Fuzzy Group Decision-making Approach Research Articles

A fuzzy reliability assessment methodology for city gas stations based on an extended T-S fault tree

City gas stations (CGSs) play a crucial role in ensuring a stable and safe supply of natural gas to urban users. However, as the service time of stations increases and the performance of components deteriorates, concerns about the safety and reliability of these station have grown among operators and local government authorities. This paper proposes a fuzzy reliability assessment methodology for CGSs that considers the polymorphism of component faults and the uncertainties associated with fault relationships, failure probabilities, and fault magnitudes. The methodology utilizes T-S fuzzy gates to describe the correlation among events and constructs a T-S fuzzy fault tree for CGSs. Component fault states are represented using fuzzy numbers, and a fuzzy group decision-making approach is introduced to evaluate the current fault magnitude of components. To handle the uncertainty caused by sparse failure sample data, a Bayesian updating estimation method is presented to estimate the failure probabilities of components. Furthermore, T-S fuzzy importance analysis is applied to identify the weak points in the CGS system. The effectiveness of the developed methodology is demonstrated through a case study of reliability analysis of a city gas distribution station. The research findings provide valuable support for optimizing the design and implementing preventive maintenance of CGSs.

Fermatean Fuzzy Group Decision-Making Based CODAS Approach for Taxation of Public Transit Investments

The provision of public goods is always a controversial issue in many aspects. Even some additional financial resources can provide public transit systems, the tax revenues constitute a significant part of the investments in practice. Yet, governments should make an important decision regarding tax schemes. This article focuses on how to identify the best tax scheme to finance public transit investments. A three-phase Fermatean fuzzy group decision-making approach is developed to solve the highlighted problem. Qualifications and experience of experts are considered to distinct their reputations. A Fermatean fuzzy direct rating method is introduced to evaluate criteria importance. A Fermatean fuzzy combinative distance-based assessment method is formulated to assess tax schemes. The study provides valuable policy implications and decision-making guidelines using a real-life case. The results show that beneficiaries of public services should pay more, and tax collectors are encouraged to use progressive tax schemes instead of the equal distribution of the cost to the taxpayers. The sensitivity analysis to changes in the threshold parameter is given. The comparative analysis with all state-of-the-art Fermatean fuzzy set based multicriteria decision-making methods is provided. The Fermatean fuzzy group decision-making approach is highly robust and reliable. The approach can solve other public transportation problems.

Prioritization of strategies for a sustainable regional transportation infrastructure by hybrid spherical fuzzy group decision-making approach

Prioritization of strategies for a sustainable regional transportation infrastructure by hybrid spherical fuzzy group decision-making approach

Material selection of sustainable composites by recycling of waste plastics and agro-industrial waste for structural applications: A fuzzy group decision-making approach

Recycling of waste plastics (WP) for the development of sustainable composite materials is one of the effective methods to deal with the problems of the generated plastics wastes. The research proposes an integrated Analytical Hierarchy Process-Techniques for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS) approach based on triangular fuzzy weighted Bonferroni mean operator (TFWBMO) for the selection of an appropriate composition for development of floor tiles by recycling WP and agro-industrial wastes. The decision hierarchy has been formed by collecting the experts' opinion from five different academicians and personalities engaged in the domain of sustainable composites. The present work suggests that the amalgamation of 90 wt % of low-density polyethylene with 10 wt % of fillers is a suitable composition for structural applications. The comparative analyses of the results with well-known techniques and sensitivity analyses have confirmed the reliability of the proposed model. Moreover, Spearman's rank correlation coefficient (SRCC) has verified the statistical consistency of the proposed model. The proposed mathematical model is efficient in handling the vagueness in decision-making and hence can be successfully implemented to overcome the issues of material selection in large scale production of sustainable structural materials. The research has explored the potential of WP in the replacements of the conventional structural materials by a sustainable composites possessing expedient features.

A novel fuzzy group decision-making approach based on CCSD method for thermal insulation board selection problem: A case study

Due to buildings’ high energy consumption and environmental impacts, selecting an appropriate thermal insulation board is crucial in reducing energy consumption in an existing building. Therefore, thermal insulation board selection has become an important issue. This study presents a combined fuzzy group decision-making approach that consists of the integrated fuzzy analytic hierarchy process and fuzzy technique for order preference by similarity to ideal solution, the integrated intuitionistic fuzzy analytic hierarchy process and intuitionistic fuzzy technique for order preference by similarity to ideal solution, and intuitionistic fuzzy technique for order preference by similarity to ideal solution for effectively evaluating thermal insulation boards. However, since these three fuzzy decision-making methods can produce different rankings, decision-making is a complex and challenging process for decision-makers. In order to overcome this problem, correlation coefficient and standard deviation method is utilized to construct the final ranking by integrating these approaches using regression statistics, standard deviations, and correlation coefficients between the closeness coefficients of each alternative in the relevant approach. The proposed approach provides an accurate ranking of thermal insulation boards validated through a real-world case study. Furthermore, the sensitivity analysis indicates that the combinations of different levels of criteria and approaches affect the final ranking. According to the study’s findings, the gray expanded polystyrene foam is the most suitable thermal insulation board in the combined fuzzy group decision-making approach.

