Application of spreadsheet and queuing network model to capacity optimization in product development

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Abstract

Modeling of a manufacturing system enables one to identify the effects of key design parameters on the system performance and as a result to make correct decision. This paper proposes a manufacturing system modeling approach using a spreadsheet model based on queuing network theory, in which a static capacity planning model and stochastic queuing model are integrated. The model was used to improve the existing system utilization in relation to product design. The model incorporates few parameters such as utilization, cycle time, throughput, and batch size. The study also showed that the validity of developed model is good enough to apply and the maximum value of relative error is 10%, far below the limit value 32%. Therefore, the model developed in this study is a valuable alternative model in evaluating a manufacturing system.

Original languageEnglish
Pages (from-to)1066-1071
Number of pages6
JournalWorld Academy of Science, Engineering and Technology
Volume58
Publication statusPublished - Oct 2009

Fingerprint

Spreadsheets
Product development
Circuit theory
Stochastic models
Product design
Throughput
Planning

Keywords

  • Manufacturing system
  • Product design
  • Spreadsheet model
  • Utilization

ASJC Scopus subject areas

  • Engineering(all)

Cite this

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abstract = "Modeling of a manufacturing system enables one to identify the effects of key design parameters on the system performance and as a result to make correct decision. This paper proposes a manufacturing system modeling approach using a spreadsheet model based on queuing network theory, in which a static capacity planning model and stochastic queuing model are integrated. The model was used to improve the existing system utilization in relation to product design. The model incorporates few parameters such as utilization, cycle time, throughput, and batch size. The study also showed that the validity of developed model is good enough to apply and the maximum value of relative error is 10{\%}, far below the limit value 32{\%}. Therefore, the model developed in this study is a valuable alternative model in evaluating a manufacturing system.",
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