Using simulation to predict system performance: a case study of an
electro‐phoretic deposition plant

Chan, Felix T. S.

doi:10.1108/09576069510093451

Cited by 18 publications

(8 citation statements)

References 18 publications

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“…peaks or oscillations, cannot be performed. An example how simulation based methods can help to predict the system performance is presented in [9]. A simulation model of an automotive production line shows the consequences of parameter variation in the production steps.…”

Section: State Of the Artmentioning

confidence: 99%

“…1. High level view on the framework for logistic plants Casella et al [21] Lamquet et al [22] Hegny et al [6] Freund et al [7] Rohrer et al [5] Chan [9] Fioroni et al [10] Rooker et al [11] Pawlewski et al [12] Bzymek et al [13] Prat et al [14], [15] Cao et al [23] Köslin et al [16] Black et al [17] Simulation of logistic process layer (time discrete) (R 1)…”

Section: State Of the Artmentioning

confidence: 99%

See 1 more Smart Citation

Bridging the gap between discrete and continuous simulation of logistic systems in production based on the Modelica modeling language

Regulin

Krooss

Schütz

et al. 2015

2015 IEEE 20th Conference on Emerging Technologies &Amp; Factory Automation (ETFA)

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The demand for mass-customized products is increasing steadily. As a consequence, the requirements regarding flexibility and efficiency of automated production systems and their logistic parts are increasing as well. In order to evaluate different plant designs before the commissioning or due to necessary reconfigurations, model-based approaches in combination with simulation techniques can be applied. Therefore, the interaction of transported load and physical aspects of plant components, e.g. motor current, needs to be described and evaluated. To bridge the gap between the time-discrete behavior of manufactured products, i.e. their occurrence, and the continuous characteristics of a transportation system's modules, i.e. differential equations, in this work a model interface was developed and integrated into model classes of the Modelica modeling language. The correct behavior of the particular library items has been proven by tests of the logical behavior and the comparison to a real conveyor belt drive system.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Bridging the gap between discrete and continuous simulation of logistic systems in production based on the Modelica modeling language

Regulin

Krooss

Schütz

et al. 2015

2015 IEEE 20th Conference on Emerging Technologies &Amp; Factory Automation (ETFA)

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show abstract

“…Discrete-event modeling and simulation (DES) is a popular tool in widely varying fields for identifying and answering questions about the effects of changes on processes. Simulation has been utilized to predict system performance in the automotive industry (Chan, 1995;Chan & Jian, 1999), motion control industry (McDonald et.al., 2002), design cells in lamp manufacturing (Chan and Abhary, 1996), aid in implementing Total Quality Management (Aghaie & Popplewell, 1997), Business Process Reengineering (Doomun & Jungum, 2008), and conversion to constant work-in-process levels, also known as CONWIP (McDonald, et al, 2002b;Li, 2010) Simulation has also been used for modeling value stream maps of a production line (McDonald, et.al., 2002b), modeling complex manufacturing systems (Benedettini & Tjahjono, 2009) and in the identification of bottlenecks Li, 2010). Discrete-event simulation models have been found to significantly improve the design, management, and analysis of production systems Li, 2010).…”

Section: Introductionmentioning

confidence: 99%

Utilizing simulation to evaluate production line performance under varying demand conditions

McDonald¹,

Akenb²,

Ellib³

2012

IJIEC

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Determining how a new production cell will function is problematic and can lead to disastrous results if done incorrectly. Discrete-event simulation can provide information on how a line will function before, during, and after the line is in operation. A simulation model can also provide a visual animation of the line to see how product will flow through the line. This paper discusses the development and analysis of a simulation model of a new manufacturing line. The manufacturing cell is a new motor assembly cell. An analysis of the capability of the line for varying demand levels was conducted for the two main motor types produced on the line. An ARENA® simulation model was developed, verified, and validated to determine the daily production and potential problem areas for the various demand levels. The results show that at all but one demand level, the line is capable of producing to within one unit of customer demand if the required number of workers is present. At the highest demand level, the simulation results suggest that the line is not capable of meeting demand. Additional analysis indicates that multiple workstations could prove problematic with minor fluctuations in demand. Problematic workstations were identified for each assembly area and for the line as a whole.

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“…Systems designers use Discrete Event Simulation (DES) to respond to this challenge, designing and redesigning the required systems through the use of computer models, which can be easily adapted and re-evaluated without the need to invest time and effort in changing the physical systems (Chan 1995). However, the results predicted by DES models have consistently been shown to be different from the results that occur in practice.…”

Section: Introductionmentioning

confidence: 99%

Using empirical evidence of variations in worker performance to extend the capabilities of discrete event simulations in manufacturing

Baines

Hadfield

Mason

et al.

Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693)

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Discrete Event Simulation of mauufacturing systems has become widely accepted as an important tool to aid the design of such systems. OAen, however, it is applied by practitioners in a manner which largely ignores an important element of industry; namely, the workforce. Workers are usually represented as simple resources, often with deterministic performance values. This approach ignores the potentially large effect that human performance variation can have on a system. A long-term data collection exercise is described with the aim of qnantifylng the performance variation of workers in a typical automotive assembly plant. The data are presented in a histogram form which is immediately usable in simulations to improve the accuracy of design assessment. The results show levels of skewness and range which are far larger than anticipated by current researchers and practitioners in the field.

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Using simulation to predict system performance: a case study of an electro‐phoretic deposition plant

Cited by 18 publications

References 18 publications

Bridging the gap between discrete and continuous simulation of logistic systems in production based on the Modelica modeling language

Bridging the gap between discrete and continuous simulation of logistic systems in production based on the Modelica modeling language

Utilizing simulation to evaluate production line performance under varying demand conditions

Using empirical evidence of variations in worker performance to extend the capabilities of discrete event simulations in manufacturing

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