結合商品關聯與EIQ方法論之IK分析應用於儲位指派之研究

Storage Assignment Based on Items Associated coupled with IK Analysis of the EIQ Methodology

張俊郁(Chun-Yu Chang)

指導教授 ： 陳志騰

國立臺中科技大學/資訊與流通學院/流通管理系碩士班/碩士(2014年)

摘要

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，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可能高達65%，所以企業紛紛尋求如何有效控制揀貨成本之解決方案。目前幾類方案如: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，或者以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，如此往往需花費較高的人力與物力且耗用期程較長。後三種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，因此屬於軟體的調整，本研究擬進行訂單商品的分析以提供調整儲位指派的參考，旨在達到揀貨距離縮減的效果。
本研究有別於先前研究之集群指派方式(CAPM)，直接採用資料探勘技術分析訂單中的商品間關聯性，藉此產生『大型商品』。透過此概念提出一結合商品關聯與IK分析的演算架構，經小型範例的實驗結果顯示本演算法可找出比CAPM以及IK排序方式更好的距離結果，且於所設定之條件參數下，本演算法於測試的例題中所產出的平均揀貨距離亦較CAPM為佳。

關鍵字

儲位指派 ； 物流中心 ； 商品關聯 ； EIQ ； 揀貨距離 ； 資料探勘

並列摘要

Since Today's global trade increasingly frequent, rapid flow of goods, and the shopping environment of consumer changes rapidly, that make the consumers behavior towards the "small-volume production of a wide range of different items and high-frequency“. Distribution centers in order to meet the needs of diverse orders, it must to face improving the challenges of working process, to improve operational efficiency and costs.
Many studies have shown that the picking cost might occupy more than 65% of the overall logistics costs of the companies, which are seeking ways to effectively control. Currently types of solution, such as: shelf reconfigured, space configuration of storage sites, storage assignment, order picking, route planning, or batch order picking to reduce picking frequency. The first two categories emphasis on re-adjustment the hardware, it takes a higher consumption and the process is longer. The others are by analyzing the orders or useful information, to provide process improvement, and therefore belong to the software. The purpose of this study is reduce the picking cost by analyzed the orders to make storage assignment.
Different from the prior studies of clustering-assignment problem model (CAPM), this study analyze the orders to find items associated by data mining technology. The experimental results show our 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, in order to shorten the picking distance and reduce picking costs.

並列關鍵字

Storage Assignment ； Distribution Center ； Items Associated ； EIQ ； Picking Distance ； Data Mining

參考文獻

樓邦儒(民94)。多目標決策應用於觀光防災區為之研究-以桃園復興鄉為例。管理科學研究所第一屆管理與決策學報，45-56。

De Koster, R., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182, 481-501.

Gu, J., Goetschalckx, M., & McGinnis, L. F. (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177, 1-21.

Liu, C. M. (1999). Clustering techniques for stock location and order-picking in a distribution center. Computers & Operations Research, 26, 989-1002.

Hwang, H., BAEK, W. J., & Lee, M. K. (1988). Clustering algorithms for order picking in an automated storage and retrieval system. The International Journal of Production Research, 26, 189-201.