Group Decision-making Environment Research Articles

CILOS-WASPAS approach based on Schweizer–Sklar power operators for evaluating cosmetic brands in a group decision-making environment

CILOS-WASPAS approach based on Schweizer–Sklar power operators for evaluating cosmetic brands in a group decision-making environment

Managerial risk data analytics applications using grey influence analysis (GINA)

We observe and analyze the causal relations among risk factors in a system, considering the manufacturing supply chains. Seven major categories of risks were identified and scrutinized and the detailed analysis of causal relations using the grey influence analysis (GINA) methodology is outlined. With expert response based survey, we conduct an initial analysis of the risks using risk matrix analysis (RMA) and the risks under high priority are identified. Later, the GINA is implemented to understand the causal relations among various categories of risks, which is particularly useful in group decision-making environments. The results from RMA concludes that the capacity risks (CR) and delays (DL) are in the category of very high priority risks. GINA results also ratify the conclusions from RMA and observes that managers need to control and manage capacity risks (CR) and delays (DL) with high priorities. Additionally from the results of GINA, the causal factors disruptions (DS) and forecast risks (FR) appear to be primary importance and if unattended can lead to the initiation of several other risks in supply chains. Managers are recommended to identify disruptions at an early stage in supply chains and reduce the forecast errors to avoid bullwhips in supply chains.

A software trustworthiness evaluation methodology for cloud services with picture fuzzy information

VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is one of the most commonly used decision-making technique. This work provides a software trustworthiness evaluation method based on VIKOR technique in a group decision-making (GDM) environment. Entropy is an important measure that refers to the amount of disorder in a decision support system. To quantify the decision quality of decision maker (DM), a new method for determining DM weights is proposed according to the entropy value of decision matrix, which is provided by that DM. A new normalization-projection measure is developed in order to test the closeness between two evaluation matrices in software trustworthiness evaluation process. The group regret measure in VIKOR-based GDM method is based on two negative reference matrices in this model. A new ranking method with classification is also developed in this work. Aiming at the complex and changeable environment, these new measures and methods are expected to enrich VIKOR techniques and to play a constructive role in group decision support systems. The feasibility and practicability of developed method are verified by some experiments. This study finds that the different methods (models) may have different results in situations where the same decision data are provided by DMs; and this study also finds that the different parameters may have different results in situation where the decision model is same. The practical implications of developed GDM method can apply not only to software trustworthiness evaluation, but also to many other product quality evaluations.

Approach to the personnel selection in a group decision-making environment based on the use of the MULTIMOORA and PIPRECIA-S methods

Approach to the personnel selection in a group decision-making environment based on the use of the MULTIMOORA and PIPRECIA-S methods

Innovative Multi-Criteria Decision-Making Approach for Supplier Evaluation: Combining TLF, Fuzzy BWM, and VIKOR

When confronted with underperforming suppliers, the need to evaluate and improve supplier performance becomes apparent. However, the inherent inaccuracies in information introduce complexity, especially when subjective human judgment is involved in the supplier evaluation process. Associated with such problem, this study presents a novel methodology for supplier performance evaluation in the crumb rubber industry, integrating the Taguchi Loss Function (TLF), fuzzy Best-Worst Method (BWM), and VIKOR technique in group decision-making environment. Aimed at addressing the challenges in industries with variable supplier quality and performance, such as the crumb rubber industry in Indonesia, the methodology was empirically tested to demonstrate its practical utility. The process involved identifying evaluation criteria through literature review tailored to the needs of decision makers (DMs), applying TLF to quantify losses from supplier performance deviations, using fuzzy BWM to determine criteria weights based on the DMs judgment, and employing the VIKOR technique for comprehensive supplier ranking. The findings underscore the methodology's effectiveness in enhancing decision-making, offering a unified metric that accommodates diverse criteria and balances precise data with subjective assessments. This approach simplifies the evaluation process, particularly in situations with conflicting interests among decision-makers. Demonstrating its practical application in the crumb rubber industry, the study highlights the methodology's potential for broader industrial applicability. Future research could explore comparative analyses with other analytical methods, further establishing the methodology's robustness and adaptability in different management contexts.

