供應商選擇一直是所有產業所關心的議題，零售商對供應商的要求眾多，從價格、穩定性、交期等，如何有效找出能滿足零售商的供應商評選指標，並根據該指標進行供應商之訂單分配為一個重要的課題。目前零售商大多都只與單一供應商進行交易，並衍生如壟斷市場或在嚴重天災下產業供應鏈斷裂之情況。面對眾多供應商時，卻又會產生訂單分配問題，零售商如何在供應商最大產能、最低訂購量等限制下，有系統地進行訂單分配，並同時最大化滿足零售商之需求、最小化成本與等待時間為一大難題。
品質機能展開是企業有效掌握最終消費者需求並將該需求轉換成具體設計規格的方法，而品質屋是品質機能展開中最重要的工具。而在過去文獻資料中，更有許多學者以供應商之角度，將品質機能展開之做法與概念應用在供應商評估中，將零售商需求連結至供應商之評選指標，並根據此評選指標選出最適合之供應商，鮮少有延伸至供應商之訂單分配問題。此外，由於市場變化迅速，下游些微變動在長鞭效應下，也會使得上游難以預測與規劃，因此零售商無法精確設定各目標式之理想值，故將模糊目標規劃引入，透過理想值與容忍值的設定，以貼近現實模糊不清之實際狀況。
本研究將模型分成三個階段。第一階段將零售商需求與供應商評選指標建立有效的連結；第二階段依產品種類個別建立品質屋，並將評選指標連結各供應商；第三階段利用模糊目標規劃方法，根據不同現實狀況套用不同模型，有效解決因眾多供應商所產生的訂單分配問題，同時以最大化零售商產品需求滿意度、最小化成本與等待時間為目標。
透過範例演練，發現本研究所提出之模型不僅優於傳統實數方法求解，且能在最低購買量限制下，根據不同情況套用至不同模型，找出最佳訂購數量。

Order allocation has become a topic of concern in all industries. Most retailers only trade with one supplier, resulting in situations such as monopolistic markets or supply chain disruptions. However, if they trade with many suppliers, retailers find it difficult to systematically allocate orders under constraints such as supplier capacity, the satisfaction of the retailer, etc. Quality function deployment (QFD) is a method by which companies can capture retailer demands that are difficult to measure due to interaction effects and can convert these demands into specific design specifications using a key tool, the House of Quality (HOQ). Scholars rarely discuss multi-product order allocation from the retailers’ perspective. In addition, retailers cannot clearly define the actual value of each target under the bullwhip effect in a rapidly changing world, which can be determined with the help of fuzzy goal programming (FGP).
In this study, we divide the model into three stages. QFD is used in both the first and second stages. The first stage establishes a link between retailer demands and the supplier selection indicators. The second stage establishes individual HOQ matrices to link selection indicators with each supplier based on product type. The results for the importance are regarded as retailer satisfaction. The third stage integrates these importance values into four different FGP models to solve order allocation problems.
With the help of practical exercises, we find the model proposed in this study not only makes it possible to obtain more accurate order quantities than would be the case otherwise, but also can be applied to different FGP models according to four situations.

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