Throughput analysis of production systems: recent advances and future topics

Li, Jingshan; Blumenfeld, Dennis E.; Huang, Ning; Alden, Jeffrey M.

doi:10.1080/00207540701829752

Cited by 293 publications

(96 citation statements)

References 131 publications

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“…The convergence was achieved because both chains were overlapped with each other [18]. The dynamic trace plots of the stochastic parameters on 10,000 iterations are shown in Figure 3 with a 95% credible interval.…”

Section: Dynamic Trace Plot Of Uncertain Parametersmentioning

confidence: 98%

“…The third checking used the Brooks Gelman Rubin (BGR) diagnostic. BGR numerically shows the convergence ratio, which should be near to 1 [18]. The idea is to generate multiple chains starting at over-dispersed initial values, and assess convergence by comparing within and between chain variability over the second half of those chains.…”

Section: Bayesian Model Validationmentioning

confidence: 99%

“…The idea is to generate multiple chains starting at over-dispersed initial values, and assess convergence by comparing within and between chain variability over the second half of those chains. According to Li, E. Blumenfeld [18], the BGR is calculated as shown in Eq. (1):…”

Section: Bayesian Model Validationmentioning

confidence: 99%

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Integration of Seasonal Autoregressive Integrated Moving Average and Bayesian Methods to Predict Production Throughput under Random Variables

Azizi¹

2014

J MECH ENG SCI

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Analysing and modelling efforts on production throughput are getting more complex due to random variables in today's dynamic production systems. The objective of this study is to take multiple random variables of production into account when aiming for production throughput with higher accuracy of prediction. In the dynamic manufacturing environment, production lines have to cope with changes in set-up time, machinery breakdown, lead time of manufacturing, demand, and scrap. This study applied a Bayesian method to tackle the problem. Later, the prediction of production throughput under random variables is improved by the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The integrated Bayesian-SARIMA model consists of multiple random parameters with multiple random variables. A statistical index, R-squared, is used to measure the performance of the integrated model. A real case study on tile and ceramic production is considered. The Bayesian model is validated with respect to the convergence and efficiency of its outputs. The results of the analyses indicate that the Bayesian-SARIMA method produces a higher R-squared value, at 98.8%, compared with previous studies on Bayesian methods where the value was 90.68% and the ARIMA method where it was 97.38%. Consequently a robust approach in terms of the degree of prediction accuracy is proposed. This integrated method may be applied for the estimation of other production performance factors like lead time and cycle time in different types of dynamic manufacturing environment.

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Section: Dynamic Trace Plot Of Uncertain Parametersmentioning

confidence: 98%

Section: Bayesian Model Validationmentioning

confidence: 99%

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Integration of Seasonal Autoregressive Integrated Moving Average and Bayesian Methods to Predict Production Throughput under Random Variables

Azizi¹

2014

J MECH ENG SCI

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“…Deif & El Maraghy [11] showed through simulation that ignoring uncertainty sources leads to wrong decisions. Processing time and breakdown time affect the production throughput [7,12]. Peidro et al [13] described the process of uncertainty as a result of an unreliable production process due to machinery breakdown time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Production throughput is commonly considered in analysis and modelling to be an important measure of production line performance [5][6][7]. Production modelling in uncertain conditions becomes even more complex if the manufacturing environment includes multi-stage production on multi-products [8].…”

Section: Introductionmentioning

confidence: 99%

Production Uncertainties Modelling by Bayesian Inference Using Gibbs Sampling

Azizi

Ali

Ping

et al. 2015

SAJIE

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Analysis by modelling production throughput is an efficient way to provide information for production decision-making. Observation and investigation based on a real-life tile production line revealed that the five main uncertain variables are demand rate, breakdown time, scrap rate, setup time, and lead time. The volatile nature of these random variables was observed over a specific period of 104 weeks. The processes were sequential and multi-stage. These five uncertain variables of production were modelled to reflect the performance of overall production by applying Bayesian inference using Gibbs sampling. The application of Bayesian inference for handling production uncertainties showed a robust model with 2.5 per cent mean absolute percentage error. It is recommended to consider the five main uncertain variables that are introduced in this study for production decision-making. The study proposes the use of Bayesian inference for superior accuracy in production decision-making. OPSOMMINGAnalise deur middel van die modellering van produksie deurset is 'n effektiewe manier om inligting vir produksiebesluitneming te verskaf. Die waarneem en ondersoek van 'n teëlproduksielyn het getoon dat die vyf hoof onsekerheidsveranderlikes die vraagtempo, breektyd, skraptempo, opsteltyd en leityd is. Die vlugtige aard van hierdie toevalsveranderlikes is waargeneem oor 'n tydperk van 104 weke. Die prosesse was opeenvolgend en multi-stadium. Die vyf onsekerheidsveranderlikes van produksie is gemodelleer om die algehele vertoning van die produksie te weerspieël deur gebruik te maak van Bayesiese afleiding met Gibbs monsterneming. Die toepassing van Bayesiese afleiding vir die hanteer van produksie onsekerhede het 'n robuuste model, met 'n tweeen-'n-half persent gemiddelde absolute persentasie fout, tot gevolg gehad. Dit word aanbeveel dat die vyf belangrikste onsekerheidsveranderlikes, wat in hierdie studie bekendgestel is, oorweeg moet word vir produksiebesluitneming. Die studie stel die gebruik van die Bayesiese afleiding tegniek voor om sodoende beter akkuraatheid in produksiebesluitneming te verkry.

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Improving Battery Manufacturing Through Quality and Productivity Bottleneck Indicators

Xiao

et al. 2016

Advances in Battery Manufacturing, Service, and Management Systems

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Throughput analysis of production systems: recent advances and future topics

Cited by 293 publications

References 131 publications

Integration of Seasonal Autoregressive Integrated Moving Average and Bayesian Methods to Predict Production Throughput under Random Variables

Integration of Seasonal Autoregressive Integrated Moving Average and Bayesian Methods to Predict Production Throughput under Random Variables

Production Uncertainties Modelling by Bayesian Inference Using Gibbs Sampling

Improving Battery Manufacturing Through Quality and Productivity Bottleneck Indicators

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