自動倉儲系統下運用關聯法則與集群分析的儲位指派探討

Storage Allocation in Automated Storage And Retrieval Systems by Association and Cluster Analysis

近年來，由於市場劇烈競爭與消費需求改變，為了提高作業流程效率與快速回應顧客訂單需求，倉儲成為供應鏈一體化的重要環節。對此，為了提升倉儲效率、降低訂單揀貨成本，企業開始引進自動倉儲系統。本研究針對自動倉儲系統，提出結合產品相關性、每類體積指標（Cube-Per-Order Index, COI）法則以及儲位指派策略，建構關聯法則與集群分析兩種儲位規劃，與傳統儲位規劃方式比較以利有效地降低揀貨搬運距離。本研究探討倉儲中心在相同的訂單內容下，不同走道數及不同操作策略對揀貨距離的影響。藉由研究中建構的儲位規劃指派方式，平衡每一走道的總揀貨量，一方面減少機器作業的閒置時間，另一方面縮短總揀貨距離來達成提升揀貨效率之目的。實驗結果發現兩種儲位規劃與傳統隨機指派方式相比都有明顯改善；與COI法則模式相比雖對於揀貨距離的影響很小，但在走道揀貨量的平衡上有明顯改善。

In recent years, the market has been competitive and the customer behavior has been changed. In order to improve the efficiency of workflow and to meet orders, the warehouses play an important role in supply chains. Thus many companies start to install Automated Storage/Retrieval Systems (AS/RS) to increase the efficiency and reduce the cost of order picking. Therefore, this study purposes two kinds of storage planning with combining product relevance, Cube-Per-Order Index (COI) rules and storage assignments which can effectively reduce the picking cost and travel distance. This study equals the workload of all zones with storage assignments of product relation and balancing workload. The method can reduce idle time of machine operation. On the other hand, it can reduce the overall travel distance to achieve the purpose of improving the picking efficiency. Finally, the results show that there is no significant difference between two kinds of storage method and COI rule storage, but two kinds of storage method can balance the workloads.