An Exact Expected Value-Based Method to Prioritize Engineering Characteristics in Fuzzy Quality Function Deployment

Liu, Jing; Chen, Yizeng; Zhou, Jian; Yi, Xiajie

doi:10.1007/s40815-015-0118-0

Cited by 16 publications

(6 citation statements)

References 38 publications

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“…From existing research, ECs usually refer to the key performances, dimensions, materials or other properties of a product [21][22][23]. For PSS, ECs also include service characteristics, such as service response time, technical support level, etc.…”

Section: Engineering Characteristics Designmentioning

confidence: 99%

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Product-service system engineering characteristics design for life cycle cost based on constraint satisfaction problem and Bayesian network

Wang

Ding

et al. 2022

Advanced Engineering Informatics

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Section: Engineering Characteristics Designmentioning

confidence: 99%

“…Assembly cost (18,20), (20,22), (22,24) Fault detection cost (432, 480), (480, 528), (528, 576) It can be seen from Fig. 9 that after decomposing the manufacturing cost and maintenance cost, the urchased parts cost node has the largest number of parent nodes in BN, which has 8 parent nodes, and there are 384 parent node status combinations.…”

Section: Cost Itemsmentioning

confidence: 99%

Product-service system engineering characteristics design for life cycle cost based on constraint satisfaction problem and Bayesian network

Wang

Ding

et al. 2022

Advanced Engineering Informatics

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“…Wong et al suggested a fuzzy approach to determine the aggregated importance weights of ECs through fuzzy expert systems in QFD [29]. Liu et al put forward an approach to get the exact expected values and rank the importance of ECs in fuzzy QFD environments [30]. In order to overcome the subjectivity of fuzzy membership function selection, some proposed a fuzzy axiomatic design method based on rough set to guide the design decision-making process with inaccurate information [31].…”

Section: Fuzzy Sets Eory and Rough Set Eorymentioning

confidence: 99%

Multiobjective Decision-Making for Technical Characteristics Selection in a House of Quality

Xie

Qin

Jiang

2020

Mathematical Problems in Engineering

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Technical characteristics are an important influencing factor in fuzzy front-end of product innovation design and also an important output of the house of quality. The analysis and decision-making of technical characteristics are conducive to the rational allocation of resources according to their own advantages and the design of products in line with market demand. In view of the multiple objective constraints and internal fuzziness of technical property selection in the house of quality, the traditional methods have great limitations on the decision-making of technical property. Therefore, this paper proposes a multiobjective decision-making method for technical property selection in the house of quality, which aims to calculate and modify the basic importance of technical property. Based on the rough set algorithm, the basic importance of technical characteristics is determined by using the correlation matrix between consumer demand and technical characteristics, and the relative weight of multiple objectives is determined by “least squares.” On this basis, the multiobjective decision-making model of technical characteristic selection is established and calculated. At last, an example of mobile phone product design was used to verify the practicability and effectiveness of the method.

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“…Fuzzy set theory was widely employed to calculate the rankings of ECs under the circumstance of vagueness and impreciseness. Fuzzy multiple objective programming [7], fuzzy goal programming [8], fuzzy relationship and correlations [9], and expected value-based method [10] are proposed to prioritize ECs. In addition, Geng et al [11] integrated the analytic network process to QFD to reflect the initial importance weights of ECs.…”

Section: Introductionmentioning

confidence: 99%

“…All the evaluation matrices uniformed are aggregated with 2TLWGBM operator in virtue of Equation(10) intoR ij (i = 1, 2, • • • , n ; j = 1, 2, • • • , s).Furthermore, the new matrix represents ultimate relationship matrix between CRs and ECs in essence.…”

mentioning

confidence: 99%

A Multi-Granularity 2-Tuple QFD Method and Application to Emergency Routes Evaluation

Mei

Liang

2018

Symmetry

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Quality function deployment (QFD) is an effective approach to satisfy the customer requirements (CRs). Furthermore, accurately prioritizing the engineering characteristics (ECs) as the core of QFD is considered as a group decision making (GDM) problem. In order to availably deal with various preferences and the vague information of different experts on a QFD team, multi-granularity 2-tuple linguistic representation is applied to elucidate the relationship and correlation between CRs and ECs without loss of information. In addition, the importance of CRs is determined using the best worst method (BWM), which is more applicable and has good consistency. Furthermore, we propose considering the relationship matrix and correlation matrix method to prioritize ECs. Finally, an example about evaluating emergency routes of metro station is proposed to illustrate the validity of the proposed methodology.

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An Exact Expected Value-Based Method to Prioritize Engineering Characteristics in Fuzzy Quality Function Deployment

Cited by 16 publications

References 38 publications

Product-service system engineering characteristics design for life cycle cost based on constraint satisfaction problem and Bayesian network

Product-service system engineering characteristics design for life cycle cost based on constraint satisfaction problem and Bayesian network

Multiobjective Decision-Making for Technical Characteristics Selection in a House of Quality

A Multi-Granularity 2-Tuple QFD Method and Application to Emergency Routes Evaluation

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