在揀貨系統當中，完成一張顧客訂單的旅途時間對於揀貨系統績效是一項重要的指標。為了增加揀貨系統的效率，一張顧客訂單應該盡可能快速地離開揀貨系統。本研究依據物品出現在相同訂單上的頻率來做物品分群之概念提供一個線性的數學模型，並為了有效地達到將相關性高的物品存放在一起又提出三種關於儲位安排的啟發式之演算法。在實驗分析當中，小實驗顯示出三個提議出來的儲位安排之演算法與由AMPL軟體求得出來的最佳解之間的關係，而大實驗則顯示出此三種儲位安排之演算法有較優於隨機式儲位政策與分區式儲位政策的績效表現。最後，經由實驗數據證實確實能夠藉由此三種儲位安排方法做為較佳的物品儲位政策。

In an order picking system, travel time to complete a customer order is a key point of the system performance. To improve the efficiency of the order picking system, an order should leave the system as soon as possible. This study proposes a Linear Programming model for clustering items which frequently appear in the same orders. This research also provides three heuristic algorithms for storage assignment in order to make high similarity items be stored together. A small experiment presents the relationship between three proposed algorithms and AMPL for optimal solution. A large experiment shows that the proposed algorithms have better performance than random storage and class-based storage policies. Finally, empirical data and experiments verify that the objective of the item clustering model is achieved by the proposed heuristic algorithms.

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