Evaluation of a proposed optimization method for discrete-event simulation models
Optimization methods combined with computer-based simulation have been utilized in a wide range of manufacturing applications. However, in terms of current technology, these methods exhibit low performance levels which are only able to manipulate a single decision variable at a time. Thus, the objective of this article is to evaluate a proposed optimization method for discrete-event simulation models based on genetic algorithms which exhibits more efficiency in relation to computational time when compared to software packages on the market. It should be emphasized that the variable's response quality will not be altered; that is, the proposed method will maintain the solutions' effectiveness. Thus, the study draws a comparison between the proposed method and that of a simulation instrument already available on the market and has been examined in academic literature. Conclusions are presented, confirming the proposed optimization method's efficiency.
| Main Authors: | , , , , , |
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| Format: | Digital revista |
| Language: | English |
| Published: |
Sociedade Brasileira de Pesquisa Operacional
2012
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| Online Access: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382012000300004 |
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| Summary: | Optimization methods combined with computer-based simulation have been utilized in a wide range of manufacturing applications. However, in terms of current technology, these methods exhibit low performance levels which are only able to manipulate a single decision variable at a time. Thus, the objective of this article is to evaluate a proposed optimization method for discrete-event simulation models based on genetic algorithms which exhibits more efficiency in relation to computational time when compared to software packages on the market. It should be emphasized that the variable's response quality will not be altered; that is, the proposed method will maintain the solutions' effectiveness. Thus, the study draws a comparison between the proposed method and that of a simulation instrument already available on the market and has been examined in academic literature. Conclusions are presented, confirming the proposed optimization method's efficiency. |
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