Journal of Automation and Control
ISSN (Print): 2372-3033 ISSN (Online): 2372-3041 Website: Editor-in-chief: Santosh Nanda
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Journal of Automation and Control. 2015, 3(3), 83-88
DOI: 10.12691/automation-3-3-9
Open AccessArticle

Improving Manufacturing Processes Using Simulation Methods

Sławomir Kłos1, Justyna Patalas-Maliszewska1 and Peter Trebuna2,

1University of Zielona Góra, Faculty of Mechanical Engineering, Licealna 9, 65-417 Zielona Góra, Poland

2Technical University of Kosice, Faculty of Mechanical Engineering

Pub. Date: December 15, 2015

Cite this paper:
Sławomir Kłos, Justyna Patalas-Maliszewska and Peter Trebuna. Improving Manufacturing Processes Using Simulation Methods. Journal of Automation and Control. 2015; 3(3):83-88. doi: 10.12691/automation-3-3-9


Computer simulation is a very important method for studying the efficiency of manufacturing systems. This paper presents the results of simulation research about how buffer space allocated in a flow line and operation times influence the throughput of a manufacturing system. The production line in the study consists of four stages and is based on a real machining manufacturing system of a small production enterprise. Using Tecnomatix Plant Simulation software, a simulation model of the system was created and set of experiments was planned. Simulation experiments were prepared for different capacities of intermediate buffers located between manufacturing resources (CNC machines) and operation times as input parameters, and the throughput per hour and average life span of products as the output parameter. On the basis of the experiments, the impact of the allocation of intermediate buffer capacities on production efficiency is analysed.

computer simulation production line buffer allocation throughput

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[1]  A. Seleima, A. Azaba, T. AlGeddawy, Simulation Methods for Changeable Manufacturing, 45th CIRP Conference on Manufacturing Systems 2012, Procedia CIRP 3, 2012, pp. 179-184.
[2]  D. R. Staley, D. S. Kim, Experimental results for the allocation of buffers in closed serial production lines, International Journal of Production Economics, Vol. 137 (2012), pp. 284-291
[3]  H. A. Vergara, D. S. Kim, A new method for the placement of buffers in serial production lines, International Journal of Production Research, 2009, 47(16), pp. 4437-4456.
[4]  H. Yamashita, T. Altiok, Buffer capacity allocation for a desired throughput in production lines. IIE Transactions 1998; Vol. 30, pp. 883-91.
[5]  G. Gurkan, Simulation optimization of buffer allocations in production lines with unreliable machines. Annals of Operations Research 2000, Vol. 93, pp. 177-216.
[6]  C. Shi, S.B. Gershwin, An efficient buffer design algorithm for production line profit maximization. International Journal of Production Economics 2009 Vol. 122, pp. 725-40.
[7]  J.A. Qudeiri, H. Yamamoto, R. Ramli, A. Jamalim, Genetic algorithm for buffer size and work station capacity in serial-parallel production lines, Artificial Life and Robotics 2008, Vol.12, pp. 102-6.
[8]  N. Nahas, D. Ait-Kadi, M. Nourelfath, Selecting machines and buffers in unreliable series-parallel production lines. International Journal of Produc- tion Research 2009;Vol. 47(14), pp. 3741-74. [32].
[9]  M. Nourelfath, N. Nahas, D. Ait-Kadi, Optimal design of series production lines with unreliable machines and finite buffers. Journal of Quality in Mainte- nance Engineering 2005, Vol. 11(2), pp. 121-38.
[10]  N. O. Fernandes, S. Carmo-Silva, Order release in a workload controlled flow-shop with sequence-dependent set-up times, International Journal of Production Research 2011, Vol. 49(8), pp. 2443-54.
[11]  A. Matta, Simulation optimization with mathematical programming repre-sentation of discrete event systems. In: Proceedings of the 2008 winter simulation conference, 2008, pp. 1393-400.
[12]  M.G. Huang, P.L. Chang, Y.C. Chou, Buffer allocation in flow-shop-type production systems with general arrival and service patterns. Computers & Operations Research 2002, Vol.29 pp. 103-21.
[13]  MacGregor Smith J, Cruz FRB. The buffer allocation problem for general finite buffer queuing networks. IIE Transactions, 2005, Vol. 37(4), pp. 343-65.
[14]  L. Demir, S.Tunalı, D. T. Eliiyi, A. Løkketangen, Two approaches for solving the buffer allocation problem in unreliable production lines, Computers & Operations Research, Vol. 40, 2013, pp. 2556-2563.
[15]  Tecnomatix Plant Simulation version 11.0.0 on-line documentation.
[16]  Trebuňa, P., Straka, M., Rosová, A., Malindžáková, M.: Petri nets as a tool for production streamlining in plastics processing, Przemysl chemiczny, 2015, 94, No. 9, 1000-1003.
[17]  Malindžáková, M., Straka, M., Rosová, A., Kanuchová, M., Trebuňa, P.: Modeling the process for incineration of municipal waste, Przemysl chemiczny, 2015, 94, No. 8, 1000-1004.
[18]  Straka, M., Trebuňa, P., Straková, D., Kliment, M.: Computer Simulation as means of urban traffic elements design, Theoretical and Empirical Researches in Urban Management, Vol. 10, Issue 4, 2015, p. 40-53.
[19]  Kollár, P., Nikitin, Y., Straka, M.: The determination of the shelf mass in the universal shelving stacker by measuring the frequency converter torque generating current of the main drive, Manufacturing technology, Vol. 15, No. 3, p. 363-366.
[20]  Malindžák, D., Straka, M., Helo, P., Takala, J.: The methodology for the logistics system simulation model design, 2010, Metalurgija, Vol. 49, No. 4 (2010), p. 348-352.
[21]  Straka, M.: System of distribution logistics of enterprise Alfa, a.s., Acta Montanistica Slovaca, Vol. 15, Special issue 1, 2010, p. 34-43.