Simulation metamodeling through artificial neural networks

被引：109

作者：

Fonseca, DJ ^{[1
]}

Navaresse, DO ^{[1
]}

Moynihan, GP ^{[1
]}

机构：

[1] Univ Alabama, Dept Ind Engn, Tuscaloosa, AL 35487 USA

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2003年 / 16卷 / 03期

关键词：

artificial neural networks; metamodeling; simulation; job shop sequencing;

D O I：

10.1016/S0952-1976(03)00043-5

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Simulation metamodeling has been a major research field during the last decade. The main objective has been to provide robust, fast decision support aids to enhance the overall effectiveness of decision-making processes. This paper discusses the importance of simulation metamodeling through artificial neural networks (ANNs), and provides general guidelines for the development of ANN-based simulation metamodels. Such guidelines were successfully applied in the development of two ANNs trained to estimate the manufacturing lead times (MLT) for orders simultaneously processed in a four-machine job shop. The design of intelligent systems such as ANNs may help to avoid some of the drawbacks of traditional computer simulation. Metamodels offer significant advantages regarding time consumption and simplicity to evaluate multi-criteria situations. Their operation is notoriously fast compared to the time required to operate conventional simulation packages. (C) 2003 Elsevier Ltd. All rights reserved.

引用

页码：177 / 183

页数：7

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