The Application of Queuing Theory in Multi-Stage Production Lines
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32797
The Application of Queuing Theory in Multi-Stage Production Lines

Authors: Hani Shafeek, Muhammed Marsudi

Abstract:

The purpose of this work is examining the multiproduct multi-stage in a battery production line. To improve the performances of an assembly production line by determine the efficiency of each workstation. Data collected from every workstation. The data are throughput rate, number of operator, and number of parts that arrive and leaves during part processing. Data for the number of parts that arrives and leaves are collected at least at the amount of ten samples to make the data is possible to be analyzed by Chi-Squared Goodness Test and queuing theory. Measures of this model served as the comparison with the standard data available in the company. Validation of the task time value resulted by comparing it with the task time value based on the company database. Some performance factors for the multi-product multi-stage in a battery production line in this work are shown. The efficiency in each workstation was also shown. Total production time to produce each part can be determined by adding the total task time in each workstation. To reduce the queuing time and increase the efficiency based on the analysis any probably improvement should be done. One probably action is by increasing the number of operators how manually operate this workstation.

Keywords: Production line, manufacturing, performance measurement, queuing theory.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1097491

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3086

References:


[1] Hideaki Takagi, “Queuing Analysis,” 1st Edition, Vol. 2, Elsevier Science Publisher, Netherland, 1993.
[2] Syski, R“Introduction to Congestion Theory in Telephone Systems,” 2nd Edition, Elsevier Science Publisher, Amsterdam, 1986.
[3] Mital, K.M., “Queuing analysis for outpatient and inpatient services: a case study,” Management Decision, Vol. 48 No. 3, pp. 419-439, 2010.
[4] Cooper, R.B., “Queuing Theory: Chapter 10 in Stochastic Models,” Heyman and M.J. Sobel Edition, North Holland, 1990.
[5] Ullah, H., “Petri net versus queuing theory for evaluation of FMS,” Assembly Automation, Vol.31, No. 1, pp. 29–37, 2011.
[6] Tsarouhas, P.A., “A comparative study of performance evaluation based on field failure data for food production lines,” Journal of Quality in Maintenance Engineering, Vol. 17 No. 1, pp. 26-39, 2011.
[7] McGuire, A.M., “A framework for evaluating the customer wait experience,” Journal of Service Management, Vol. 21 No. 3, pp. 269- 290, 2010.
[8] Caputo, A.C. and Pelagagge, P.M. “A methodology for selecting assembly systems feeding policy,” Industrial Management & Data Systems, Vol. 111 No. 1, pp. 84-112, 2011.
[9] Mehmood, R. and Lu, J.A. “Computational Markovian analysis of large systems,” Journal of Manufacturing Technology Management, Vol. 22 No. 6, pp. 804-817, 2011.
[10] Diaz, D.Z. “New ways of thinking about nurse scheduling,” Journal of Advances in Management Research, Vol. 7 No. 1, pp. 76-93, 2010.
[11] Gudmundsson, D. and Goldberg, K., “Optimizing robotic part feeder throughput with queueing theory,” Assembly Automation, Vol. 27 No. 2, pp. 134–140, 2007.
[12] Papadopoulos,H.T., Heavy, C. and Browne, J., “Queueing Theory in Manufactring System Analysis and Design,” Springer Verlag Gmbh, 2013.
[13] Smith, J.M. and Tan, B., “Handbook of Stochastic Models and Analysis of Manufacturing System Operations,” Springer, 2013.
[14] Guy L.C. and , Feldman, R.M.,Manufacturing Systems Modeling and Analysis, Second Edition, Springer, 2010.
[15] Gershwin.S.B., “Manufacturing Systems Engineering, Englewood Cliffs, NJ: Prentice-Hall,1994.
[16] Yao, D.D., “Stochastic Modeling and Analysis of Manufacturing Systems,” Berline: Springer-Verlag,1994.
[17] Askin, R.G. and Standridge, C.R., “Modeling and Analysis of Manufacturing Systems,” New York:Wiley, 1993.
[18] Koo, P.H., Moodie, C.L., and Talavage, J.J., “A spreadsheet model approach for integrating static capacity planning and stochastic queuing model,” International Journal of Production Research, vol. 33, no. 5, pp. 1369-1385, 1995.
[19] Sukhotua, V. and Peters, B.A., “Modelling of material handling systems for facility design in manufacturing environments with job-specific routing,” International Journal of Production Research, vol. 50, no. 24, pp. 7285-7302, 2005.
[20] Marcheta, G., Melacinia, M., Perottia, S. and Tappiaa, E., “Analytical model to estimate performances of autonomous vehicle storage and retrieval systems for product totes,” International Journal of Production Research, Vol. 50, No. 24, pp. 7134-7148, 2012.
[21] Sivakumar, A.I. and Chong, C.S., “A simulation based analysis of cycle time distribution, and throughput in semiconductor backend manufacturing,” Computer in Industry, Vol. 59, pp. 78–45, 2001.
[22] Domaschke, J. and Brown, S., “Effective implementation of cycle time reduction,” Proceeding of the 1998 Winter Simulation Conference, USA, 1998.
[23] Wang, M., Sun, G. and Wang D., “Manufacturing simulation – An effective tool for productivity improvement productivity and reducing manufacturing cycle time through simulation modeling,” Proceeding of 3rd International Microelectronic & Systems Conference, Malaysia, 1993.
[24] Toh, G.K., Teck, U.W., Lie, A., Sun, G., Ming, W., and Kok, K. “Reducing manufacturing cycle time of wafer fab with simulation,” World Scientific, Vol. July 1995, pp. 889-896, 1995.
[25] Zhou, M., Chen, Z., He, W. and Chen, X., “Representing and matching simulation cases: a case-based reasoning approach,” Computers & Industrial Engineering, Vol. 59, pp. 115–125. 2010.