現今全球貿易愈趨頻繁、商品快速流通，消費者購物環境變遷迅速，使得消費需求朝向「多樣、少量、高頻率」的購物行為模式，進而促使零售商改變其訂貨模式。物流中心為了滿足零售商多樣化的訂單需求，必須面對改善內部運作流程之挑戰，以達到提升作業效率，同時有效控制作業成本。許多研究顯示物流中心的揀貨作業成本佔據整體物流成本的比例高達50%以上，所以企業紛紛尋求如何有效控制揀貨成本。目前解決方案包含貨架的重新配置、儲存場所的空間配置、儲位指派、訂單揀貨路線的規劃、或採批次訂單揀貨以減少揀取頻率。前兩類解決方案重於倉儲相關硬體的調整，此往往需花費較高的人力與物力且耗用期程較長。後兩類解決方案則是透過分析的方式尋求商品或訂單資料的有用訊息，提供流程改善的策略，此屬於軟體上的調整。本研究嘗試結合商品關連與IK分析之特性，期提供在商品數量眾多時一種快速有效的儲位指派法則。經初步測試，本研究認為此法則所提出的解決方向，具備進一步研究與應用的潛力。

Since the consumer behavior changed, the orders become towards varied items with small-volume and issued frequently. The managers of distribution centers must to face the challenges of improving operational efficiency and decreasing costs. Many studies have shown that the picking cost might occupy more than 65% of the overall logistics costs of the companies. There are many strategies could be considered to decrease picking cost, such as: shelf reconfigured, storage space reconfiguration, storage assignment rule, order picking rule, route planning, or order batching, etc. The first two categories emphasis on re-adjustment the hardware, so called “hard” improvements, it takes more cost and time-consumed. The others are by analyzing the orders or useful information, to provide process improvement, and therefore belong to the ＂soft＂ improvements. Different from the prior studies of clustering-assignment problem model (CAPM), this study considers items associated by data mining technology and proposed a heuristic procedure that combined item-association rule and IK analysis in order to provide a fast and effective storage assignment for high-volume items number. The results of example show the algorithm can find a better solution than the CAPM model and IK analysis. In general, this study try to adopt a directly way to assign the item locations, and that could be potential for the further application.