Analytical and Discrete-Event Simulation Models - Ambelu, Melkamu; Geremew, Sisay
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System-wide performance analysis of manufacturing setup helps a company to stay competitive. This can be done by selecting appropriate performance analysis tool which can save time and effort. As a problem assembly line systems are difficult to completely model and analyze using either of analytical or discrete-event simulation (DES) models. The main objective of this study is to analyze the distinct modeling capabilities of analytical modeling approach and DES approach so as to take their respective primacy for analysis of particular pertinent parameters suitable for Tana Communication (TC…mehr

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Produktbeschreibung
System-wide performance analysis of manufacturing setup helps a company to stay competitive. This can be done by selecting appropriate performance analysis tool which can save time and effort. As a problem assembly line systems are difficult to completely model and analyze using either of analytical or discrete-event simulation (DES) models. The main objective of this study is to analyze the distinct modeling capabilities of analytical modeling approach and DES approach so as to take their respective primacy for analysis of particular pertinent parameters suitable for Tana Communication (TC assembly line). Both analytical and discrete-event simulation models are developed for TC production process using decomposition approach and AnyLogic software. The results from the two models for work in process, queue cycle time, cycle time and resource utilization have high degree of agreement. By making reassignment of operators from the idle stage to the bottleneck stage the system waiting time and work in process is reduced by 12% and 13% respectively from the proposed model.
Autorenporträt
Mr Melkamu Ambelu Biazen received BSc in Industrial Engineering, MSc in Production Engineering and Management.Currently, he is lecturer in Industrial Engineering, Dire Dawa University,Ethiopia. His research interests include system dynamics, process optimization, and production and operations management.