粒子群演算法應用於物流中心批次人工揀貨之研究

Applying Particle Swarm Algorithm to the Batch Order Policies in Manual Picking System of Distribution Center

羅婉珍(Wan-Chen Lo)

指導教授 ： 胡黃德

元智大學/工程學院/工業工程與管理學系/碩士(2009年)

摘要

在物流中心內部所涵蓋的作業範圍裡，揀貨作業是其中一項重要且繁雜的工作。揀貨作業時間佔整個物流中心作業時間比為30~40%，因此若能妥善規劃揀貨作業將能有效降低物流中心之營運成本。本研究主要在探討人工揀貨作業在物流中心之最佳化。本研究利用隨機批量法及最少走道批量法兩種方法搭配穿越策略及最大間隙策略兩種揀貨路徑法產生初始解。為了決定較佳之揀貨策略，本研究利用粒子群演算法進行改善，以找尋揀貨路徑之最佳解。本研究使用 MINITAB軟體分析不同的初始解方法、揀貨策略及載重限制其對物流中心批次人工揀貨之影響。實驗分析結果顯示，不同之初始解方法對於揀貨距離並無顯著影響。而不同之揀貨策略與載重限制在本研究中對於揀貨距離則都有顯著之影響。其中在不同之揀貨策略中，以最大間隙策略所得之揀貨距離最短。

關鍵字

物流中心 ； 揀貨作業 ； 訂單批次揀取 ； 粒子群演算法 ； 揀貨距離

並列摘要

The order picking is one of the important and complex operations in the distribution center. The order picking time accounts for 30%~40% of total operation time in distribution center. Hence, a well planned order picking strategy can reduce the operating cost of the distribution center effectively. In this study, the optimal order picking strategies were evaluated. We used two order batching methods (Random Batching and Least Aisle Batching) combined with two route planning methods (Traversal Strategy and Largest Gap Strategy) to determine the initial solutions. Then, we used Particle Swarm Optimization (PSO) to improve the initial solution and then tried to achieve the optimum solution. Finally, we utilized the MINITAB statistical software to test t the relationships among initial solutions, order picking strategies and the load limit of the picking cart. The results showed that the initial solutions had no statistical significance on the order picking distance. While order picking strategies and load limit had more influence on the order picking distance. We also found that the largest gap strategy had best performance in these simulation test problems.

並列關鍵字

Distribution center ； Order Picking ； Order Batching ； Particle Swarm Optimization ； Order Picking Distance

參考文獻

周士俊，「塔布搜尋法應用於物流中心人工揀貨區揀貨問題」，元智大學工業工程與管理研究所，碩士論文，2000。

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