Intuitionistic fuzzy DEMATEL for developing causal relationship of water security

PurposeThis study aims to develop a cause-effect relationship between criteria that contribute to water security using the Intuitionistic Fuzzy-Decision-Making Trial and Evaluation Laboratory (IF-DEMATEL) method. Differently from the typical DEMATEL which utilizes crisp numbers, this modification introduces intuitionistic fuzzy numbers (IFNs) to enhance judgments in a group decision-making environment. In particular, the linguistic variables used in IF-DEMATEL are defined using the concept of three-tuple of IFNs.Design/methodology/approachData with the linguistic variable “influence” were collected from a group of experts in water security via personal unstructured interviews. Seven water security criteria are considered in this study. Computational software was employed to execute the computational procedures of the IF-DEMATEL method. It is anticipated that by taking into account the hesitation degree of IFNs will reflect the scenario in real life, which could lead to precise decision-making.FindingsThe results show that “Over-ion”, “Saltwater Intrusion” and “Limited Infrastructures” are the cause criteria that contribute to water security. In addition, the relationship map of influence shows that “Water Pollution” and “Rapid Urbanization” are the most vulnerable criteria as these two criteria are most easily affected by other criteria in a unidirectional relation.Practical implicationsIt is anticipated that these findings will serve as useful references for water security management and policymakers.Originality/valueThe present study makes a noteworthy contribution to the modification of DEMATEL where three-tuple of intuitionistic fuzzy numbers are considered in the computations. The present study also provides additional evidence with respect to factors that contribute to water security.

Extended CODAS method for MAGDM with $ 2 $-tuple linguistic $ T $-spherical fuzzy sets

<abstract><p>In the literature, extensions of common fuzzy sets have been proposed one after another. The recent addition is spherical fuzzy sets theory, which is based on three independent membership parameters established on a unit sphere with a restriction linked to their squared summation. This article uses the new extension that presents bigger domains for each parameter for production design. A systematic approach for determining customer demands or requirements, Quality Function Deployment (QFD) converts them into the final production to fulfill these demands in a decision-making environment. In order to prevent information loss during the decision-making process, it offers a useful technique to describe the linguistic analysis in terms of 2-tuples. This research introduces a novel decision-making method utilizing the 2-tuple linguistic $ T $-spherical fuzzy numbers (2TL$ T $-SFNs) in order to select the best alternative to manufacturing a linear delta robot. Taking into account the interaction between the attributes, we develop the 2TL$ T $-SF Hamacher (2TL$ T $-SFH) operators by using innovative operational rules. These operators include the 2TL$ T $-SFH weighted average (2TL$ T $-SFHWA) operator, 2TL$ T $-SFH ordered weighted average (2TL$ T $-SFHOWA) operator, 2TL$ T $-SFH hybrid average (2TL$ T $-SFHHA) operator, 2TL$ T $-SFH weighted geometric (2TL$ T $-SFHWG) operator, 2TL$ T $-SFH ordered weighted geometric (2TL$ T $-SFHOWG) operator, and 2TL$ T $-SFH hybrid geometric (2TL$ T $-SFHHG) operator. In addition, we discuss the properties of 2TL$ T $-SFH operators such as idempotency, boundedness, and monotonicity. We develop a novel approach according to the CODAS (Combinative Distance-based Assessment) model in order to deal with the problems of the 2TL$ T $-SF multi-attribute group decision-making (MAGDM) environment. Finally, to validate the feasibility of the given strategy, we employ a quantitative example to select the best alternative to manufacture a linear delta robot. The suggested information-based decision-making methodology which is more extensively adaptable than existing techniques prevents the risk of data loss and makes rational decisions.</p></abstract>

Risk Evaluation of Logistics Park Projects’ Lifecycle during the COVID-19 Pandemic: Failure Mode and Effects Analysis

