物流中心之最佳化揀貨策略; Optimizing Order Picking in Distribution Centers

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题名:	物流中心之最佳化揀貨策略;Optimizing Order Picking in Distribution Centers
作者:	蘇騰昇;Teng-Sheng Su
贡献者:	工業管理研究所
关键词:	揀貨路徑規劃;物流中心;單元負荷;訂單切割;貨品揀取與處理;種子與配合訂單選取;訂單批次化;order-batching;route planning;order-splitting;order-picking;distribution center
日期:	2002-07-08
上传时间:	2009-09-22 14:11:24 (UTC+8)
出版者:	國立中央大學圖書館
摘要:	在這一波「電子商務」快速發展後，利用網路進行商業交易的活動日益明顯，顧客對於消費產品的需要，已經可以透過網路的便利性，隨時隨地取得所欲訂購的貨品，並且在國人對於生鮮貨品的衛生、安全、健康觀念之下，使這些日常生活主食品的購買方式，已經轉變為經由零售商的倉儲、運輸與配送的作業處理，加上時下便利商店已經快速成長，提供多項貨品宅配服務，都促使消費者對貨品能及時送達與新鮮性要求，有逐年增加之趨勢。　　本研究將就貨品揀取時「揀貨箱pick and sort」、「訂單批次化處理」與「訂單分割」問題作探討，分為四個部份進行：首先決定在固定期間內揀取訂單（Order Pool），第二部份開始以四種種子訂單選取法則（RD隨機策略、LNVL最多揀取點策略、LNI最多品項策略、MRV最少剩餘揀貨箱容積策略），與四種配合訂單選取法則（MAD最少增加距離、MCVL最多共同揀取點、MCI最多共同品項、SNAA最少增加走道數）作訂單批次的搭配組合。第三部分為做揀取路徑的規劃，在第一階段利用三種啟發式演算法（LG最大間隙策略、NCRI最接近矩形中心插入、MTLI最短旅行迴圈插入）找到一個不錯的初始解，第二階段裡再利用模擬退火法（Simulated Annealing; SA）最佳化技術改善第一階段所得到的初解。第四部分是處理在第二部分訂單批次化會發生的訂單切割，以三種訂單切割選取法則（RD隨機策略、MDPD最近P/D距離、MIV最大品項容積）進行切割訂單。最後，本研究利用Visual Basic 6.0與Arena作模擬實驗，來評估不同揀貨路徑規劃下，種子訂單選取法則，搭配配合訂單選取法則與切割法則的各個實驗組合績效表現。除此之外，本研究是在既有的揀取問題上再作深入的探討，有其應用價值與實際性，期望在現今以網路購物的環境愈趨完整，提供未來其他研究的參考。 Distribution centers have been playing an important role in delivering goods from manufacturers to the end users. The development of computer, information, and communication technologies has made everything go faster than it used to. For a distribution center, this means more frequent and faster delivery of goods to its customers. For years, industrial engineers have been looking for ways to optimize the order-picking operations in a distribution center. It has been shown by researchers that the cost of order picking comprises about 50% or more of the total operation cost of a distribution center (Hwang et al., 1988). And, it has been shown that a distribution center can boost its competitive advantages if its order-picking cost can be reduced （Ballou, 1992, Morabito et al., 2000）. The purpose of this paper is to look for way to make the order-picking operations more efficient and effective. Problems investigated in this paper include order-batching problem, route-planning problem, and the order-splitting problem. For each problem, several solutions will be proposed. For order-batching problem, four seed-order selection algorithms (i.e., RD, LNVL, LNI, MRV) and four accompanying-order selection algorithms (i.e., MAD, MCVL, MCI, SNAA) will be proposed and investigated. They will make up 16 different combinations of seed-order selection algorithm and accompanying-order selection algorithm. As for the route-planning problem, four methods (i.e., NCGI, LG, NCRI, MTLI) will be investigated. The first method is obtained from Ho and Lo (2001), while the second one is called the largest-gap algorithm that was originally proposed by (Hall, 1993). And, the last two methods are proposed by us. It is a two-stage approach that finds an initial route first and then uses a heuristic optimization technique to improve the result. Finally, for the order-splitting problem, three algorithms (i.e., RD, MDPD, MIV) will be proposed and investigated. An order will be divided into two orders if its required space is greater than the remaining space in the picking cart. To understand the performance of the above algorithms and methods, simulation experiments (using Arena) will be conducted. The simulation results not only can tell us which algorithm can perform better than others but also can help us understand their relationships. For example, one may see how one seed-order-selection algorithm is affected by another accompanying-order selection algorithm. It is our hope that the knowledge learned from this research can help us find the best solution for each of the above problems so that more effective and efficient order-picking operations can be obtained.
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