The Study of Order Batching Methods with Delay Time Consideration Time in an Order Picking System

考量人工揀貨系統延遲時間下訂單批次化方法之研究

吳絹念(Juain-Nain, Wu)；楊明峯(Ming-Feng Yang)；施伯勳(Po-Hsun Shih)；石代維(Tai-Wei Shih)

picker-to-part warehouse system ； order picking ； warehouse management ； group genetic algorithm ； 人工倉儲系統 ； 揀貨作業 ； 倉儲管理 ； 群組基因演算法

耕莘學報

19期（2021 / 12 / 01）

52 - 68

英文

Order picking is the key factor affecting the total cost of warehousing operations and the efficiency of warehousing management. In the past, most of the research on order batching for order picking considered only the travel time or distance of pickers as the main indicator affecting the order picking efficiency. However, in practice, orders have to be fulfilled before certain due dates to avoid delivery delays. Thus, the order batching and sequencing problem has become an important issue in recent years. The objective of this paper is to devolve an order batching heuristic based on a group genetic algorithm for an order picking system and consider both the travel distance of pickers and the sequencing of orders to reduce the tardiness of picking orders. Through simulation experiments, the provided method is compared with the genetic algorithm and first-come-first-served method. Moreover, various scenarios are designed to validate the performance of the provided algorithm. Finally, the results show that the provided method based outperforms those existing methods significantly in an order picking system.

揀貨作業是影響倉儲運營總成本以及倉儲管理效率的關鍵因素。過去多數對於人工揀貨作業訂單批量相關研究，皆僅以揀貨員的行走時間或距離作為績效指標，然而，實務上訂單揀貨作業必須於期限內完成，以避免發生延遲交貨之情形。因此，訂單批量及揀貨順序問題成為近年來重要的研究議題。本目的旨在研究針對人工倉儲系統並同時考慮揀貨員行走距離及訂單排程下，提出一種以群組基因演算法為基礎的訂單批次啟發法，以減少訂單延遲時間。透過模擬實驗，將本研究所提之方法與基因演算法以及先到先服務法等過去傳統的訂單批量方法相互比較，並且進行各種不同參數實驗，結果顯示以群組基因演算法所發展的訂單批量方法明顯優於現存傳統方法。