Logistics parks served as critical logistics facilities during the coronavirus disease 2019 pandemic. Constructing these large-scale projects including their subsequent operations involved unusual risk factors that required comprehensive risk evaluations. Therefore, it is vital to examine the potential risk factors across the entire life cycle of these projects in accordance with the complexity of these large-scale logistics park projects (LLPPs) during the pandemic. This study proposes a new risk analysis model that evaluates a comprehensive list of risk factors based on the failure mode and effects analysis (FMEA). The hesitant uncertain linguistic Z-numbers (HULZNs) and fuzzy C-means (FCM) clustering algorithm are incorporated into the FMEA model to evaluate the risk. The practicability of the model was assessed in a case study. The findings identified and distinguished risk factors based on their levels of influences and priorities in dealing with uncertainties during the decision-making process in LLPPs. The FMEA model’s new methodological assessment also contributes to the efficient risk evaluation process through its more reliable and reasonable failure mode risk clustering, particularly for complex group decision-making environments during the pandemic.

Two-Stage Feedback Mechanism With Different Power Structures for Consensus in Large-Scale Group Decision Making

This article investigates a two-stage consensus feedback mechanism that considers different power structures in large-scale group decision-making (LSGDM) environments. A Louvain algorithm type is used to detect subgroups in LSGDM by trust relationship. The concepts of internal subgroups and external subgroup consensus levels are defined, and an approach to identify the inconsistent individual/subgroup is developed to avoid the issue of “pseudoconsensus.” Combined with the background of the company’s shareholder power management regulations, the following three power structures are constructed: “absolute power,” “relative power,” and “democratic power.” A two-stage feedback mechanism is investigated with minimum adjustments to achieve the optimal power allocation under each power structure. This mechanism supports individuals reach consensus both inside and outside their subgroup. An illustrative example and discussions to verify the validity of the proposed method are reported.

Selection of an appropriate material for a crash-box in multi-attribute group decision-making environment using R-method

Crash-box is an essential passive safety device in an automobile that absorbs the kinetic energy of an external impact during an accident, thereby improving passenger safety. Selecting a suitable material for fabricating a crash-box to maximize its crashworthiness performance is a critical issue and is a multi-attribute decision-making (MADM) problem. This study focuses on choosing an appropriate aluminium alloy for a crash-box out of 16 different frequently used wrought aluminium alloys considered as alternatives based on eight crashworthiness performance indicators used as attributes by taking the opinions of three decision-makers (DMs). The effect of varying the material of thin-walled circular crash-boxes with identical geometry is first examined by simulating their axial quasi-static collapse using ABAQUS/Explicit. Various crashworthiness performance indicators obtained from these simulations are used for the decision-making process. While taking a decision, the opinions of several DMs from varied backgrounds are generally considered. A systematic approach is proposed to determine the importance of each DM involved in group decision-making situations by considering their credentials, such as educational qualification, work experience, etc., using a ranking based MADM technique known as ‘R-Method’. Opinions of multiple DMs are then combined to come up with a single final decision, called a consensus. The application of the R-Method is discussed in detail to derive consensus in a group decision-making environment. The aluminium alloy AA 7075 T6 is the best, while AA 3003 H12 is the least preferred material for manufacturing a crash-box of the considered geometry as per the proposed method.

Pivotal voting: The opportunity to tip group decisions skews juries and other voting outcomes

Many important social and policy decisions are made by small groups of people (e.g., juries, college admissions officers, or corporate boards) with the hope that a collective process will yield better and fairer decisions. In many instances, it is possible for these groups to fail to reach a decision by not garnering a minimum number of votes (e.g., hung juries). Our research finds that pivotal voters vote to avoid such decision failure-voters who can "tip" their group into a punishment decision will be more likely to do so. This effect is distinct from well-known social pressures to simply conform with others or reach unanimity. Using observational data from Louisiana court cases, we find a sharp discontinuity in juries' voting decisions at the threshold between indecision and conviction (Study 1). In a third-party punishment paradigm, pivotal voters were more likely to vote to punish a target than nonpivotal voters, even when holding social information constant (Study 2), and adopted harsher views about the target's deservingness of punishment (Study 3). Using vignettes, we find that pivotal voters are judged to be differentially responsible for the outcomes of their votes-those who "block" the group from reaching a punishment decision are deemed more responsible for the outcome than those who "fall in line" (Study 4). These findings provide insight into how we might improve group decision-making environments to ensure that their outcomes accurately reflect group members' actual beliefs and not the influence of social pressures.

