Concept design evaluation by using Z-axiomatic design

Aydoğan, Sena; Günay, Elif Elçin; Akay, Diyar; Kremer, Gül E. Okudan

doi:10.1016/j.compind.2020.103278

Cited by 45 publications

(19 citation statements)

References 43 publications

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“…Additionally, the proposed model has the capacity to consider the judgmental reliability of different individuals. In general, the assessment process is usually closer to the real perception of decision‐makers under considering judgmental reliability 61 . Thus, based on the above analysis, it can be judged that the developed Z‐fuzzy cloud model can reliably and rationally depict the experts’ real cognitions.…”

Section: Case Studymentioning

confidence: 98%

An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

Xiao

Huang

Zhang

2022

Quality & Reliability Eng

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As a systematic risk assessment instrument, failure mode, and effects analysis (FMEA) has been extensively applied for enhancing systems’ quality. However, the classical FMEA technique has limitations in many application scenarios. Although fuzzy logic‐based risk assessment techniques are introduced to enhance conventional FMEA, they still have limitations, for example, needing prior presumptions, neglecting decision‐makers’ bounded rationality, and lacking the capability to reflect the judgmental reliability and randomness of experts’ evaluations simultaneously. To cover these limitations, an extended FMEA method is presented in this paper by using Z‐fuzzy clouds and generalized TODIM (An acronym in Portuguese of interactive and multiple attribute decision‐making). First, a novel concept of Z‐fuzzy clouds is proposed to handle various factors of uncertainties by combining the advantages of the Z‐number theory, the interval‐valued intuitionistic fuzzy sets theory, and the cloud model theory, and the basic operations and Minkowski‐type distance measure of Z‐fuzzy clouds are also defined and discussed. Furthermore, more risk factors are added into the conventional three risk factors and to further build a multiple dimension risk factor system for better evaluating failures. Additionally, the experts’ bounded rationality and the risk factors’ interrelationships are considered by two developed weighting models. Finally, a real study case, sensitivity analysis, and several comparisons are performed to validate the effectiveness of the proposed model. Results demonstrate that the proposed model is more rational compared with the previous several methods.

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Section: Case Studymentioning

confidence: 98%

An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

Xiao

Huang

Zhang

2022

Quality & Reliability Eng

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“…Tel: +86 21 34206552. Fax: +86 21 34206313 2 [12,13], grey number [14,15], rough number [1,6,7] and other integrated numbers [3,16] to transform these linguistic terms to some kinds of quantified crisp, interval or triple values, based on either membership functions or interval boundaries [17]. However, some hidden information has been ignored in old representation methods, for example, the confidence attitude of customer, i.e., the reliability degree of customer's preference judgement.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, the confidence attitude of this customer is unsure, and the reliability degree of his preference judgement is relatively low, in other words, he might probably change his decision. Hence, the problem may arise while assigning a customer's subjective preference to a kind of crisp number since the role of reliability of preference is not incorporated in this transformation [17]. For the better uncertainty formalization, Zadeh [28] proposed a new concept of Z-number.…”

Section: Introductionmentioning

confidence: 99%

Z-preference-based multi-criteria decision-making for design concept evaluation highlighting customer confidence attitude

2023

Preprint

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The aim of customer-oriented design concept evaluation (CDCE) is to select the best product design solution from the perspective of customer. Traditionally, most of CDCE methods mainly focus on the customer preference judgement but ignore the confidence attitude of customer, namely, the reliability of preference. However, the customer’s uncertain attitude means he is unsure about his decision and could probably change his mind. With the help of Z-number, more complete customer preference information is recorded (Z-preference). The main contribution of this paper is to propose a new Z-preference-based multi-criteria decision-making (MCDM) for CDCE method that retains the confidence coefficient α in evaluation value (Z-CDCE-α) to highlight the role of confidence attitude for CDCE, rather than simply translating Z-preference into a regular fuzzy preference value. By integrating with multiple information such as preference value, confidence coefficient α and the importance rating of design attribute, a novel ideal solution definition (ISD) strategy is put forward. For the re-defined ideal solutions, the distances of alternative to ideal solutions are deduced to get the priority degree δ to sort alternatives. According to the proposed ISD strategy of Z-CDCE-α, the best concept is that its important attribute values are preferred by customers with higher certainty or least preferred by customers with lower certainty, and the customers’ preferences and confidence attitudes for its less important attribute values are opposite. A case study and two comparison experiments are carried out to validate the reasonability and feasibility of Z-CDCE-α for CDCE by comparing with different evaluation values, ISD rules and MCDM models.

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“…Since Zadeh, 15 Z-number has been applied in various decision-making problems. [16][17][18][19][20][21][22] However, most of these applications of Z-number suffer from information loss and improper information utilization issues. To circumvent these issues, Garg et al 23 introduced the concept of granulized Z-number (gZN).…”

Section: Introductionmentioning

confidence: 99%

Granulized Z‐OWA aggregation operator and its application in fuzzy risk assessment

Garg

Maiti

Kumar

2021

Int J of Intelligent Sys

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The concept of granulized Z-numbers improves information utilization and manages the degree of uncertainty in decision-making. In this paper, a novel scoring method, namely, ordered weighted averagingbased expected granulized Z-number, a new aggregation operator, named, granulized Z-number-based ordered weighted averaging operator, and a novel fuzzy risk assessment scheme is proposed. The proposed scoring method is used to order the granulized Z-numbers and takes care of the possibilistic as well as probabilistic information contained in the granulized Z-numbers. The maximum entropy principle-based nonlinear optimization model is formulated to capture the aforesaid probabilistic information during the scoring process. Based on the proposed scoring and ordering method, the granulized Z-number-based ordered weighted averaging operator is developed, which judiciously integrates the benefits of the granulized Z-numbers and the ordered weighted averaging operator to provide an improved aggregation of the decision-making information collected from multiple sources (experts). The required properties are also proved. Finally, the novel fuzzy risk assessment scheme is developed using the granulized Z-numberbased ordered weighted averaging operator, the average linkage-based ordered weighted averaged similarity measure between two granulized Z-numbers, and the

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Concept design evaluation by using Z-axiomatic design

Cited by 45 publications

References 43 publications

An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

Z-preference-based multi-criteria decision-making for design concept evaluation highlighting customer confidence attitude

Granulized Z‐OWA aggregation operator and its application in fuzzy risk assessment

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