Fuzzy neural network approach to optimizing process performance by using multiple responses

Al–Refaie, Abbas; Chen, Tin-Chih; Al-Athamneh, Raed; Wu, Hsin Chieh

doi:10.1007/s12652-015-0340-5

Cited by 26 publications

(16 citation statements)

References 40 publications

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“…Several approaches have been proposed to optimize process performance with multiple responses [4]. Therefore, multiple formularizations of goal programming (GP) models were shown for deciding the fuzzy GP (FGP) problems obtaining into account the decision maker's (DM's) priority [5]. An effective FGP technique is the weighted additive model, which considers all shapes of membership functions, with the objective to minimize the weighted deviations from the imprecise fuzzy values for all quality responses and process factors [6].…”

Section: Introductionmentioning

confidence: 99%

Optimizing and evaluating performance quality control of the production process of disposable essentials using approach vague goal programming

Gholizadeh

Tajdin

2019

Journal of Industrial and Production Engineering

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To have effective production planning, it is necessary to control the quality of processes. This paper aims at improving the performance of the disposable essentials process using statistical quality control and goal programming in a vague environment. That is expressed uncertainty because there is always a measurement error in the real world. Therefore, in this study, the conditions are examined in a vague environment that is a distance-based environment. The disposable essentials process in Kach Company was studied. Statistical control tools were used to characterize the existing process for four factor responses including the average of disposable glasses' weights, heights, crater diameters, and volumes. Goal programming was then utilized to find the combination of optimal factors setting in a vague environment which is measured to apply uncertainty of the initial information when some of the parameters of the models are vague; also, the fuzzy regression model is used to predict the responses of the four described factors. Optimization results show that the process capability index values for disposable glasses' average of weights, heights, crater diameters and volumes were improved. Such increasing the quality of the products and reducing the waste, which will reduce the cost of the finished product, and ultimately will bring customer satisfaction, and this satisfaction, will mean increased sales.

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Section: Introductionmentioning

confidence: 99%

Optimizing and evaluating performance quality control of the production process of disposable essentials using approach vague goal programming

Gholizadeh

Tajdin

2019

Journal of Industrial and Production Engineering

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“…In today's competitive markets, customers are increasingly interested in multiple quality responses of products (Al-Refaie, 2013b,c;Çakıroğlu and Acır, 2013). Several methods have, therefore, been developed to optimize process performance for multiple responses of a product, including data envelopment analysis (Al-Refaie et al, 2009), fuzzy regression (Al-Refaie, 2013a), artificial neural networks (Al-Refaie, et al, 2016), fuzzy methods (Al-Refaie, 2015a;Bose et al, 2013), utility concepts (Sivasakthivel et al, 2014), and goal programming (Al-Refaie et al, 2014;Al-Refaie, 2015).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic and Back-Propagation Neural Networks for Optimal Performance

Al–Refaie¹,

Fouad²,

Athamneh³

2019

MAS

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Thus far, the Taguchi technique is found only efficient in obtaining the combination of optimal factor settings when a single product/process response is considered. In today’s dynamic environment, customers are interested in multiple quality responses. This research, therefore, utilizes fuzzy logic and backward-propagation neural networks (BPNNs) to optimize process performance for products of multiple quality responses. In this research, quality characteristics are transformed to signal to noise (S/N) ratios, which are then used as inputs to a fuzzy model to obtain a single common output measure (COM). Next, BPNNs are employed to obtain full-factorial experimental data. Finally, the combination of factor levels that maximizes the average COM value is chosen as the optimal combination. Three case studies are provided for illustration; in all of which the proposed approach provided the largest total anticipated improvement. This indicates that the proposed approach is more efficient than Taguchi-fuzzy, grey-Taguchi, and Taguchi-utility methods. In conclusion, the fuzzy-BPNN approach may greatly assist process/product engineers in optimizing performance with multiple responses in a wide range of business applications.

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“…In reality, products are manufactured with multiple built in quality characteristics of main customer interest (Al-Refaie et al, 2009;Al-Refaie, 2012). Several optimization techniques were employed in previous studies to optimize process performance (Al-Refaie, 2015;Al-Refaie et al, 2016;Al-Refaie, 2017). Among them, the grey relational analysis based on the grey system theory (Deng, 1982;Tsao, 2009) can be utilized for solving complicated interrelationships among multiple quality responses (Deng, 1989;Al-Refaie, 2010).…”

Section: Introductionmentioning

confidence: 99%

Optimal Performance of Caps’ Pressing Process Using Taguchi-Grey Method

Al–Refaie¹,

Fouad²,

Bata³

2019

MAS

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This paper aims at improving the performance of caps’ pressing process using Taguchi-grey relational analysis. Five main quality characteristics were considered, involving cap’s height, inner diameter, outer diameter, angle, and plastic weight. The individual and moving range control charts were constructed for each quality characteristic. Then, process capability analysis was then carried out to assess process performance at initial process factor settings, at which the process was found incapable in producing conforming caps for some quality characteristics. Thus, the Taguchi’s designed experiments were employed to provide experimental layout followed by the grey relational analysis to determine the combination of optimal factor settings. Results showed significant in almost all the five quality responses and thereby resulted in huge production and quality cost savings. 

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Fuzzy neural network approach to optimizing process performance by using multiple responses

Cited by 26 publications

References 40 publications

Optimizing and evaluating performance quality control of the production process of disposable essentials using approach vague goal programming

Optimizing and evaluating performance quality control of the production process of disposable essentials using approach vague goal programming

Fuzzy Logic and Back-Propagation Neural Networks for Optimal Performance

Optimal Performance of Caps’ Pressing Process Using Taguchi-Grey Method

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