物聯網（IoT）近年來已經成為全球最具影響力的發展之一。這一發展影響了許多企業適應並轉向發展線上商務或所謂的電子商務。其中一個案例，作為一個電子商務平台。亞馬遜提供了數百萬種產品的銷售。在2017年，亞馬遜提供了3.72億線上販售產品。面對如此多的產品，亞馬遜使用智動化揀貨系統（RMFS）作為他們的倉庫運作系統，大幅的改善原有的運作效率。
在這項研究中，使用NetLogo軟體平台建立了一個RMFS的模型。同時，建立了一個基於歷史訂單數據驅動的庫存分配決策，以最大化系統效能和吞吐率。
本研究由4部分組成，包括一個存貨分配階段與三個模擬階段。第一階段是根據歷史訂單數據和補貨效率計算出初始庫存水平。然後，依序進入了三個模擬階段，首先是SKU分散度。 SKU分散度是每個SKU的存儲容量和SKU在一個貨架內的多樣性之間的權衡，同時也影響到補貨的難度。不同程度的分散是通過改變貨架上的分格數量來實現的，用於尋找本研究中SKU分佈的最佳分散程度。
此外，下一階段的重點是不同類別的SKU在貨架上的比例。在這個階段有六個不同的組合。本研究的最後階段，定義了RMFS中每個貨架的補貨機會成本。在此基礎上，舊有的補貨策略得到了改進，因為更多的關鍵的SKU得到補貨，並基於機會成本選擇更有補貨效益的貨架來使補貨流程更加有效。
經過一系列的實驗，我們可以找到最佳的系統庫存分配和補貨策略，以此建立一個完整的決策過程。除此之外，補貨的機會成本也得到了定義。在實驗中也有一些發現。庫存決策過程中需要考慮訂單數據的特點，且單次訂購數量是庫存分配問題中的關鍵指標之一。同時，在研究中也再次證明了吞吐時間和揀貨次數有很強的正相關。

The Internet of Things (IoT) has become the most impactful development worldwide. This development influenced many businesses to adapt and shift to online business or so-called e-commerce. For example, as an e-commerce platform, Amazon offers millions of products for sale. In 2017, Amazon offered 372 million online products. With this amount of products, Amazon used the Robotic Mobile Fulfillment System (RMFS) as their warehouse system.
In this study, a simulation of the RMFS was built using NetLogo. Also, a historical order data-driven inventory allocation decision is established to maximize the system efficiency and the throughput rate.
This study consists of 4 parts, including three simulation stages. The first stage
is calculating the initial inventory level based on historical order data and replenishment efficiency. Then, a simulation experiment test was conducted on SKU dispersion. SKU dispersion is a trade-off between the storage capacity of each SKU and SKU diversity on one pod, and it also affects the difficulty of replenishment. The different degrees of dispersion are achieved by changing the number of slots on the pod, used to find the best degree of SKU distribution in this case.
Moreover, the next stage focuses more on the ratio of SKUs of different classes
on the pods. There are six different combinations in this stage. Finally, in the last
stage of this study, the replenishment opportunity cost of each pod in RMFS is
defined. Based on this, the previous replenishment policy is improved, for more
critical SKUs get replenished and pods selected to make the replenishment policy
more efficient.
After a series of experiments, we can find the best system inventory allocation
and replenishment policy based on historical order data to build a complete decision-making process. Other than that, replenishment opportunity cost has been defined, and some discoveries were made during the experiment. Order data features need to be considered in the inventory decision-making process, and unit order quantity is one of the critical indicators in the inventory allocation issue. At the same time, it is also proved once again in the study that throughput time and picking movement have a strong positive correlation.

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