrial robots have been increasingly used by many manufacturing firms in different industries. The selection of robots for a particular application and manufacturing environment is a difficult task for decision makers. It has become more and more complicated due to increase in complexity, advanced features and facilities that are continuously being incorporated into the robots by different manufacturers. The decision maker needs to identify and select the best-suited robot in order to achieve the desired output with respect to many criteria. In the decision-making process, we usually confront with ambiguity and uncertainty for evaluating the criteria weights and alternatives of the problem. Interval type-2 fuzzy sets which are characterized by an interval membership function can provide us with more degrees of freedom to represent the uncertainty of the real-world problems. This paper presents a multi-criteria group decision-making approach for robot selection in the context of type-2 fuzzy sets. We propose a method based on Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method with interval type-2 fuzzy numbers to handle this problem. The best alternative (robot) is selected in the developed method according to both the ideal and the nadir solutions without defuzzification. An example with eight alternatives (robots) and seven commonly used criteria is used to illustrate the proposed method. We compare the results with some existing methods to show the validity of the extended method. Seven sets of criteria weights and the Spearman correlation coefficient are also utilized for analyzing the stability of the proposed method. It is observed that the obtained rankings of the proposed method are relatively consistent with the other methods and have good stability in different criteria weights. We focus on fuzzy multi-criteria group decision-making for robot selection.Interval type-2 fuzzy sets are considered in this research.An extended version of VIKOR method is developed for group decision-making.An example is utilized for showing the application of the proposed method in robot selection.We compare and analyze the results of the proposed method to represent the validity of it.

An integrated fuzzy multi-criteria group decision-making approach for green supplier evaluation

Purchasing is one of the most vital functions within a company and supplier performance evaluation is one of the most important business processes of the purchasing function. Traditionally, companies have considered factors such as price, quality, flexibility, etc. while evaluating suppliers. However, environmental pressures urge them to consider green issues. This study proposes a decision model for supplier performance evaluation by considering various environmental performance criteria. An integrated, fuzzy group decision-making approach is adopted to evaluate green supplier alternatives. More precisely, a fuzzy analytic hierarchy process (AHP) is applied to determine the relative weights of the evaluation criteria and an axiomatic design (AD)-based fuzzy group decision-making approach is applied to rank the green suppliers. Finally, a case study is given to demonstrate the potential of the methodology.

A model for supplier selection based on fuzzy multi-criteria group decision making

The great number of suppliers with different capabilities, decision about their numbers and combination, and the way of communication and other related relationships, makes their selection in trouble. Since human perception and judgment which have ambiguous natures govern decision making process, using fuzzy sets will be a suitable selection in these cases. In this paper, we proposed a multi-criteria group decision-making approach based on fuzzy sets which can solve supplier selection problems that have much vagueness. The majority of the existing supplier selection approaches focus on operational metrics. This study considers the strategic and operational factors simultaneously. Business process improvement (BPI) is employed as the strategic criteria, and product quality measurement indicators are operational criteria. In this method, at first, linguistic variables are used to assess the importance weights of strategic and operational criterion. Then the rating of suppliers in strategic criteria and scores of suppliers' product in operational criteria are assessed. Finally, suppliers are ranked in terms of their scores in each criterion and the top suppliers are selected. Also, an empirical case study is performed to clarify the procedure of the proposed method. Key words: Supply chain management, supplier selection, fuzzy sets, multi-criteria group decision making, business process improvement.

An integrated multi-criteria group decision-making approach to efficient supplier selection and clustering using fuzzy preference relations

In the last decades, supply chain management (SCM) has become a significant issue in real life and in the literature due to increasing globalisation. Moreover, supplier selection and periodical evaluation has become an important tool for the companies in order to maintain an effective SCM. The main goal of this study is to construct an integrated method to build a decision support system for supplier evaluation and selection that incorporates quantitative and qualitative calculations together to deal with vague and uncertain data available to decision makers. A methodology, which is capable of evaluating and monitoring suppliers' performance, is constructed, using fuzzy analytic hierarchy process (AHP) to weight the established decision criteria and ELECTRE III to evaluate, rank and classify performance of suppliers regarding relative criteria. The proposed methodology is applied to a real-life supplier-selection and classification problem of a pharmaceutical company.

A multicriteria group decision-making approach based on interval-valued intuitionistic fuzzy sets: A comparative perspective

This study develops an approach to tackle multiple criteria group decision-making problems in the context of interval-valued intuitionistic fuzzy sets. Due to conflicting evaluations and insufficient information about the criteria, an interval-valued intuitionistic fuzzy preference relation matrix is employed to determine the relative importance of criteria in terms of pairwise comparisons. The decision matrix, which indicates the degree of alternatives with respect to each criterion, is expressed by interval-valued intuitionistic fuzzy numbers (IVIFNs). In order to integrate interval-valued intuitionistic fuzzy information, some special aggregation operators are created by altering the aggregation operation of IVIFNs. The three families of parametric fuzzy unions and fuzzy intersections are applied in the aggregation operation with comparisons of the ranking results of alternatives. With a linear programming method, the proposed approach uses an optimization model to obtain criterion weights in exact numbers rather than intervals, and subsequently calculates an aggregated IVIFN for each alternative. The score function and accuracy function assist in discriminating between the aggregated IVIFNs, and in generating a final rank of alternatives. Finally, an illustrative supplier selection problem is used to demonstrate how to apply the proposed approach and to observe the computational consequences resulting from various aggregation operators. The results reveal that the parameters of the aggregation operators indeed have an impact on the ranks of alternatives.