A CURRENT APPROACH TO OBJECTIVE CRITERIA WEIGHTING: THE HELLINGER DISTANCE METHOD (HDM)

Multi-criteria decision making (MCDM) deals with the process of making decisions among a number of experts who evaluate several alternatives or solutions over different criteria. Real-world MCDM problems often present changing contexts in which uncertainty has not a probabilistic nature, in such cases, linguistic information has been successfully applied to model such uncertainty. The use of linguistic information in decision making implies making computations with it to solve linguistic MCDM problems, under these conditions, computing with words methodology allows to obtain linguistic outputs from linguistic premises. There is a large amount of computational models to carry out these processes in the specialized literature, being often difficult to make comparison among different models for a specific MCDM problem. Both the large number of existing models and the increasing difficulty of MCDM problems make difficult the resolution of such problems for the decision makers without any additional support. Therefore, the need of decision support tools that consider all relevant information seems evident. For this reason, FLINTSTONES was proposed as a software suite for dealing with MCDM problems within linguistic contexts based on the 2-tuple linguistic model. This contribution presents an updated version, FLINTSTONES 2.0, that includes new useful features for supporting MCDM resolution processes.

In this study, problems in the manufacturing environment were solved using the multicriteria group decision-making (MCGDM) approach with the recently developed m-polar fuzzy (mF) paradigm. This is a useful strategy when applied to various industrial selection issues. Numerous multi-criteria decision-making (MCDM) approaches are currently in use, each with distinctive features and results. The mF set combined with the technique for order performance by similarity to the ideal solution (TOPSIS) method is a methodology for solving MCGDM issues. Compared to the current and well-established MCDM techniques, the mF TOPSIS method is accurate in its performance, easy to calculate, and can address MCDM and MCGDM problems. The rank performances of the two MCDM approaches were compared using the Spearman’s rank correlation coefficient. This study compares the rank performance acquired by the mF TOPSIS technique to the rank performance obtained by earlier plans to assess the rank performance of industrial selection problems, such as cutting fluid selection, flexible manufacturing system (FMS) selection, and robot selection. It was found that the m-polar fuzzy TOPSIS method, which was developed to solve MCGDM problems, can be used to calculate MCDM problems from the manufacturing environment.

Simplified neutrosophic sets (SNSs) can effectively solve the uncertainty problems, especially those involving the indeterminate and inconsistent information. Considering the advantages of SNSs, a new approach for multi-criteria decision-making (MCDM) problems is developed under the simplified neutrosophic environment. First, the prioritized weighted average operator and prioritized weighted geometric operator for simplified neutrosophic numbers (SNNs) are defined, and the related theorems are also proved. Then two novel effective cross-entropy measures for SNSs are proposed, and their properties are proved as well. Furthermore, based on the proposed prioritized aggregation operators and cross-entropy measures, the ranking methods for SNSs are established in order to solve MCDM problems. Finally, a practical MCDM example for coping with supplier selection of an automotive company is used to demonstrate the effectiveness of the developed methods. Moreover, the same example-based comparison analysis of between the proposed methods and other existing methods is carried out.

Purpose – The purpose of this paper is to report a study in which analytical network process (ANP) was used for selecting the best concept from sustainability view point. Design/methodology/approach – The concept selection in the sustainability viewpoint is a typical multi-criteria decision-making (MCDM) problem involving complex interrelationship among the decision criteria. The formulated MCDM problem of sustainable concept selection was solved using ANP. The sensitivity analysis was also being conducted to validate the results. Findings – The interrelationship among the decision criteria was analyzed using ANP, and the best alternative was selected based on the computation of Product Sustainability Weighted Index (PSWI). The selected best alternative was subjected to implementation in the case organization. Research limitations/implications – The study deals with the formulation of sustainable concept selection as a typical MCDM problem and providing solutions using ANP. The best alternative “weight reduction” was subjected to implementation. The developed MCDM problem also could be solved using hybrid MCDM methods. Practical implications – The study focuses on selecting the best sustainability concept for an Indian automotive component manufacturing organization. Hence, the inferences being derived from the study are practically feasible and contribute toward the improvement of product sustainability. Originality/value – The formulation of a hierarchical model for sustainable concept selection as MCDM problem and generating solution using ANP is the contribution of the authors.

