Unreliable Production Lines with Simultaneously Unbalanced Operation Time Means, Breakdown, and Repair Rates

This paper investigates the benefits of deliberately unbalancing both operation time means (MTs) and unreliability (failure and repair rates) for non-automated production lines. The lines were simulated with various line lengths, buffer capacities, degrees of imbalance and patterns of MT and unreliability imbalance. Data on two performance measures, namely throughput (TR) and average buffer level (ABL) were gathered, analyzed and compared to a balanced line counterpart. A number of conclusions were made with respect to the ranking of configurations, as well as to the relationships among the independent design parameters and the dependent variables. It was found that the best configurations are a balanced line arrangement and a monotone decreasing MT order, coupled with either a decreasing or a bowl unreliability configuration, with the first generally resulting in a reduced TR and the second leading to a lower ABL than those of a balanced line.

Authors:

• S. Shaaban
• T. McNamara
• S. Hudson

Keywords:

• Average buffer level
• throughput
• unbalanced failure and repair rates
• unequal mean operation times
• unreliable production lines.

References:

[1] H. Tempelmeier, “Practical considerations in the optimization of flow
production systems,” International Journal of Production Research, vol
41, no.1, pp. 149-170, 2003.
[2] F. S. Hillier and R. W. Boling , R. W. (1966) “The effect of some design
factors on the efficiency of production lines with variable element
times,” Journal of Industrial Engineering, vol. 17, no. 12, pp. 651-658,
1966.
[3] T. El-Rayah, T, “The effect of inequality of inter-stage buffer capacities
and operation time variability on the efficiency of production line
systems,” International Journal of Production Research, vol.17, no.1,
pp.77-89, 1979.
[4] K. So, “Optimal buffer allocation strategy for minimizing work-inprocess
inventory in unpaced production lines,” IIE Transactions, vol.
29, no. 1, pp. 81-88, 1997.
[5] H. T. Papadopoulos and M. I. Vidalis, “Minimizing WIP inventory in
reliable production lines”, International Journal of Production
Economics, Vol. 70, pp. 185-197, 2001.
[6] B. Das, J. M. Sanchez-Rivas, A. Gacia-Diaz and C. A. MacDonald, "A
computer simulation approach to evaluating assembly line balancing
with variable operation times," Journal of Manufacturing Technology
Management, vol. 2, no. 7, pp. 872-887, 2010.
[7] S. Shaaban, and T. McNamara, “Improving the efficiency of unpaced
production lines by unbalancing service time means”, International
Journal of Operational Research, vol. 4, no. 3, pp. 346-361, 2009.
[8] M. Hillier, “Designing unpaced production lines to optimize throughput
and work-in-process inventory,” IIE Transactions, vol. 45, no. 5, pp.
516-527, 2013.
[9] J. A. Buzacott, “The role of inventory banks in flow-line production
systems,” International Journal of Production Research, vol. 9, no. 4,
pp. 425-436, 1971
[10] J. Wijngaard, “The effect of inter-stage buffer storage on the output of
two unreliable production units in series, with different production
rates,” AIIE Transactions, Vol. 11, No. 1, pp. 42-47, 1979.
[11] T. Altiok, “Production lines with phase-type operation and repair times
and finite buffers,” International Journal of Production Research, vol.
23, no. 3, pp. 489-498, 1985.
[12] Y. F. Choong and S. B. Gershwin, “A decomposition method for the
approximate evaluation of capacitated transfer lines with unreliable
machines and random processing times”, IIE Transactions, Vol. 19, No.
2, pp. 150-159, 1987.
[13] D. E. Blumenfeld, “A simple formula for estimating throughput of serial
production lines with variable processing times and limited buffer
capacity”, International Journal of Production Research, vol. 28, no. 6,
pp. 1163-1182, 1990.
[14] V. S. Kouikoglou and Y. A. Phillis, “Discrete event modelling and
optimization of unreliable production lines with random rates,” IEEE
Transactions on Robotics and Automation, vol. 10, no. 2, pp. 153-159,
1994.
[15] A. A. Bulgak, P. D. Diwan and B. Inozu, “Buffer size optimization in
