許多研究問題常需運用電腦模擬來解決並希望同時滿足多個目標，樹脂轉注成型（Resin Transfer Molding, RTM）就是其中的一個例子。本研究使用目標規劃（Goal Programming, GP）去評估RTM的問題，使用拉丁超方格設計（Latin Hypercube Design, LHD）從電腦模擬中挑選出訓練數據（Training Data），並使用Kriging建立近似模型(Approximation Model)來取代電腦模擬，並發展新的演算法在合理的時間下找出RTM製程最佳的變數設定包括入射孔、射出孔、壓力與黏度這四個變數的最佳設定以縮短RTM製程時間同時提高品質還有最小化預算與黏度設定的偏差值（Deviation）。最後再用模擬模型來驗證預測模型所找出的變數設定的結果。
在演算法方面，本研究運用Kriging所提供之斜率資訊來改善粒子群最佳化演算法（Particle Swam Optimization ,PSO），並命名為PSO-Gradient演算法。結果發現本研究發展的演算法可以有效的搜尋出RTM製程的最佳變數設定且PSO-Gradient演算法的效果優於傳統PSO。

Many design problems incorporate computer simulation as design tool and are required to meet several performance criteria. Resin Transfer Molding (RTM) process is one of the examples. In this research, we use Goal Programming (GP) to formulate the RTM problem, use Latin Hypercube Design (LHD) to sample the training data from computer simulation model, apply Kriging to construct an approximation model to replace the simulation model, and develop a new algorithm to identify the good design setting in a reasonable time for the RTM process. The values of gate location, vent location, pressure, and viscosity are determined to minimize the fill time, maximize the quality, and minimize the deviations from budget plan and viscosity setting. The performance of the setting identified by the approximation model is verified by simulation results.
In our algorithm, we use the gradients estimated by Kriging model to improve Particle Swam Optimization (PSO) algorithm. We call it PSO-Gradient algorithms. The results show that this approach can effectively identify a good setting for RTM process and the PSO-Gradient algorithms outperforms traditional PSO.

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