Nonconformity Assessment in Building Construction Projects: A Fuzzy Group Decision-Making Approach

Construction nonconformity assessment of buildings is critical to ensure the anticipated quality and living safety for their future occupants. Previous studies have paid less attention to identifying and analyzing building construction nonconformities (BCNs) in the design and construction (D&C) phases. They considered expert judgments in nonconformity assessment, which are critiqued for human bias, uncertainty, and imprecision. In a BCN assessment, previous studies also did not consider the specific time frame to detect construction nonconformities. This study aims to prioritize nonconformities in the D&C phases, addressing the limitations of expert judgment by applying the fuzzy group decision-making approach (FGDMA). The FGDMA computes the defuzzified scores of the nonconformities to prioritize and identify critical nonconformities. The defuzzified scores are explained further by associating them with the corresponding fuzzy numbers to address the limitations involved in expert judgments. The study also identifies the detection time of BCNs and analyzes 15 different Bangladeshi project scenarios to understand their context better. The critical nonconformities identified include premature stressing on concrete, inaccurate water-cement ratios, insufficient concrete compaction, lack of full-time site supervision, and the absence of stirrups in beam-column joints. Critical nonconformities are mostly identified during construction, and residential, commercial, and multipurpose buildings, regardless of ownership (i.e., public or private) and size, have experienced poor quality construction. This study will assist major stakeholders (owner, contractor, consultant, and regulatory authorities) to fully understand the critical nonconformities in different building projects from their preconstruction to construction phases for better quality assurance in providing a safe living and working environment for their future occupants.

Evaluating the sustainability of smart technology applications in healthcare after the COVID-19 pandemic: A hybridising subjective and objective fuzzy group decision-making approach with explainable artificial intelligence.

During the COVID-19 pandemic, some smart technology applications were more effective than had been expected, whereas some others did not achieve satisfactory performance. Consequently, whether smart technology applications in healthcare are sustainable is a question that warrants investigation. To address this question, a hybridising subjective and objective fuzzy group decision-making approach with explainable artificial intelligence was proposed in this study and then used to evaluate the sustainability of smart technology applications in healthcare. The contribution of this research is its subjective evaluation of the sustainability of smart technology applications followed by correction of the evaluation outcome on the basis of the applications' objective performance during the COVID-19 pandemic. To this end, a fuzzy nonlinear programming model was formulated and optimised. In addition, the impact of several major global events that occurred during the pandemic on the sustainability of smart technology applications was considered. The proposed methodology was applied to evaluate the sustainability levels of eight smart technology applications in healthcare. According to the experimental results, three applications-namely healthcare apps, smartwatches, and remote temperature scanners-are expected to be highly sustainable in healthcare, whereas one application, namely smart clothing, is not.

A knowledge-based expert system to assess power plant project cost overrun risks

Preventing cost overruns of such infrastructure projects as power plants is a global project management problem. The existing risk assessment methods/models have limitations to address the complicated nature of these projects, incorporate the probabilistic causal relationships of the risks and probabilistic data for risk assessment, by taking into account the domain experts’ judgments, subjectivity, and uncertainty involved in their judgments in the decision making process. A knowledge-based expert system is presented to address this issue, using a fuzzy canonical model (FCM) that integrates the fuzzy group decision-making approach (FGDMA) and the Canonical model (i.e. a modified Bayesian belief network model). The FCM overcomes: (a) the subjectivity and uncertainty involved in domain experts’ judgment, (b) significantly reduces the time and effort needed for the domain experts in eliciting conditional probabilities of the risks involved in complex risk networks, and (c) reduces the model development tasks, which also reduces the computational load on the model. This approach advances the applications of fuzzy-Bayesian models for cost overrun risks assessment in a complex and uncertain project environment by addressing the major constraints associated with such models. A case study demonstrates and tests the application of the model for cost overrun risk assessment in the construction and commissioning phase of a power plant project, confirming its ability to pinpoint the most critical risks involved ̶ in this case, the complexity of the lifting and rigging heavy equipment, inadequate work inspection and testing plan, inadequate site/soil investigation, unavailability of the resources in the local market, and the contractor's poor planning and scheduling.

