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Simulation metamodeling through artificial neural networks

机译:通过人工神经网络进行模拟元建模

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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 metamodcls. 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.
机译:在过去的十年中,模拟元建模一直是一个主要的研究领域。主要目标是提供强大,快速的决策支持工具,以增强决策过程的整体有效性。本文讨论了通过人工神经网络(ANN)进行仿真元建模的重要性,并为基于ANN的仿真元模块的开发提供了一般指导。此类准则已成功地应用于开发了两个经过训练的人工神经网络,这些人工神经网络用于估计在四台机器的车间同时处理的订单的制造提前期(MLT)。诸如人工神经网络之类的智能系统的设计可能有助于避免传统计算机仿真的某些弊端。元模型在耗时和简化评估多标准情况方面都具有明显的优势。与运行常规仿真程序包所需的时间相比,它们的运行速度非常快。

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