Large-scale group decision-making for prioritizing engineering characteristics in quality function deployment under comparative linguistic environment

Quality function deployment (QFD) is a well-known tool for quality management. With its systematic operation mechanism for translating customer requirements (CRs) into corresponding engineering characteristics (ECs), QFD highly facilitates product/service design and improvement. However, the evolution of the decision-making environment has made the quantification within the traditional QFD operation could hardly able to meet the demanded efficiency as well as the accuracy of the generated results. To cope with the problem, an enhanced large-scale group decision-making method that integrates proportional hesitant fuzzy linguistic term sets (PHFLTSs) and the cumulative prospect theory (CPT) is put forward by this study to determine the ranking priority of ECs in doing QFD. To facilitate QFD team members carry out an easier and more accurate evaluation, they would use comparative linguistic expressions in making judgments, and the comparative linguistic expressions are subsequently transformed into hesitant fuzzy linguistic term sets (HFLTSs). The obtained HFLTSs are transformed into PHFLTSs based on the statistical method under a group decision-making environment for preventing information loss, and, at the same time, improving calculation accuracy. Taking decision-makers’ heterogeneity and risk attitudes into consideration, the CPT method is adopted and extended under the PHFL environment to prioritize the ECs in QFD operation. To illustrate how the proposed large-scale group decision-making-based QFD method is to be applied, a case study about solar photovoltaic cell development is presented. Furthermore, a comparative analysis is conducted to exhibit the effectiveness of the proposed QFD method.

Fuzzy cluster analysis and decision-making algorithms for optimal water distribution network design

ABSTRACT Three objectives, maximization of resilience, minimization of cost, and minimization of leakages, were considered in the Water Distribution Network (WDN) framework for a benchmark problem of Hanoi WDN and a real-world problem, Pamapur WDN, Telangana, India. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is employed to generate Non-dominated WDN Strategies (NWDNS). In order to simplify the decision-making process of engineers, Fuzzy Cluster Analysis (FCA) is employed to categorize NWDNS into groups. An optimum number of clusters are based on the Elbow method. Representative NWDNS, i.e. RNWDNS for each sub-cluster, is based on the membership values. Ranking of RNWDNS is performed with two decision-making algorithms, namely, Preference Ranking Organization METHod for Enrichment of Evaluations-2 (PROMETHEE-2) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The additive ranking rule is also applied to obtain ranks in a group decision-making environment to arrive at the optimal WDN. It is observed that 1020 NWDNSH (H represents Hanoi) generated for the Hanoi are optimally classified into 18 clusters based on the Elbow method, and A13 representing RNWDNSH 37 (cost, resilience, and leakages, respectively, are 7.8818 × 106 $, 0.3194, 0.2024 × 10−3 m3/s) is preferable, respectively. Whereas 272 NWDNSP (P represents Pamapur) generated for the Pamapur are classified into 9 clusters where S6 representing RNWDNSP 203 (cost, resilience, and leakages, respectively, are 3.5159 × 106 Rs., 0.8367, 0.5317 × 10−3 m3/s) is preferred, respectively. The present study facilitated seamless integration of NSGA-II (generation of Pareto front), FCA (reducing to manageable set) and MCDM methods (to rank the reduced manageable set) in a robust manner.