A Fuzzy Group Decision-making Model for Determining the Most Influential Persons in the Sustainable Prevention of Accidents in the Construction SMEs

Safety in the sustainable construction is a game with uncertain reality, with human’s life. Managers in power, involved in the construction project life cycle, create rules for the game and are the main players. The project’s success can be perceived differently by stakeholders. A company’s competitiveness, successful implementation of the projects and effective safety management depends on the strategic allocation of its human resources in order to select a proper project team in alignment with employee capabilities. This requires detailed factors reflecting their role (in line with their interests and attitudes) and knowing which of them has a decisive influence on the successful implementation of the project. The real data describing key factors in this concept can be provided as uncertain conditions. This work presents a novel integrated modified fuzzy group decision-making approach to select and rank the most influential persons ensuring the sustainable prevention of accidents at work in the small and medium-sized construction enterprises. This model includes the Delphi method and fuzzy extensions of Eckenrode’s criteria rating method. The proposed model could be expanded in order to select the most suitable individuals for sustainable management of safety and, moreover, for the effective implementation of safety and health measures.

Modified Fuzzy Group Decision-Making Approach to Cost Overrun Risk Assessment of Power Plant Projects

AbstractCost overrun risk assessment in the preliminary stages of a power plant project is laden with subjectivity, uncertainty, imprecision, and the vagueness of expert judgment. Current fuzzy app...

A fuzzy group decision making approach to new product concept screening at the fuzzy front end

Efficient new product concept screening plays an important role in new product development (NPD). Due to the inherent complexity, incompleteness and ambiguity in NPD, fuzzy approaches have been widely developed and used for new product concept screening. Because of the group behaviours (diversity of gate-team members and senior management’s preference), the new product concept screening is typical group decision-making problem. Moreover, it is usually more efficient to conduct the screening evaluation in a gate-team and make a screening decision outside the gate-team by senior management. Therefore, it is necessary to provide a result with a high degree of flexibility and credibility so as to better support the senior management to make a screening decision in NPD. Existing fuzzy approaches neglect these two issues. Towards this end, a three-stage fuzzy group decision-making approach is proposed to assist the senior management perform new product concept screening. A case study is borrowed from the literature to illustrate our proposed approach. Our approach is compared with two existing techniques from theoretical and empirical perspectives. The results show that the proposed approach can capture the group behaviours and present a relatively realistic and informative result which gives the senior management a high degree of flexibility and credibility in new product concept screening.

A fuzzy hybrid group decision support system approach for the supplier evaluation process

The quality and delivery performance of a typical company depends largely on the capability of its suppliers. It is therefore important for a company to evaluate the performance of its existing suppliers. However, the problem of supplier evaluation is rather complex when a group of decision makers responsible for these suppliers are geographically dispersed. Evaluating decisions requiring the elicitation of vague and imprecise linguistic information expressed in fuzzy terms. For this purpose, a hybrid fuzzy group decision-making approach for supplier evaluation is proposed, integrating the fuzzy analytic hierarchy process (F-AHP) and the fuzzy preference-ranking-organization-method-for-enrichment-of-evaluation (PROMETHEE) group decision support system (F-PROMETHEE GDSS) method. F-AHP is used to determine the weight of multiple criteria while F-PROMETHEE GDSS is applied for the final ranking of suppliers from a group decision viewpoint. The approach advances the supplier evaluation context in two aspects. First, it treats each decision maker uniquely in terms of the preference function and threshold levels. Second, it provides rapid communication between teams through video conferencing. The hybrid approach is used to evaluate the suppliers of a dairy company.

Fuzzy group decision-making with generalized probabilistic OWA operators

This paper presents a new fuzzy group decision-making approach by using probabilities and ordered weighted averages (OWA). Thus, we are able to assess the problem considering probabilistic information and the attitudinal character of the decision maker. For doing so, the fuzzy generalized probabilistic OWA (FGPOWA) operator is presented. It is an aggregation operator that unifies the probability and the OWA operator considering the degree of importance that each concept has in the aggregation. It also uses fuzzy numbers that deal with uncertain environments where the available information is imprecise. Moreover, it includes a wide range of particular cases including the fuzzy generalized probabilistic aggregation, the fuzzy POWA operator and the fuzzy GOWA operator. A further generalization to this approach is presented by using quasi-arithmetic means forming the fuzzy quasi-arithmetic POWA (Quasi-FPOWA) operator. An application in fuzzy group decision making is developed by using the multi-person - FGPOWA (MP-FGPOWA) operator.