asynchronous assembly systems using genetic algorithms,” Computers
and Industrial Engineering, vol. 28, no. 2, pp. 309-322, 1995.
[16] G. A. Vouros and H. T. Papadopoulos, “Buffer allocation in unreliable
production lines using a knowledge based system,” Computers and
Operations Research, vol. 25, no. 12, pp. 1055-1067, 1998.
[17] S. Y. Chiang, C. T. Kuo and S. M. Meerkov, “DT-bottlenecks in serial
production lines: theory and application,” IEEE Transactions on
Robotics and Automation, vol. 16, no. 5, pp. 352-359, 2000
[18] W. L. Sloan, “A study on the effects of protective capacity on cycle time
in serial production lines”, M.Sc. Thesis, Mississippi State University,
Mississippi, 2001.
[19] H. Tempelmeier and M. Bürger, “Performance evaluation of unbalanced
flow lines with general distributed processing times, failures and
imperfect production,” IIE Transactions, vol. 33, pp. 293-302, 2001.
[20] S. Helber, “Cash-flow-orientated buffer allocation in stochastic flow
lines,” International Journal of Production Management, vol. 39, no. 14,
pp. 3061-3083, 2001.
[21] J. W. Herrmann and M. M. Chincholkar, “Reducing throughput time
during product design,” Journal of Manufacturing Systems, vol. 20, no.
6, pp. 416-428, 2001/2002.
[22] E. J. Enginarlar, J. Li, S. M. Meerkov, and R. Q. Zhang, “Buffer
capacity for accommodating machine downtime in serial production
lines,” International Journal of Production Research, vol. 40, no. 3, pp.
601-624, 2002.
[23] M. I. Vidalis and C. Heavey, “On the workload allocation problem of
short unreliable production lines with finite buffers,” in Proc. 4th
Aegean Int. Conf. Analysis of Manufacturing Systems, Samos Island,
Greece, pp. 1158–1162, 2003.
[24] E. J. Enginarlar, J. Li and S. M. Meerkov, “Lean buffering in serial
production lines with non-exponential machines,” OR Spectrum, vol. 27,
pp. 195-219, 2005
[25] J. Li and S. M. Meerkov, “Evaluation of throughput in serial production
lines with non-exponential machines,” In Boukas, E. K. and Malhame,
R. (Eds.), Analysis, Control and Optimization of Complex Dynamic
Systems, Berlin, Springer, pp. 55-82, 2005
[26] S. Y. Chiang, A. Hu and S. M. Meerkov, “Lean buffering in serial
production lines with nonidentical exponential machines,” IEEE
Transactions on Automation Science and Engineering, vol. 5, no. 2, pp.
298-306, 2008.
[27] J. K. Cochran, A. Kokangul and T. A. Khaniyev, “Stochastic
approximations for optimal buffer capacity of many-station production
lines,” International Journal of Mathematics in Operational Research,
vol. 1, nos. 1/2, pp. 211-227, 2009.
[28] J. Li, D. E. Blumenfeld, N Huang and J. M. Alden, “Throughput
analysis of production systems: recent advances and future topics,”
International Journal of Production Research, vol. 47, pp. 3823–3851,
2009.
[29] A. L. Patti and K. J. Watson, “Downtime variability: the impact of
duration-frequecny on the performance of serial production systems”,
International Journal of Production Research, vol. 48, no. 19, pp. 5831-
5841, 2010.
[30] N. Huang, “Mean station reliabilities cause throughput overestimates in
production system design,” Journal of Manufacturing Systems, vol. 31,
pp. 184– 195, 2012.
[31] W. Guo, J. Jin, and J. Hu, “Allocation of maintenance resources in
mixed model assembly systems,” Journal of Manufacturing Systems,
vol. 32, pp. 473– 479, 2013.
[32] N. Slack, “Work time distributions in production system modelling”,
research paper, Oxford Centre for Management Studies, 1982.
[33] A. M. Law and W. D. Kelton, Simulation Modeling and Analysis,
Illinois, Irwin / McGraw-Hill, 2000.
[34] C. Harrell, B. K. Ghosh and R. O. Bowden, Simulation using ProModel,
New York, McGraw – Hill, 2004.
[35] R. R. Inman, “Empirical evaluation of exponential and independence
assumptions in queuing models of manufacturing systems,” Production
and Operations Management, vol. 8, no. 4, pp. 409-432, 1999.
[36] A. M. Law, Simulation Modeling and Analysis, Illinois, Irwin /
McGraw-Hill, 2007
[37] S. Shaaban, T. McNamara and S. Hudson, “Mean time imbalance effects
on unreliable unpaced serial flow lines,” Journal of Manufacturing
Systems, vol. 33, no. 3, pp. 357-365, 2014.