Mathematical Model of Productivity with Reliability and Losses Parameters for Serial Structure Linear Production Automated Flow Line: A Simulation Analysis

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Industrial automated production flow line focus on productivity assessment and analysis since it present an important indicator of company profit and performance. Serial structure of production automated flow line widely implement in automated production sector for mass production. High accuracy yield of mathematical model of productivity is important for forecast work to increase the profits of company. This paper presents application of Witness software simulation for mathematical model of productivity with different reliability level of workstations and mechanisms, bottleneck machining time, and defected parts parameters for serial structure automated to validate this mathematical model which produce higher accuracy forecast result compare to current mathematical model of productivity which not consider few of productivity losses parameters. Validation through software simulation for mathematical model is important since there is lack of simulation for this model in current research for mathematical model. The simulation result for model is show higher accuracy which near to actual simulation productivity and the current mathematical model is replace by mathematical model with different reliability level for productivity forecast in academic and industries due to accuracy reason.

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Productivity, Reliability, Serial Structure, Production Flow Line, WITNESS Simulation

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C. Dym, Principles of Mathematical Modeling, 2nd Editio. Elsevier Academic Press, 2004.

J. Herskovits, P. Mappa, E. Goulart, and C. M. Mota Soares, “Mathematical programming models and algorithms for engineering design optimization,” Comput. Methods Appl. Mech. Eng., vol. 194, no. 30–33, pp. 3244–3268, Aug. 2005.

B. Oyediran and A. Abraham, “Mathematical Modeling: An Application To Corrossion In A Petroleum Industry,” in NMC Proceeding Workshop on Environment, 2005.

a C. van Leeuwen, S. H. Ong, a Vissink, D. W. Grijpma, and R. R. M. Bos, “Reconstruction of orbital wall defects: recommendations based on a mathematical model.,” Exp. Eye Res., vol. 97, no. 1, pp. 10–8, Apr. 2012.

P. Sheoran, O. P. Sheoran, and V. Sardana, “Modeling Sunflower Productivity and Profitability in Relation to Adequate and Limited Sulphur Availability under Semiarid Irrigated Conditions,” Int. J. Agron., vol. 2013, pp. 1–4, 2013.

Y. Zhou, X. Zhang, and J. Deng, “A mathematical optimization model of insulation layer’s parameters in seasonally frozen tunnel engineering,” Cold Reg. Sci. Technol., vol. 101, pp. 73–80, May 2014.

M. Niss, “Applications and Modelling in the Mathematics Curriculum – State and Trends,” Int. J. Math. Educ. Sci. Technol., no. 18, pp. 487–505, 1987.

Men-la-yakhaf, S., Gueraoui, K., Maaouni, A., Driouich, M., Numerical and mathematical modeling of reactive mass transfer and heat storage installations of argan waste, (2014) International Review of Mechanical Engineering (IREME), 8 (1), pp. 236-240.

R. G. Sargent, “Verification and Validation of Simulation Models,” in Winter Simulation Conference, 2007, pp. 124–137.

Tan Chan Sin, Ryspek Usubamatov, M. Fidzwan, B. A. Hamzas, L. K. Wai, and T. K. Yao, “Investigate of Potential Parameters to Improve Mathematical Model of Productivity for Automated Line With Average Level of Reliability Using DMAIC Methodology,” in International Postgraduate Conference Engineering & Management, 2014.

C. D. Senanayake and V. Subramaniam, “Estimating customer service levels in automated multiple part-type production lines: An analytical method,” Comput. Ind. Eng., vol. 64, no. 1, pp. 109–121, Jan. 2013.

P. Semanco and D. Marton, “Simulation Tools Evaluation using Theoretical Manufacturing Model,” Acta Polytech. Hungarica, vol. 10, no. 2, pp. 193–204, 2013.

W. S. Smutkupt U., “Plant Layout Design with Simulation,” in International MultiConference of Engineers and Computer Scientists Vol II, 2009.

R. Usubamatov, Tan Chan Sin, Mohd Fidzwan B. Md. Amin Hamzas, “Productivity Theory for Industrial Automated Lines,” in Proceedings of the 2013 Mechanical Engineering Congress & Exhibition ASME, 2013, pp. 1–11.

R. Usubamatov, K. a. Ismail, and J. M. Sah, “Mathematical models for productivity and availability of automated lines,” Int. J. Adv. Manuf. Technol., vol. 66, no. 1–4, pp. 59–69, Jun. 2012.

Wu Guang, Massimo Baraldo, Mario Furlanut, Calculating percentage prediction error: A user's note, Pharmacological Research, Volume 32, Issue 4, October 1995, Pages 241–248.

Wang Ting, Cai Lin-qin, Fu Yao, and Zhu Tingcheng, “A Wavelet-Based Robust Relevance Vector Machine Based on Sensor Data Scheduling Control for Modeling Mine Gas Gushing Forecasting on Virtual Environment,” Mathematical Problems in Engineering, vol. 2013, Article ID 579693, 4 pages, 2013. doi:10.1155/2013/579693.


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