Production Throughput Modeling under Five Uncertain Variables Using Bayesian Inference
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32922
Production Throughput Modeling under Five Uncertain Variables Using Bayesian Inference

Authors: Amir Azizi, Amir Yazid B. Ali, Loh Wei Ping


Throughput is an important measure of performance of production system. Analyzing and modeling of production throughput is complex in today-s dynamic production systems due to uncertainties of production system. The main reasons are that uncertainties are materialized when the production line faces changes in setup time, machinery break down, lead time of manufacturing, and scraps. Besides, demand changes are fluctuating from time to time for each product type. These uncertainties affect the production performance. This paper proposes Bayesian inference for throughput modeling under five production uncertainties. Bayesian model utilized prior distributions related to previous information about the uncertainties where likelihood distributions are associated to the observed data. Gibbs sampling algorithm as the robust procedure of Monte Carlo Markov chain was employed for sampling unknown parameters and estimating the posterior mean of uncertainties. The Bayesian model was validated with respect to convergence and efficiency of its outputs. The results presented that the proposed Bayesian models were capable to predict the production throughput with accuracy of 98.3%.

Keywords: Bayesian inference, Uncertainty modeling, Monte Carlo Markov chain, Gibbs sampling, Production throughput

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2125


[1] C. S. Tang, "The impact of uncertainty on a production line," Management Science, pp. 1518-1531, 1990.
[2] P. M. Swamidass and W. T. Newell, "Manufacturing strategy, environmental uncertainty and performance: a path analytic model," Management Science, pp. 509-524, 1987.
[3] C. J. Ho, "Evaluating the impact of operating environments on MRP system nervousness," International Journal of Production Research, vol. 27, pp. 1115-1135, 1989.
[4] R. Stratton, Robey, D., and Allison, I., "Utilising buffer management to manage uncertainty and focus improvement," in Proceedings of the International Annual Conference of EurOMA, Gronegen, the Netherlands, 2008.
[5] P. Kouvelis and J. Li, "Flexible Backup Supply and the Management of Lead Time Uncertainty," Production and Operations Management, vol. 17, pp. 184-199, 2008.
[6] S. C. Graves, "Uncertainty and Production Planning," Planning Production and Inventories in the Extended Enterprise, pp. 83-101, 2011.
[7] Z. Hoque, "A contingency model of the association between strategy, environmental uncertainty and performance measurement: impact on organizational performance," International Business Review, vol. 13, pp. 485-502, 2004.
[8] X. Yan and X. Su, Linear regression analysis: theory and computing: World Scientific Pub Co Inc, 2009.
[9] G. Kirchgässner and J. Wolters, Introduction to modern time series analysis: Springer Verlag, 2007.
[10] T. Efendigil, Önüt, S., Kahraman, C., "A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis," Expert Systems With Applications, vol. 36, pp. 6697-6707, 2009.
[11] L. Aburto and R. Weber, "Improved supply chain management based on hybrid demand forecasts," Applied Soft Computing, vol. 7, pp. 136-144, 2007.
[12] M. Khashei and M. Bijari, "A New Hybrid Methodology for Nonlinear Time Series Forecasting," Modelling and Simulation in Engineering, 2011.
[13] C. F. Chien, Hsiao, C.W., Meng, C., Hong, K.T. and S. T. Wang, "Cycle time prediction and control based on production line status and manufacturing data mining," 2005, pp. 327-330.
[14] K. R. Baker and S. G. Powell, "A predictive model for the throughput of simple assembly systems," European journal of operational research, vol. 81, pp. 336-345, 1995.
[15] D. E. Blumenfeld and J. Li, "An analytical formula for throughput of a production line with identical stations and random failures," Mathematical Problems in Engineering, vol. 3, pp. 293-308, 2005.
[16] J. Li, et al., "Comparisons of two-machine line models in throughput analysis," International Journal of Production Research, vol. 44, pp. 1375-1398, 2006.
[17] J. Li, et al., "Throughput analysis of production systems: recent advances and future topics," International Journal of Production Research, vol. 47, pp. 3823-3851, 2009.