Research on credit rating and risk measurement of electricity retailers based on Bayesian Best Worst Method-Cloud Model and improved Credit Metrics model in China's power market

With the advancement of Power Market reform and the opening of the Demand Side market, the dynamic risk management of electricity retailers has attracted more attention. This paper designs a risk prevention linkage mechanism of credit evaluation-risk measurement for retailers. Firstly, this paper constructs a retailers' credit evaluation index system containing 18 indexes, and proposes a retailers' credit rating technology based on Bayesian Best Worst Method (BBWM)- Cloud model. On the basis of Best Worst Method (BWM), BBWM uses Multinomial Distribution to model the input indexes, so as to obtain more reliable weights in the group decision-making environment. Secondly, a credit risk measurement model based on improved Credit Metrics model and CVaR method for electricity retailers is constructed. The Credit Metrics model adopts the Marked-to-Market system, which can consider the retailers' default risk and the risk of credit rating changes, and CVaR method can accurately describe the retailers’ credit risk. Finally, four electricity retailers are taken as examples to verify the effectiveness and scientificity of the model. This paper can provide guarantee and theoretical basis for the credit management of Power Sales market, standardize the behavior of electricity retailers and reduce the transaction risk of Power Market.

Three-way multi-criteria group decision-making method in a fuzzy [formula omitted]-covering group approximation space

At present, some researchers have studied the decision-making methods in a fuzzy β-covering approximation space, which not only can play the advantages of rough set theory in dealing with inaccurate data, but also inherit the ranking function of traditional decision-making methods. However, these methods merely consider the ranking problem in a single decision-maker environment and most of the decision-making problems in reality are group decision-making problems that need to consider multiple opinions. In light of this, in this paper, we propose the concept of fuzzy β-covering group approximation spaces and establish a three-way multi-criteria group decision-making method, which can solve some ranking and classification problems of objects under a group decision-making environment. Based on a fuzzy β-covering group approximation space, we firstly propose two fuzzy β-fitting neighborhoods with pessimistic and optimistic attitudes to construct a fuzzy binary relation between any two objects. Secondly, we introduce an overall loss function to estimate the risk loss of all objects when they choose different behaviors in different states under a group decision-making environment. Subsequently, based on the conditional probability estimation formula and the overall loss function, we propose a three-way group decision-making idea in a fuzzy β-covering group approximation space, which contains eight different decision-making attitudes to meet the preferences of decision-makers. Furthermore, for the ranking and classification performance of our method, we use numerical analysis, comparative analysis and Spearman analysis to illustrate the feasibility and superiority of the method, and take experimental analysis to test the stability of our method.

Hospital selection under Insured Public Health Schemes in the multi-criteria group decision-making environment

Abstract A thriving healthcare system perfectly reflects economic development and contentment amongst the people of any region. With increasing anxiety concering health and growing medical needs, hospitals worldwide face substantial challenge to provide patients with adequate medical facilities under one roof. With a fragile state of the health industry in a developing country like India, there is a need for the hospitals to opt for international standards and comply with other premier health centers of the country. This paper aims to select the hospitals based on incongruous and conflicting criteria involving group decision-making using the Intuitionistic Fuzzy (IF) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The criteria used are concomitant to an insured public health scheme named Ayushman Bharat-National Health Protection Scheme (AB-NHPS) of the Government of India. For each alternative Euclidean distance has been used to calculate the positive and negative separation measure from the ideal solution. The relative closeness to the ideal solution has been used to rank the hospitals. The result is a list of hospitals ranked from best to worst based on the laid criteria. It can aid governing bodies in decision-making under an uncertain environment with multiple complex criteria to analyze.

Three-way decision model under a large-scale group decision-making environment with detecting and managing non-cooperative behaviors in consensus reaching process

Three-way decision model under a large-scale group decision-making environment with detecting and managing non-cooperative behaviors in consensus reaching process

Strategic marketing initiatives for small co-operative enterprises generated from SWOT-TOWS analysis and evaluated with PROMETHEE-GAIA