In recent years, Dempster–Shafer (D–S) theory has been widely used in multi-criteria decision-making (MCDM) problems due to its excellent performance in dealing with discrete ambiguous decision alternative (DA) evaluations. In the general framework of D–S-theory-based MCDM problems, the preference of the DAs for each criterion is regarded as a mass function over the set of DAs based on subjective evaluations. Moreover, the multi-criteria preference aggregation is based on Dempster’s combination rule. Unfortunately, this an idea faces two difficulties in real-world applications: (i) D–S theory can only deal with discrete uncertain evaluations, but is powerless in the face of continuous uncertain evaluations. (ii) The generation of the mass function for each criterion relies on the empirical judgments of experts, making it time-consuming and laborious in terms of the MCDM problem for large-scale DAs. To the best of our knowledge, these two difficulties cannot be addressed with existing D–S-theory-based MCDM methods. To this end, this paper proposes a clustering MCDM method combining D–S theory with the analytic hierarchy process (AHP) and the Silhouette coefficient. By employing the probability distribution and the D–S theory to represent discrete and continuous ambiguous evaluations, respectively, determining the focal element set for the mass function of each criterion through the clustering method, assigning the mass values of each criterion through the AHP method, and aggregating preferences according to Dempster’s combination rule, we show that our method can indeed address these two difficulties in MCDM problems. Finally, an example is given and comparative analyses with related methods are conducted to illustrate our method’s rationality, effectiveness, and efficiency.

Simplified neutrosophic sets (SNSs) can effectively solve the uncertainty problems, especially those involving the indeterminate and inconsistent information. Considering the advantages of SNSs, a new approach for multi-criteria decision-making (MCDM) problems is developed under the simplified neutrosophic environment.

The α-Discounting Method was developed to be an alternative to and extension of the Analytical Hierarchy Process (AHP) to solve multi-criteria decision-making (MCDM) problems with non-commensurable and conflicting criteria. In contrast to the AHP, this method works not only for pairwise comparisons but also for n-wise comparisons if relative importance of criteria can be expressed in a system of linear homogenous equations. This method also has a comparative advantage as it can transform those MCDM problems, classified as inconsistent by the AHP, into a consistent form. This study briefly compares the two methods and then develops the Fuzzy α-Discounting Method for Multi-Criteria Decision Making (Fα-DM MCDM). Two illustrative fuzzy MCDM problems from the literature have been solved to show how the Fα-DM MCDM works.

