| 摘要: | 近年來隨著科技越趨進步，網路及智慧型手機越來越普及於社會大眾，大部分人的生活與網路已經越來越緊密，消費模式也一部分慢慢地與以前不同，不再只有到現場採購這個選項，在電腦及智慧型手機上進行購物的比例越來越高，消費需求也產生了改變，逐漸轉換為多樣、少量，甚至是客製化，使得在了解顧客喜好以及有效率的解決顧客問題等各種面向，在現在這個大數據與物聯網的時代變得更加重要。
而當消費者從網路的購物平台下單後，顧客無不希望能盡快地收到採購的商品，而另一方面對於物流公司而言，收到顧客的訂單之後需要的就是立即統整顧客資料，以最快的速度出貨給消費者，而這時候物流中心的出貨效率就變得萬分重要。本研究以亞馬遜物流中心的Kiva系統為主，因為此為目前現有物流中心最有效率的系統之一，Kiva系統最大的特色為利用無人搬運車到指定的揀貨區將訂單上的商品送到揀貨人員手上，以節省傳統方式中揀貨人員走動的時間，進而達到縮短出貨時間的目的。
本研究延續楊卓翰（2017）的研究針對亞馬遜Kiva系統流程中的兩個問題做出探討，分別為揀貨工作站挑選問題以及訂單選取問題，利用模擬軟體Arena建構出類似亞馬遜的物流中心環境，並探討改變Kiva機器人的方式會產生什麼影響，找出在不同績效及不同Kiva機器人數量下表現最好的法則及參數組合，以達到提升物流中心效率的目的。
;In recent years, with the advancement of technology, Internet and smart phones have become more and more popular among the public. Most people′s lives and networks are getting closer and closer, and consumption patterns are gradually different from the past. Only by purchasing this option on-site, the proportion of shopping on computers and smart phones is getting higher and higher, consumer demand has also changed, and it has gradually changed into diverse, small, and even customized, making it possible to understand customer preferences and Efficient solutions to customer issues and other aspects have become more important in the era of big data and the Internet of Things.
When the consumer places an order from the online shopping platform, the customer wants to receive the purchased product as soon as possible, and on the other hand, for the logistics company, after receiving the customer′s order, it is necessary to immediately integrate the customer. The data is shipped to consumers at the fastest speed, and at this time the logistics center′s shipping efficiency becomes extremely important. This research is based on the Kiva system of the Amazon Logistics Center. Because this is one of the most efficient systems in the existing logistics center, the biggest feature of the Kiva system is to use the unmanned van to deliver the goods on the order to the designated picking area. In the hands of the pickers, it saves the time for the pickers to move in the traditional way, thus achieving the purpose of shortening the shipping time.
This study continues Yang Zhuohan′s (2017) research to address two issues in the Amazon Kiva system process, selecting picking problems for the picking station and order selection issues, using the simulation software Arena to construct a logistics center environment similar to Amazon, and explore What is the impact of changing the way Kiva robots are, finding the best-performing rules and combinations of parameters for different performances and different Kiva robots to improve the efficiency of the logistics center. |
