TY - JFULL AU - Amir Azizi and Amir Yazid B. Ali and Loh Wei Ping PY - 2012/10/ TI - Production Throughput Modeling under Five Uncertain Variables Using Bayesian Inference T2 - International Journal of Industrial and Manufacturing Engineering SP - 1875 EP - 1883 VL - 6 SN - 1307-6892 UR - https://publications.waset.org/pdf/7551 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 69, 2012 N2 - 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%. ER -