近年來，由於全球化的市場、貿易的自由化、產品生命週期的縮短、供應鏈管理觀念的興起、顧客服務水準的提升等因素，物流已經成為全球企業所關注的焦點。此外，越來越多消費者的需求特性轉變為少量多樣，零售商為了因應此特性勢必要減少其商品庫存的數量，而物流業者必須以少量、多樣、多頻率的配送方式來滿足消費者與零售商的需求，因此將傳統多層級的配銷通路由物流中心取代。其中，揀貨作業是最重要的環節，也是滿足顧客期望的關鍵，揀貨作業是勞力密集且花費成本高的活動，估計揀貨作業佔物流中心的總作業成本約達55％。 本研究將會提出相鄰區域揀貨員可以互助合作的揀貨策略。藉由揀貨員之間相互幫忙，進而避免發生因工作量不平衡累積，造成有些揀貨員過於忙碌，而導致其它人員閒置或揀貨箱阻塞的問題，以提升整體揀貨系統的揀貨效率。本研究透過Arena11.0軟體建構模擬環境，再以SPSS 18.0進行實驗數據分析，最後找出完成訂單的總揀貨系統時間為最小之最佳因子組合。研究結果顯示，以BT與NBT為忙碌程度界限下之搭配合作策略的最佳績效，比起傳統順序式分區的總揀貨系統時間各降低31.281%與31.494%。 In recent years; because of globalization, trade liberalization, the shortening of product life cycle, the emergence of supply chain management concept and the enhancement of customer service level, logistics has become an important issue to global business. However, there are more and more customers whose demand becomes low-volume and high-diversified. In response to this demand characteristic, volume of product inventory should be reduced by retailers, and low-volume, high-diversity, high-frequency delivery practice should be designed into retailers’ logistic system to satisfy customers and retailers. Therefore, the traditional multi-level distribution channel will be replaced by distribution center (DC). And many activities are carried out in the distribution center (DC). Among them, order picking is the critical one which leads to customer satisfaction. Moreover, order picking is labor intensive and high-cost activity which may consume as much as 55% of overall activity cost. In traditional picking system, it can be observed that some pickers are idle while others are busy and some picking zones are blocked due to the excess of queue. In this study, we propose cooperation concepts and rules which make pickers in adjacent zones to help each other possible when some conditions are held. It can eliminate the load imbalance between pickers in different zones. To analyze the efficiency and effectiveness of those concepts and rules, the Arena 11.0 and SPSS 18.0 are adopted to construct the simulation model and to do the statistical analysis. Finally, best combinations under different conditions which bring the minimum total system time are found after the analysis of experiment results. The statistical analysis showed that the combination of (BT3*HBZ*HFB1*B3) increase performance by 31.281%, and the combination of (B4*NBT0*HBZ*HBF1) increase performance by 31.494%.
