Commenced in January 2007
Paper Count: 30458
Optimizing and Evaluating Performance Quality Control of the Production Process of Disposable Essentials Using Approach Vague Goal Programming
Abstract: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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1315639Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 572
 Kaya I., Kahraman C. (2011). Process capability analyses based on fuzzy measurements and fuzzy control charts. Expert Systems with Application7s, Vol. 38, pp. 3172–3184.
 Hung Shu. M, Chung Wu. H. (2011). Fuzzy X and R control charts: Fuzzy dominance approach. Computers & Industrial Engineering, Vol. 61, pp. 676–685.
 Erginel, N., Sentürk, S., Kahraman, C., & Kaya. I. (2011). Evaluating the Packing Process in Food Industry Using Fuzzy and
[Stilde] Control Charts. International Journal of Computational Intelligence Systems ,4 (4), 509-520.
 Lin H-C. Su C-T. Wang C-C. Chang B-H. and Juang R-C. "Parameter optimization of continuous sputtering process based on Taguchi methods, neural networks, desirability function, and genetic algorithms," Expert Systems with Applications, Vol. 39(17): 2012, pp. 12918–12925.
 AL-Refaie A., T. Chen, R. Al-Athamneh, H. C. Wu, "Fuzzy neural network approach to optimizing process performance by using multiple responses," Journal of Ambient Intelligence and Humanized Computing, Vol. 7 (6), 2016, pp. 801-816.
 Al-Refaie A. and A. Diabat, "Optimizing convexity defect in a tile industry using fuzzy goal programming," Measurement, Journal of the International Measurement Confederation, Vol. 46 (8), 2013, pp. 2807-2815.
 Al-Refaie A., "Optimizing multiple quality responses in the Taguchi method using fuzzy goal programming: modeling and applications," International Journal of Intelligent Systems, Vol. 30(6), 2015a, pp. 651–675.
 V. A. Gonzalez-Lopez, R. Gholizadeh, A. M. Shirazi: Optimization of queuing theory based on vague environment, International Journal of Fuzzy System Applications, Volume 5 Issue 1, 1-26, 2016.
 A. B. Ubale, S. L. Sananse. “Fuzzy Regression Model and Its Application: A Review”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 4, Issue 11, November 2015.
 Jones D. F, Tamiz M. (2010) Practical Goal Programming, Springer Books.
 Abbas Al-Refaie,” Optimizing Performance of Tablet's Direct Compression Process Using Fuzzy Goal Programming”, International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:11, No:5, 2017.
 A. R. Arabpour and M. Tata (2008). Estimating the Parameters Of A Fuzzy Linear Regression Model. Iranian Journal of Fuzzy Systems Vol. 5, No. 2, (2008) pp. 1-19.