應用反覆貪婪演算法求解多樓層倉儲訂單揀貨批量問題

過去研究指出倉儲中心在人工揀貨系統中為一勞力密集作業型態，其中人工揀貨作業成本約佔倉儲中心總作業成本35%至40%。因此，提升人工揀貨效率便為企業首要的議題。在訂單揀貨研究中，倉儲系統主要探討對象為單一樓層訂單揀貨問題，而鮮少研究探討多樓層倉儲訂單揀貨問題，而在多樓層倉儲訂單揀貨問題中，尚未發現有學者利用訂單批量來縮短總揀貨路徑，故本研究在此提出兩種反覆貪婪(Iterated Greedy ; IG)演算法來求解此問題，為驗證本研究所提出之IG演算法之求解效率，本研究將應用過去學者所提出之基因演算法(Genetic algorithms; GA) ，並擴充為多樓層倉儲問題，將其實驗結果進行比較，實驗結果發現在多樓層倉儲訂單揀貨問題使用IG演算法能求得較GA演算法績效更好的解，並提供企業訂單揀貨批量實務之使用並做為後續相關學術研究的參考依據。

關鍵字

訂單批量；多樓層倉儲訂單揀貨；反覆貪婪演算法

並列摘要

The past research pointed out that manual picking system is a type of intensive labor work in storage center. The cost of manual picking is about 35-40% of the total operating cost in the storage center. Therefore, to enhance artificial picking efficiency will be the primary issue for the enterprise. According to the research of order picking, the object of the warehousing system is for a single floor order picking problem, but few researches explore the multi-floor of warehouse order picking. In multi-floor of warehouse order picking, it has not been found that scholars use the order batching to shorten the overall procedure of order picking. This research provided two kinds of Iterated Greedy Algorithm (IG) to solve the problem, and verify the solution of efficiency from the proposed IG. This research applied to Genetic Algorithms (GA) which was proposed by the scholars in the past, and expended to multi-floor storage problems. And the experimental results are compared, which shows that Iterative Greedy Algorithm can obtain the better solution performance in multi-floor warehouse order picking, and it provides enterprise’s order batching of practical use as a reference for subsequent academic research.

並列關鍵字

Order batching ； Multiple-Level Warehouse ； Iterated Greedy Heuristic

參考文獻

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應用反覆貪婪演算法求解多樓層倉儲訂單揀貨批量問題

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