• It offers a methodology in defining, developing, and prioritizing strategic marketing initiatives for co-operative enterprises. • The crafting of strategic marketing initiatives is carried out through a SWOT-TOWS analysis. • The PROMETHEE-GAIA approach is used to determine priority strategic marketing initiatives. • Partnerships with secondary and tertiary co-operatives on R&D activities is the most preferred initiative. • The inclusion of competitive employee compensation and benefits yields the least preferred initiative. Due to the long-term agenda strategic marketing initiatives pose to co-operative enterprises, the development and the priority identification of these initiatives under multiple criteria must be rigorous and systematic for greater tractability of the decision-making processes. Thus, this work offers an integrated methodological framework in determining the viable strategic marketing options of the case co-operative based on a SWOT-TOWS analysis, adopting relevant strategic marketing criteria for small co-operatives, and evaluating the degree of importance of identified strategic marketing initiatives via a Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) - Geometrical Analysis for Interactive Decision Aid (GAIA) approach, popularly known as PROMETHEE-GAIA. The proposed framework elucidates inclusive strategy development and systematic decision analysis under a group decision-making environment. A case study of a small co-operative enterprise in the Philippines is used to demonstrate the applicability of the proposed integrated approach. The case findings suggest that partnerships with secondary and tertiary co-operatives on R&D activities as the most preferred initiative, followed by the implementation of business process improvement activities for customer relationship management. Meanwhile, the inclusion of competitive employee compensation and benefits yields as the least preferred initiative. The results of the PROMETHEE are further analyzed with GAIA in order to gain detailed insights into the case decision-making problem. The sensitivity analysis yields stability intervals for criteria weights. Also, it is found that the PROMETHEE results are robust to changes in the choice of criterion types and minor changes in parameter values of the criteria. This work advances the domain literature by offering small co-operative enterprises an integrated approach to systematically develop and evaluate strategic marketing initiatives – crucial activities for planning, resource allocation, and policymaking.

EMBEDDED REMOTE GROUP ENVIRONMENT THROUGH MODIFICATION IN MACBETH – AN APPLICATION OF CONTRACTOR’S SELECTION IN CONSTRUCTION

A group decision environment has profound roots in MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) analysis which indeed has plentiful advantages; however, many researchers envisage the embedded group decision system as an impediment in actual implementation. The accessibility of explicit interaction of decision makers at a single platform in the form of embedded group decision environment is a great impediment to the researchers. Accordingly, this research aims to tailor a novel alternative system of dealing with the embedded group decision under a remote group decision environment via integrating MACBETH and Exploratory Factor Analysis. The study finds that an embedded remote group decision making system could serve as an alternative system of group decision making which has plentiful perks in group decision applications. This system could help researchers to carry out research without confusing in embedded group decision environment but including all decision-makers in the model. The implication of proposed system is not only limited to MACBETH; however, due to system’s versatility, a similar approach could be fruitful for other group-related environments involving collective decisions.

A New Grey Approach for Using SWARA and PIPRECIA Methods in a Group Decision-Making Environment

The environment in which the decision-making process takes place is often characterized by uncertainty and vagueness and, because of that, sometimes it is very hard to express the criteria weights with crisp numbers. Therefore, the application of the Grey System Theory, i.e., grey numbers, in this case, is very convenient when it comes to determination of the criteria weights with partially known information. Besides, the criteria weights have a significant role in the multiple criteria decision-making process. Many ordinary multiple criteria decision-making methods are adapted for using grey numbers, and this is the case in this article as well. A new grey extension of the certain multiple criteria decision-making methods for the determination of the criteria weights is proposed. Therefore, the article aims to propose a new extension of the Step-wise Weight Assessment Ratio Analysis (SWARA) and PIvot Pairwise Relative Criteria Importance Assessment (PIPRECIA) methods adapted for group decision-making. In the proposed approach, attitudes of decision-makers are transformed into grey group attitudes, which allows taking advantage of the benefit that grey numbers provide over crisp numbers. The main advantage of the proposed approach in relation to the use of crisp numbers is the ability to conduct different analyses, i.e., considering different scenarios, such as pessimistic, optimistic, and so on. By varying the value of the whitening coefficient, different weights of the criteria can be obtained, and it should be emphasized that this approach gives the same weights as in the case of crisp numbers when the whitening coefficient has a value of 0.5. In addition, in this approach, the grey number was formed based on the median value of collected responses because it better maintains the deviation from the normal distribution of the collected responses. The application of the proposed approach was considered through two numerical illustrations, based on which appropriate conclusions were drawn.