[18] J. Alden, "Estimating performance of two workstations in series with downtime and unequal speeds," General Motors Research & Development Center, Report R&D-9434, Warren, MI, 2002.
[19] J. Mula, et al., "Models for production planning under uncertainty: A review," International Journal of Production Economics, vol. 103, pp. 271-285, 2006.
[20] S. Koh, et al., "A business model for uncertainty management," Benchmarking: An International Journal, vol. 12, pp. 383-400, 2005.
[21] M. A. Wazed, et al., "Uncertainty factors in real manufacturing environment," Australian Journal of Basic and Applied Sciences, vol. 3, pp. 342-351, 2009.
[22] A. M. Deif and H. A. ElMaraghy, "Modelling and analysis of dynamic capacity complexity in multi-stage production," Production Planning and Control, vol. 20, pp. 737-749, 2009.
[23] H. Tempelmeier, "Practical considerations in the optimization of flow production systems," International Journal of Production Research, vol. 41, pp. 149-170, 2003.
[24] M. S. Han and D. J. Park, "Optimal buffer allocation of serial production lines with quality inspection machines," Computers & Industrial Engineering, vol. 42, pp. 75-89, 2002.
[25] Y. C. Chou, et al., "Evaluating alternative capacity strategies in semiconductor manufacturing under uncertain demand and price scenarios," International Journal of Production Economics, vol. 105, pp. 591-606, 2007.
[26] L. Li, et al., "Throughput Bottleneck Prediction of Manufacturing Systems Using Time Series Analysis," Journal of Manufacturing Science and Engineering, vol. 133, p. 021015, 2011.
[27] C. M. Lee and C. N. Ko, "Short-term load forecasting using lifting scheme and ARIMA models," Expert Systems With Applications, 2011.
[28] F. Z. a. S. Zhong, "Time series forecasting using a hybrid RBF neural network and AR model based on binomial smoothing," World Academy of Science, Engineering and Technology, vol. 75, pp. 1471-1475, 2011.
[29] D. J. Spiegelhalter, , K. R. Abrams, J. P. Myles, Bayesian approaches to clinical trials and health-care evaluation, vol. 13: Wiley, Chichester, 2004.
[30] D. J. Spiegelhalter, N. G. Best, B. P. Carlin, A. Van Der Linde, Bayesian measures of model complexity and fit, Journal of the Royal Statistical Society. Series B, Statistical Methodology, 2002, pp. 583-639.
[31] S. P. Brooks and A. Gelman, Alternative methods for monitoring convergence of iterative simulations, Journal of Computational and Graphical Statistics, vol. 7, 1998, pp. 434-455.
[32] G. B. Hua, Residential construction demand forecasting using economic indicators: a comparative study of artificial neural networks and multiple regression, Construction Management and Economics, vol. 14, 1996, pp. 25-34.
[33] L. Aburto and R. Weber, Improved supply chain management based on hybrid demand forecasts, Applied Soft Computing, vol. 7, 2007, pp. 136-144.
[34] F. Zheng and S. Zhong, Time series forecasting using a hybrid RBF neural network and AR model based on binomial smoothing, World Academy of Science, Engineering and Technology, vol. 75, 2011, pp. 1471-1475.
[35] C. F. Chien, C. Y. Hsu, C. W. Hsiao, Manufacturing intelligence to forecast and reduce semiconductor cycle time, Journal of Intelligent Manufacturing, 2011 pp. 1-14.
[36] S. F. Arnold, Mathematical Statistics, Prentice-Hall, 1990.
[37] R. E. Walpole, Mayers, R.H., Mayers, S.L, Probability and statistics for engineers and scienticts, 6 ed., New Jersey, Prentice Hall Int. , 1998.
[38] Amir Azizi, Amir Yazid b. Ali, Loh Wei Ping and Mohammadzadeh, M. (2012b). A Hybrid model of ARIMA and Multiple Polynomial Regression for Uncertainties Modeling of a Serial Production Line. International Conference on Engineering and Technology Management (ICETM), P-ISSN 2010-376X and E-ISSN 2010-3778, Kuala Lumpur, Malaysia, 62, 63-68.
[39] Amir Azizi, Amir Yazid b. Ali, Loh Wei Ping and Mohsen Mohammadzadeh (2012c). Estimating and Modeling Uncertainties Affecting Production Throughput using ARIMA-Multiple Linear Regression International Proceedings of Computer Science and Information Technology, ISSN 2010-460X, Singapore, Trans Tech Publications, 1263-1268.
[40] Amir Azizi, Amir Yazid b. Ali and Loh Wei Ping (2011a). Prediction of the Production Throughput under Uncertain Conditions Using ANFIS: A Case Study. International Journal for Advances in Computer Science (IJACS), ISSN 2218-6638, 2(4), 27-32.