根據歷史資料顯示，某些物品時常出現在同一張客戶訂單中。這些物品間的相關程度可由客戶訂單資料中，統計重複出現的頻率取得。與其他不考慮物品相關程度的儲位配置方法相比，當相關程度高的物品被配置在近鄰的位置時，揀貨員即可使用較少的時間完成訂單的揀貨程序。本研究提出最佳化模型以使相關程度高之物品被置放在近鄰的位置。然而，即使所提出的整數非線性規畫模式可轉換成整數線性規畫模式，此模型仍然無法解決大型問題。因此，本研究根據於小型問題之最佳解所觀察之特性，發展兩個啟發式演算法以處理大型問題。最後本研究進行數值實驗以比較所發展之演算法與隨機儲位方法(Random Storage)以及分類儲位方法(Class-Based Storage)的配置結果差異。

According to historical customer orders, some items tend to be ordered at the same time, i.e., in the same orders. The correlation of items can be obtained by the frequency of these items present at the same orders. When the dependent items are assigned to adjacent storage locations, order pickers will spend less time to complete customer orders, compared to the other storage assignment which treats items independently. This research provides optimization models to make sure that the storage locations of highly dependent items are nearby. Although the provided nonlinear integer programming can be transformed to a linear integer model, it is still too complex to deal with large problems. For a large number of items, two heuristic algorithms are proposed according to properties of optimal solutions in small problems. Numerical experiments are conducted to show the results of the proposed algorithms with the comparison of the random and class-based storage assignment.

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