Fuzzy theory applied in quality management of distributed manufacturing system: A literature review and classification

Lv, Yaqiong; Man, Lee Ka; Wu, Zhang

doi:10.1016/j.engappai.2010.10.008

Cited by 32 publications

(16 citation statements)

References 80 publications

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“…Yaqiong et al (2011) presented a literature review and classification of fuzzy theory application in quality management of distributed manufacturing system, and the author concluded that the fuzzy decision making in quality improvement will keep on increasing in the future according to the distributions of past articles by year of publication. Verma and Kumar (2014) proposed a new novel general approach named as vague lambda-tau to evaluating the reliability of gas turbine system based on a trapezoidal vague set with different left and right heights.…”

Section: Fuzzy Mcdmmentioning

confidence: 98%

A fuzzy TOPSIS and Rough Set based approach for mechanism analysis of product infant failure

Wang

et al. 2016

Engineering Applications of Artificial Intelligence

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Section: Fuzzy Mcdmmentioning

confidence: 98%

A fuzzy TOPSIS and Rough Set based approach for mechanism analysis of product infant failure

Wang

et al. 2016

Engineering Applications of Artificial Intelligence

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“…Aiming at the problem of the diagnosis of dimensional failures, Xie et al [14] proposed a rough set-based fault diagnosis method. Yaqiong et al [15] and Chang et al [16] introduced fuzzy theory to quality management and diagnosis, and improved enterprises' quality control levels. Focusing on the problem of monitoring the quality of the small scale resistance spot welding of titanium alloy, Wan et al [17] proposed different neural network models for weld quality prediction.…”

Section: Problem Statementsmentioning

confidence: 99%

A Novel Quality Defects Diagnosis Method for the Manufacturing Process of Large Equipment Based on Product Gene Theory

Chen

Guo

et al. 2019

Symmetry

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Focusing on the problems of quality information management and quality defects diagnosis in the manufacturing process of large equipment, a novel quality defects diagnosis method based on product gene theory and knowledge base was developed. First, a product gene model and a sectional encoding method for the quality control of the manufacturing process of large equipment were proposed. In that model, the processing surface was the minimum information granularity to meet the production characteristics of large equipment and to improve the flexibility of the product gene model. Then, a similarity evaluation rule and an optimization method of the weights of elements based on particle swarm optimization (PSO) were addressed to filter the available knowledge of product gene from the product gene knowledge base. Aiming at the characteristic of many-to-many between quality defects and quality influence factors in some cases, a fuzzy comprehensive evaluation (FCE) method was developed for the further localization of diagnosis knowledge. Finally, an experiment of bearing spacer was applied to illustrate the proposed quality diagnosis approach. In the experiment, the data from the target gene and knowledge genes were described reasonably. On this basis, available knowledge genes could be accurately filtered with the proposed similarity rule and the method of filtration, where the PSO was proved to be effective. The diagnosis results of the experiment show that multiple factors lead to the defects that were verified. Therefore, the proposed quality defects diagnosis method is an effective way for quality control.

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“…Fuzzy theory can help guide decision-making and forecast or assess the effects of decisions under approximate condition. The fuzzy technique based on fuzzy theory can be applied in several aspects to solve the uncertain problems (Yaqiong, Man, & Zhang, 2011). In reality, however, human cognition, perception and judgement involves approximate and vague reasoning, and cannot be modelled adequately by classical set theory (Liou & Chen, 2006).…”

Section: Introductionmentioning

confidence: 99%

Key performance indicators measurement in service business: a fuzzy VIKOR approach

Sofiyabadi

Kolahi

Valmohammadi

2015

Total Quality Management & Business Excellence

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This study attempts, through focusing on the concept of strategic control and by using fuzzy VIsekriterijumska Optimizacija i KOmpromisno Resenje (VIKOR) technique, to measure the importance and priority of key performance indicators (KPIs) in a successful service business of LG 'Goldiran Customer Service Company'. Based on the balanced score card, the KPIs in the four perspectives were determined and, by employing fuzzy multiple criteria decision-making (FMCDM) techniques, a systematic approach to help managers in strategic control was developed. The KPIs have been selected and used solely on the basis of LG's strategy and requirements. The survey company is among the best practice firms in the Middle East region. Therefore, the proposed hybrid systematic approach could be used as a suitable base for the development of strategic control by other companies. The obtained result from the experts' views reveal that on time and high-quality services to customer and human capital development are the most important KPIs for the implementation of strategic control in Goldiran Customer Service Company. Therefore, it could be concluded that the survey company should pay special attention to the control of the customer, and learning and growth perspectives.

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Fuzzy theory applied in quality management of distributed manufacturing system: A literature review and classification

Cited by 32 publications

References 80 publications

A fuzzy TOPSIS and Rough Set based approach for mechanism analysis of product infant failure

A fuzzy TOPSIS and Rough Set based approach for mechanism analysis of product infant failure

A Novel Quality Defects Diagnosis Method for the Manufacturing Process of Large Equipment Based on Product Gene Theory

Key performance indicators measurement in service business: a fuzzy VIKOR approach

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