這項研究的著重在於優化智動化揀貨系統（RMFS），以實現成本降低。透過應用演算法來改善裝載過程，並使用多元啟發式來確定每個庫存保管單位（SKU）的最佳位置。
我們在這項研究中解決的一個主要問題是貨櫃裝載問題（CLP），這是一個存在了許多年的NP-Hard問題。我們採取的方法有兩個面向，首先，最大化空間利用率，從而減少插槽的浪費；其次，根據整體訂單數量為每個SKU指定策略位置。這兩步方法使我們能夠計算出有效運作所需的POD（隨選移動儲存單元）的總數。
此研究是通過融合現實世界的觀察並調整實證數據來模擬我們的實驗的實際情境。此實驗的限制也反映了現實世界的約束，包括旋轉方向和不允許在同一插槽中放置不同的SKU。這些因素使我們的研究更能反映實際倉庫環境所面臨的真實挑戰。
這項研究的結果顯示，我們的方法在確定滿足訂單要求所需的POD數量方面是有效的，從而幫助倉庫將多餘的POD放置的成本降至最低。通過提供一種準確預測所需POD數量的方法，我們的研究為改善倉庫效率和降低運營成本的廣泛論述做出了貢獻。我們相信，這些見解及其相關策略可為希望優化其RMFS操作並實現顯著成本降低的企業提供寶貴的資源。

This research focuses on optimizing Robotic Mobile Fulfillment Systems (RMFS) for cost reduction. The methodology enhances the loading process by applying advanced algorithms and metaheuristics to identify optimal locations for each Stock Keeping Unit (SKU).
One of the central issues we address in this study is the Container Loading Problem (CLP), an NP-Hard problem that has persisted for many years. The approach undertaken here is twofold: firstly, to maximize space utilization, thereby reducing the wastage of slots, and secondly, to strategically assign locations for each SKU based on the overall order quantity. This two-step methodology enables us to calculate the total number of PODs (Portable On Demand storage units) required for efficient operations.
This research was conducted by incorporating real-world observations and adjusting empirical data to simulate a practical context for our experiment. This experiment’s constraints also reflect real-world limitations, including the direction of rotation and the restriction against placing different SKUs in the same slot. These factors make our study more reflective of the challenges faced in warehouse environments.
This research demonstrates that our method effectively identifies the accurate number of PODs required to fulfill the order requirements, thus helping warehouses minimize the cost of surplus POD placements. By providing a means to anticipate the required number of PODs accurately, our study contributes to the wider discourse on improving warehouse efficiency and decreasing operational costs. We believe these insights and the associated strategies presented here can be a valuable resource for businesses looking to optimize their RMFS operations and achieve significant cost reductions.

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