Hybrid Multiattribute Group Decision Making Based on Intuitionistic Fuzzy Information and GRA Method

Hybrid multiple attribute group decision making involves ranking and selecting competing courses of action available using attributes to evaluate the alternatives. The decision makers assessment information can be expressed in the form of real number, interval-valued number, linguistic variable, and the intuitionistic fuzzy number. All these evaluation information can be transformed to the form of intuitionistic fuzzy numbers. A combined GRA with intuitionistic fuzzy group decision-making approach is proposed. Firstly, the hybrid decision matrix is standardized and then transformed into an intuitionistic fuzzy decision matrix. Then, intuitionistic fuzzy averaging operator is utilized to aggregate opinions of decision makers. Intuitionistic fuzzy entropy is utilized to obtain the entropy weights of the criteria, respectively. After intuitionistic fuzzy positive ideal solution and intuitionistic fuzzy negative ideal solution are calculated, the grey relative relational degree of alternatives is obtained and alternatives are ranked. In the end, a numerical example illustrates the validity and applicability of the proposed method.

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 novel fuzzy group decision-making approach to prioritising engineering characteristics in QFD under uncertainties

In new product development, design teams commonly need to define engineering characteristics (ECs) in a quality function deployment (QFD) planning process. Prioritising the engineering characteristics in QFD is essential to properly plan resource allocation. However, the inherent vagueness or impreciseness in QFD presents a special challenge to the effective calculation of the importance of ECs. Generally, there are two types of uncertain input in the QFD process: human perception and customer heterogeneity. Many contributions have been made on methods to prioritise ECs. However, most previous studies only address one of the two types of uncertainties that could affect the robustness of prioritising ECs. To address the two types of uncertainties simultaneously, a novel fuzzy group decision-making method that integrates a fuzzy weighted average method with a consensus ordinal ranking technique is proposed. An example is presented to illustrate the effectiveness of the proposed approach. Results of the implementation indicate that the robustness of prioritising ECs based on the proposed approach is better than that based on the method of Chen et al. (Chen, Y., Fung, R.Y.K., Tang, J.F., 2006. Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator. European Journal of Operational Research, 174 (3), 1553–1556).

A fuzzy group decision-making approach in quality function deployment

Quality function deployment (QFD) is comprised of two major group decision-making processes. One is to collect the customer requirements from a group of customers, whereas the other is to determine the relationship between customer requirements and technical measures by a cross-functional team. Generally, different and/or even subjective opinions are quite often in a group decision-making process due to the limitations of experience and impreciseness. Obviously, the importance of each customer requirement and the relationship between customer requirements and technical measures are determined by a group of people with imprecision and vagueness. Under such circumstances, a fuzzy group decision-making approach can be applied in QFD to deal with a group decision-making process when the information is imprecise and fuzzy. Moreover, an example is provided as well as the computational steps to show this fuzzy group decision-making approach can be effectively used in QFD to make decisions with imprecision and vagueness.

A fuzzy group decision making approach for bridge risk assessment

This paper proposes a fuzzy group decision making (FGDM) approach for bridge risk assessment. The FGDM approach allows decision makers (DMs) to evaluate bridge risk factors using linguistic terms such as Certain, Very High, High, Slightly High, Medium, Slightly Low, Low, Very Low or None rather than precise numerical values, allows them to express their opinions independently, and also provides two alternative algorithms to aggregate the assessments of multiple bridge risk factors, one of which offers a rapid assessment and the other one leads to an exact assessment. A case study is investigated using the FGDM approach to illustrate its applications in bridge risk assessment. It is shown that the FGDM approach offers a flexible, practical and effective way of modelling bridge risks.

Fuzzy group decision-making to multiple preference formats in quality function deployment

In a competitive and global business environment, it is certainly a distinct advantage to capture the genuine and major customer's requirements effectively. To take advantage of this, the unique way is to analyze customer's requirements systematically and to transform them into the appropriate product features properly. Quality function deployment (QFD) is a well-known planning methodology for translating customer needs (CNs) into relevant design requirements (DRs). The intent of applying QFD is to consolidate the customers’ preferences to the various phases of the product development cycle for a new product, or a new version of an existing product. However, it is more difficult to assess the performance of this process with accurate quantitative evaluation due to its uncertain nature. Moreover, people tend to give information about their personal preferences in many different ways, numerically or linguistically, depending on their background and value systems. In this study, a new fuzzy group decision-making approach is presented to fuse multiple preference styles to respond CNs in product development with QFD in a better way. The approach is illustrated with a numerical example concerning the development of the hatch door of a car.