合併控制變量於具限制式之完全連續選擇程序;Applying Fully Sequential Procedures for Comparing Constrained Systems with Control Variate

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题名:	合併控制變量於具限制式之完全連續選擇程序;Applying Fully Sequential Procedures for Comparing Constrained Systems with Control Variate
作者:	陳瑋;Chen, Wei
贡献者:	工業管理研究所
关键词:	模擬;比較系統;連續選取程序;控制變量;限制條件;Simulation;Comparing systems;Fully sequential selecting procedures;Control Variate;Constrained systems
日期:	2018-07-20
上传时间:	2018-08-31 11:26:45 (UTC+8)
出版者:	國立中央大學
摘要:	排序與選擇程序(Ranking and Selection Procedure; R&S)是從許多不同的模擬系統中，找出績效表現最佳或近似最佳系統的模擬最佳化方法，且可使實驗者獲得結果的同時對其具一定信心水準。以往的排序與選擇程序的方法主要追求隨機目標最佳化，考量隨機限制式問題的研究不多。因此，Andradottir 和Kim (2010)發展可行性檢查程序(Feasibility Checking Procedure; FCP)，在統計的理論基礎上，找出滿足隨機限制式的可行或接近可行的系統。然而，假若模擬系統或隨機限制式數量過多，且績效輸出值變異程度過大，將導致抽樣成本及運算時間提高，也影響程序執行速度。因此透過變異縮減技術中的控制變量(Control Variate; CV)，利用與輸出值相關之輔助變數修正輸出估計量，使其變異下降以解決上述問題。本研究以等候線理論架構模擬系統，對具有隨機目標函數與單一隨機限制式的系統選擇問題，應用控制變量將所得之替代估計量應用於完全連續選擇之排序與選擇程序，並與Andradottir 和Kim (2010)提出的方法比較，減少了為滿足所求知最佳目標與限制條件對模擬系統需要的抽樣工作量，同時保證所解正確性的信心水準。 ;Ranking and Selection (R&S) is a kind of stochastic simulation for finding the system with best or near-best performance from among a finite number of alternatives. It also allows the experimenters to obtain results with a certain level of confidence. However, because of managerial or physical limits, sometimes we will face constraints on other performances. Therefore, Andradottir and Kim (2010) developed a Feasibility checking procedure (FCP) to find feasible or near-feasible systems which satisfied the stochastic constraints based on statistical theory. Nevertheless, the procedure can be inefficient when the number of candidate systems or the variances of sampling performances outputs are large. In this paper, we propose a new R&S procedure, combine the variance reduction techniques of Control variates (CV) with the FDP procedure. We provide a queuing example to compare our procedure with previous ones. In our procedure, we use a set of random variables that are correlated with the outputs of interest, whose means are known to the user, to replace the origin output. Since it can reduce the variance of the estimator for original, the new procedure is expected to be more efficient than other competitors in the sense that fewer observations and less computer time are needed to find the best system which under the constraints.
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