Fuzzy multicriteria decision making (MCDM) has been widely used in ranking a finite number of decision alternatives characterised by fuzzy assessments with respect to multiple evaluation criteria. The MCDM methods suitable for solving a given decision problem usually differ in their normalisation process and aggregation process for handling the performance ratings of the decision alternatives and the weights of the evaluation criteria. The overall preference of a decision alternative is obtained by aggregating the criteria weights and the performance ratings of the alternatives, on which the ranking is based. Due to their structural differences, these methods often produce inconsistent ranking results for the same fuzzy MCDM problem. To address this issue, this study develops a novel approach for the development and validation of fuzzy MCDM models. The approach incorporates three normalisation methods, three aggregation methods, and a α-cut based defuzzification method to develop fuzzy MCDM models. The α-cut based defuzzification method allows the decision maker’s attitude on fuzzy assessments to be incorporated into the decision making process. To examine the validity of the fuzzy MCDM models available for a given decision problem, a new validation process is developed based on the fuzzy clustering technique to assist in selecting a valid outcome from the inconsistent ranking results produced by these models. To demonstrate the effectiveness of the fuzzy MCDM model development and validation approach, three practical applications under various decision contexts are conducted. The first application is about the airport performance evaluation problem. This study selects 12 Asia-Pacific major international airports as the decision alternatives of the evaluation problem and identifies 19 quantitative and qualitative evaluation criteria under the airport operator, passenger, and airline dimensions. Based on three normalisation methods and two aggregation methods, six fuzzy MCDM models are developed which produce inconsistent ranking results for the evaluation problem. The ranking validity of the six models is examined by the validation process using fuzzy clustering and the most valid model is selected. The second application is concerned with the scrap metal buyer selection problem. This study considers five recycling companies in southern China as the decision alternatives of the buyer selection problem and identifies four qualitative selection criteria under the economic and environmental dimensions. Based on three normalisation methods and three aggregation methods, seven fuzzy MCDM models are developed which produce inconsistent ranking results for the selection problem. The ranking validity of the seven models is examined by the validation process using fuzzy clustering and the most valid model is selected. The third application deals with the non-ferrous scrap metal supplier selection problem. This study considers 15 scrap metal suppliers as the decision alternatives of the supplier selection problem and identifies five quantitative and qualitative selection criteria for a non-ferrous scrap metal buyer. Based on three normalisation methods and three aggregation methods, seven fuzzy MCDM models are developed which produce inconsistent ranking results for the selection problem. The ranking validity of the seven models is examined by the validation process using fuzzy clustering and the most valid model is selected. With the development of the approach and the three empirical applications, this study makes significant methodological and practical contributions. The approach addresses the validity issue of the cardinal rankings generated by different fuzzy MCDM models. In practical applications, the subjective attitude of the decision maker is effectively incorporated into the decision making process. With its simplicity in both concept and computation, the approach has a general applicability for solving general MCDM problems, and is particularly suited to decision situations where the ranking results produced by different fuzzy MCDM models differ significantly.

Archimedean [formula omitted]-norm and [formula omitted]-conorm coupled q-rung orthopair fuzzy TOPSIS method for unknown criteria weighting information

Multi-criteria decision making with interval type 2 fuzzy Bonferroni mean

All assets and attempts of the people are threatened by uncertainty named as the risk. Insurance is a social security tool used to recover the loss that may arise as a result of the realization of risks. Insurance contract is established by mutual agreement between an insurance company (insurer) and the insurance holder (insured). The insurer takes over the insurance coverage, the insured falls under the premium payment obligation with the insurance contract. The contract may be signed for life (personal accident, health), goods (automobiles, house, fire, transportation, engineering), liability, legal protection and credit. There are numerous insurance companies in the market and the contracts may change from insurance company to company. Therefore, it is important to select the insurance company that meets the business needs in the best way. This selection may be handled as a MCDM (Multi Criteria Decision Making) problem. MCDM problems refer to make a decision for the alternatives characterized by multiple, usually conflicting, criteria and there are several methods for solving MCDM problems. In this paper, QUALIFLEX (QUALItative FLEXible) and ORESTE (Organization, Rangement Et Synthese De Donnes Relationnelles) methods are used for the insurance company selection problem of the textile firm in Denizli. The insurance company alternatives are ranked by these methods and the results are compared

WITHDRAWN: Application of bipolar vague sets on MCDM problems

Due to the diversity of information, many decision-making problems cannot be solved based on a single criterion. The complexity of assessment objects and the limitations of individual cognition cause the opinions given by experts uncertain, which further aggravates decision-making difficulties. Although fuzzy sets and intuitionistic fuzzy sets are proposed to express vague information, they still have the problem of losing information. As a tool to express uncertain information, interval techniques can effectively prevent the loss of information and improve the accuracy of decision-making. In this regard, many scholars applied interval analysis techniques and their fuzzy extensions to solve multi-criteria decision-making (MCDM) problems. This study reviews 195-related articles published from 2007 to 2022, and analyzes the research progress of interval analysis techniques and their fuzzy extensions in MCDM problems. Through bibliometrics analyses, publication and citation trends as well as productive countries can be intuitively and quantitatively obtained. Then, we review theories and methods regarding the combination of interval techniques and MCDM methods, and find that interval-valued fuzzy sets are extensively discussed and combined with TOPSIS. Next, real-world applications of these publications are reviewed, and we obtain that interval techniques are mainly used in supply chain selection. Finally, we propose future directions regarding interval techniques in MCDM problems. It is hoped that this study would be helpful for scholars and practitioners to carry out further research.

