模擬最佳化是一種利用一系列模擬的資訊找出系統中最佳的方案的解決之道，在個人電腦發達的時代下受到了廣泛的注意並且已運作在許多實務問題上。然而，典型的模擬最佳化方法是將問題視為一個隨機系統並以期望值作為績效衡量指標，少有以分量做為績效衡量指標的研究。本研究提出一個梯度搜尋架構，gradient-based framework for quantile-based simulation optimization (GBQS)來解決以分量為績效衡量指標的模擬最佳化問題。GBQS是以STRONG-S為基礎並且作修改，使之得以解決更高維度的問題，過程中運用了大量的統計方法如實驗設計、分量迴歸、因子篩選及假設檢定來提高求解效率及控制求解的品質，在最後的數值實驗及一個實務問題也獲得了不錯的表現，驗證了GBQS在各種情況下都有著高度的適應性。

Simulation optimization is one kind of optimization methods aimed to find the best solution in a simulated stochastic system. Especially in PC-era, simulation optimization has been attracting a lot of attention, and adopted in many practical problems. However, classical simulation optimization methods focused on expectation-based problems; seldom researches considered quantile-based problems. In this thesis, a gradient-based framework for quantile-based simulated optimization (GBQS) has been proposed. GBQS is based on the framework of STRONG-S, and modified it to fit the quantile-based case. GBQS is designed to solve not only lower dimensional problems but also higher ones. For efficiency and controlling solution qualification purpose, GBQS uses a good deal of statistical techniques such as design of experiments, quantile regression, factor screening, and hypothesis testing. GBQS is verified as a highly adaptable method because it has good performance on many situations by testing for several numerical experimental problems and a practical problem.

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