A group decision model based on quality function deployment and hesitant fuzzy for selecting supply chain sustainability metrics

Osiro, Lauro; Lima, Francisco Rodrigues; Carpinetti, Luiz César Ribeiro

doi:10.1016/j.jclepro.2018.02.197

Cited by 73 publications

(45 citation statements)

References 78 publications

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“…Wu et al [3] provided a hybrid QFD model with decision-making trial and evaluation laboratory (DEMATEL) technique and VIKOR method within hesitant fuzzy environment. In addition, the fuzzy QFD-MCDM frameworks were applied to various fields, such as supply chain management strategies formulation problems [2,33], risk management of hazardous material transportation process [34], and green supplier selection problems [6].…”

Section: Literature Reviewmentioning

confidence: 99%

“…As one of the most famous design quality control technologies, quality function deployment (QFD) [1] is an effective and efficient way to convert customer requirements into production demand [2]. This facilitates research and development (R&D) companies to design and manufacture customer-driven products.…”

Section: Introductionmentioning

confidence: 99%

“…However, although the usefulness of QFD models has improved a little, three deficiencies still exist in these studies which are formulated below: (1) the evaluation information of relationships between CRs and ECs was given with crisp values or various kinds of fuzzy numbers [8]. Nevertheless, in most actual circumstances, the information provided by experts may be unreliable or a bit reliable because of their lacks of professional knowledge and limited time to make decisions; (2) as a rule, the ranking process of ECs requires several QFD experts to participate in the consultations. However, the weights of expert team members (TMs) were provided directly without any discussions [2], which was not scientific and rational.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, in most actual circumstances, the information provided by experts may be unreliable or a bit reliable because of their lacks of professional knowledge and limited time to make decisions; (2) as a rule, the ranking process of ECs requires several QFD experts to participate in the consultations. However, the weights of expert team members (TMs) were provided directly without any discussions [2], which was not scientific and rational. What's more, it was inconsistent with actuality to determine the experts' weights by an either subjective or objective method [9]; (3) the ranking results may be inaccurate in real-life decision-making processes when only subjective or objective weighting method employed [10,11].…”

Section: Introductionmentioning

confidence: 99%

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New Framework for Quality Function Deployment Using Linguistic Z-Numbers

Song

Wang

2020

Mathematics

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Quality function deployment (QFD) is a useful design quality control tool in service enterprises and manufacturing enterprises. However, there are several issues in extant QFD frameworks, that is, in three aspects: description of evaluation information, weight determination of expert team members (TMs), and weight identification of customer requirements (CRs). In order to address these issues, a novel QFD framework is first proposed utilizing linguistic Z-numbers (LZNs) with integrated subjective and objective weights of TMs and CRs. The LZNs can represent uncertain information and the reliability of information in a specific way while the fuzzy numbers cannot. Moreover, the order relation analysis (G1) method and improved maximum consensus (MC) method are developed to get the subjective and objective weights of TMs, respectively. Further, the step-wise weight assessment ratio analysis (SWARA) method and statistical distance (SD) method are studied to acquire combined weights of CRs. Next, the proposed QFD framework is applied to a case of logistics service provider, which illustrates the availability and utility of the framework. Then, a sensitivity analysis is conducted to prove the reliability of the framework. Finally, two comparative analyses are performed to declare the advantages of the framework. Results indicate the proposed QFD framework is better than existing models.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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New Framework for Quality Function Deployment Using Linguistic Z-Numbers

Song

Wang

2020

Mathematics

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“…A large value of consensus indicates a reliable result obtained by the decision group, which has been widely investigated by researchers (Palomares, Estrella, Martínez, & Herrera, ). Considerable GDM methods for QFD usually drive a single group preference from numbers of specific individual preferences based on various aggregation operators (Beheshtinia & Farzaneh Azad, ; Lin, Huang, & Yeh, ; Osiro, Lima‐Junior, & Carpinetti, ; Wang, Fung, Li, & Pu, ). However, these GDM methods commonly overlook the group consensus process.…”

Section: Introductionmentioning

confidence: 99%

Group multigranular linguistic QFD for prioritizing service designs with combined weighting method

et al. 2019

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Qualification for the importance degree of service designs (SDs) is an essential stage in promoting accommodation performances to satisfy customers and gain market shares. Although various methods have been applied to derive the priorities of SDs, they do not effectively deal with the vagueness of information and the heterogeneity of decision makers (DMs). This study presents an improved quality function deployment (QFD) for prioritizing SDs. Multigranular unbalanced linguistic term sets are used to capture evaluators' ratings to cope with vague information. The multigranular linguistic information is unified by using a novel uniform method. This kind of information then is represented as linguistic distribution assessments using the concept of random preferences based on possibility interpretations of weight information. A consensus‐based mathematical programming model is established to determine the weights of DMs. Moreover, a combined structure that combines maximizing deviation and best worst methods is used to derive basic weights of SDs. Finally, an illustrative example of island accommodation management in Weizhou Island is provided to demonstrate the applicability and advantages of the proposed QFD framework.

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Risk assessment on multimodal transport network based on quality function deployment

Guo

et al. 2020

Int J Intell Syst

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As a necessary work before risk prevention and control, risk assessment has become increasingly important in multimodal transport networks. On the basis of the idea of quality function deployment (QFD), this paper combines fuzzy G1 method, two-tuple linguistic representation model, and dynamic intuitionistic fuzzy multi-attribute evaluation method to propose a new method for risk assessment. QFD is innovatively used to create an overall framework, which includes the basic importance rating of risk factors, the correlation between risk factors and network failures, the importance rating of network failures, and the comprehensive importance rating of risk factors. The comprehensive importance rating is regarded as an indicator to verify, improve, and modify the basic importance rating, which can make up for the disadvantage of the existing risk assessment methods in proving the credibility of the results. Its result is the key to judging the degree of influence of risk factors on network operation. This paper takes the multimodal transport network in the Sichuan-Tibet region of China as an example for analysis. The results are analyzed and discussed to prove the validity and advancement of the proposed method.

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A group decision model based on quality function deployment and hesitant fuzzy for selecting supply chain sustainability metrics

Cited by 73 publications

References 78 publications

New Framework for Quality Function Deployment Using Linguistic Z-Numbers

New Framework for Quality Function Deployment Using Linguistic Z-Numbers

Group multigranular linguistic QFD for prioritizing service designs with combined weighting method

Risk assessment on multimodal transport network based on quality function deployment

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