在全球性競爭壓力下，產品生命週期除了變得更短外，各家廠商莫不以削價進行市占率的侵蝕，此種現象在電子產業尤甚。在如此高競爭的產業中，藉以推估趨勢的長期資料已不可得，因此如何藉由短期的資料推估未來需求以因應生產線的人力配置，是一個值得探討的問題。此外，由於供應鏈內的長鞭效應往往會延遲資訊的傳遞，使得下游的批發商所提供的需求並不穩定(即非等間距)。雖然非等間距灰預測模式NGM(1,1)常被應用在此類的學習問題上，然而其預測值的準確度仍可藉由決定背景值而進行改善。事實上，參數 才是決定此些背景值的重要關鍵。因此本研究使用各期資料的落點資訊取代 而提出一個改良的NGM(1,1)模型，其中落點資訊是藉由模糊隸屬函數(MF)所決定，而MF則是基於盒鬚圖對合理值域的定義所建構。本研究以國內某龍頭不斷電系統製造商為例，蒐集2011年至2012年間對於某單一產品的需求量做為研究標的。實驗結果顯示，本研究所提出的改良型非等間距灰預測模式BWNGM(1,1)確實較傳統非等間距灰預測模式NGM(1,1)於個案資料中有較佳的預測準確度。
關鍵字：非等間距灰預測、非等間距短期時間序列資料、盒鬚圖

Under the pressure of global competition, product life cycles are becoming shorter; in addition, enterprises are trying to increase more market shares by reducing product prices. Such competition is especially intense in the electronics industry, and long-term data used to infer future demands is no more available. Therefore, obtaining future demands through short-term data to allocate the manpower of production lines is worthy of investigation. In addition, the demands provided by distributors usually represent unstable (i.e. non-equigap), since the bullwhip effect existing in the supply chains always delays the information delivery. Although the non-equigap grey model (NGM) is widely applied to this learning problem, there still exists one way to improve the accuracy of the predictors by determining the background values. In fact, the coefficient that decides those actually plays the key role. Therefore, an improved NGM(1,1) model, called the BWNGM(1,1), is proposed by taking the location information (LI) of each datum to replace the , where the LI is determined by the fuzzy membership function (MF), and the MF is constructed based on the reasonable value locating ranges defined by the Box-and-Whisker plots. The experiment dataset containing the demands of a product from 2011 to 2012 is collected from the leading UPS (uninterruptible power supply) manufacturer in Taiwan. The results shown that the predictive accuracies of the BWNGM(1,1) is better than those of the NGM(1,1).
Key words：NGM(1,1), short-term time series data, Box-and-Whisker plots

中文部分
林耀三，2006年，針對小型製造資料的漸增式學習演算法，國立成功大學工業與資訊管理學系，博士論文。
張哲榮，2007年，「適應性灰預測模型」，國立成功大學工業與資訊管理學系，碩士論文。
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