Since light fidelity (LiFi) and wireless fidelity (WiFi) do not interfere with one another, a LiFi/WiFi hybrid network may provide superior performance to existing wireless options. With many users and constant changes, a network can easily become overloaded, leading to slowdowns and fluctuations in data transfer speeds. Access point assignment (APA) is required with the increase of users, which can negatively impact the system performance and quality-of-service (QoS) due to mobility and line-of-sight (LOS) blockage. Many variables could influence the APA process; these variables are considered as criteria, such as the network capacity, the degree of blockage, the speed of the connected user, etc. Unlike conditional APA methods, recent studies have considered treating these variables as “evaluation criteria”. Considering these criteria can offer better and more accurate results, eventually enhancing the APA process and QoS. However, the variety of these criteria, the conflict amongst them, their weights (importance), and priority have not been addressed so far. Moreover, treating the criteria equally might result in inaccurate outcomes. Therefore, to solve this issue, it is essential to investigate the impact of each criterion on the APA process. In this work, a multicriteria decision-making (MCDM) problem is formulated to determine a network-level selection for each user over a period of time The decision problem is modeled as a hierarchy that fragments a problem into a hierarchy of simple and small subproblems, and the selection of the AP network among various alternatives is a considered as an MCDM problem. Based on the previous works, we are not aware of any previous research attempts using MCDM methods in the LiFi research area for network selection. Therefore, this work proposes an access point assignment framework using an MCDM approach for users in a hybrid LiFi/WiFi network. The experiment was conducted based on four phases: Five criteria were identified and evaluated with eleven APs (alternatives). The outcome of this phase was used to build the decision matrix and an MCDM was developed and built based on user mobility and blockages with various scenarios using all the criteria; The analytic hierarchy process (AHP) was employed to identify the criterion of the subjective weights of each criterion and to determine the degree of importance supported by experts’ judgement. Determining the weights in the AHP process considered various investigations, including the consistency ratio (CR) and the AHP consensus indicator, which is calculated using the rank-based maximum likelihood method (RGMM) and Shannon entropy techniques. The VIekriteri-Jumsko KOmpromisno Rangiranje (VIKOR) method is adopted in the selection of the optimal AP for the proper selection of whether a LiFi or WiFi AP must serve the users. The integrated AHP–VIKOR was effective for solving the APA and was the best solution based on using weighted criteria simultaneously. Moreover, the ranking outcomes of the developed integrated AHP–VIKOR approach were evaluated using sensitivity analysis. The result of this work takes the APA for hybrid LiFi networks to a new perspective.

Pythagorean fuzzy set (PFS) is an advanced version of intuitionistic fuzzy set which generalizes fuzzy set. Consequently, PFS has a better applicative expression in real-life decision-making (RLDM) or multicriteria decision-making (MCDM) problems due to its capacity to curb uncertainties embedded in decision making. Correlation coefficient is a significant measuring tool applicable to solving RLDM/MCDM problems via Pythagorean fuzzy environment approach. The main aim of this paper is to reexamine Garg’s correlation coefficient for PFSs and generalize it for a better output in resolving MCDM problems. The axiomatic description of correlation coefficient for PFSs is proposed, and the generalized triparametric correlation coefficient for PFSs is characterized with some number of results. Numerical verification of the proposed correlation coefficient is given to validate the preeminence of the generalized correlation coefficient for PFSs over Garg’s approach. Lastly, some MCDM problems such as pattern recognition problem (e.g., classification of mineral fields) and diagnostic medicine in the framework of Pythagorean fuzzy pairs are discussed with the aid of the novel correlation coefficient. This proposed measuring tool could be exploited in other MCDM problems via object-oriented approach.