Order Consolidation via Data Mining Techniques for Distribution Centers

• Main Authors: Hsiao-Pin Wu, 吳曉蘋
• Other Authors: Mu-Chen Chen
• Format: Others
• Language: zh-TW
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/55412682839114898589

碩士 === 國立臺北科技大學 === 商業自動化與管理研究所 === 90 === Previous studies of automated warehousing have developed. “Single order picking” and “batch picking” are two common methods that are widely adopted by distribution centers nowadays. The order batching is a complex problem. However, the order batching output can influence the performance of order picking operations. In order to enhance the efficiency of order picking, this study intends to integrate data mining technique of association rules and then develop an optimal algorithm and two heuristic algorithms for batching the orders in distribution centers. Under warehouses parallel aisles, the order data sets are tested for order batching by using First Come First Service (FCFS), Random Sequential Batching Method (RSPM), 0-1 integer planning (ZOIP), Stepwise Batching Heuristic Method (SBHM), and Concurrent Batching Heuristic Method (CBHM) algorithms. The results show that the picking distance, the number of order pickers and the number of storage retrieval machines can be reduced by using the proposed ZOIP, SBHM, and CBHM algorithms.