本論文提出了一種新穎的演算法，以提升移動機器人揀貨系統(robotic mobile fulfillment system, RMFS)的揀貨效率。RMFS 通常應用於需要快速出貨的電子商務配送中心，以迅速完成揀貨。在傳統的人工配送中心，作業員需要在貨架間移動挑選訂單中的商品，而電子商務訂單所需的商品數量少但種類多，導致作業員的時間主要被移動佔據，從而降低揀貨效率。相比之下，在 RMFS 中，移動機器人將貨架運送到作業員所在的揀貨站進行揀貨。這種系統通過節省作業員的移動時間，大幅提高了揀貨效率。

本演算法通過估計在不同訂單分派與貨架選擇下的揀貨站吞吐量，以最大化配送中心的商品吞吐量為目標，聯合優化所有揀貨站的訂單分派(pick order assignment, POA)和貨架選擇(pick pod selection, PPS)問題。同時，透過貪婪演算法，在取得良好結果的同時，維持合理的運算時間。

通過考慮真實機器人的移動和補貨作業的RMFS模擬框架RAWSim-O，本演算法與實務中常用的由多個啟發式演算法所組成的循序演算法以及近期的一種同樣基於聯合優化POA和PPS問題的高效演算法進行了比較。模擬結果在五個不同大小的方形布局中進行，其貨架儲存位置的數量在728與3280個之間，貨架的數量均占所有位置的85%。模擬場景包含以10為間隔，從10到70個機器人，以及六種在100到5000種商品數量(stock keeping unit, SKU)。結果顯示，本演算法與其他演算法相比，在所有布局與場景中均有較高的物品吞吐量與較低的機器人總移動距離。在1848個儲存位置的中型佈局以及所有機器人數量與1000種商品數量的場景中，本演算法與循序演算法相比，可提升27%的物品吞吐量並減少35%的總移動距離。雖然其計算時間是循序演算法的三倍，但仍僅需高效演算法計算時間的1/6，且高效演算法在吞吐量或移動距離並未表現得更好。

總結來說，本論文提出的演算法不僅提高了揀貨效率，還具備較高的適用性和擴展性，為電子商務配送中心中的移動機器人揀貨系統提供了一個有效的解決方案。

In this thesis, a novel algorithm to improve the picking efficiency of robotic mobile fulfillment systems (RMFS) is proposed. RMFS are commonly used in e-commerce fulfillment centers, which often face short due times for orders. In traditional manual fulfillment centers, workers need to move between shelves to pick items for orders. The high variety of small orders in e-commerce results in workers spending most of their time traveling, thus decreasing picking efficiency. In contrast, RMFS use mobile robots to move shelves to picking stations, saving worker travel time and greatly enhancing efficiency.

The proposed algorithm aims to maximize the throughput rate of fulfillment centers by jointly optimizing pick order assignment (POA) and pick pod selection (PPS) in RMFS. It estimates the station throughput rate under different decisions for order assignment and pod selection. Utilizing a greedy algorithm, the proposed solution achieves good results while maintaining reasonable computational time.

Using the RAWSim-O simulation framework, which accurately models realistic robot movement and the replenishment process, the proposed algorithm is compared against a sequential algorithm that combines commonly used industry heuristics and a recent high-performance algorithm focusing on joint optimization of POA and PPS. Simulations were conducted across five square layouts ranging from 728 to 3,480 pod storage locations, with 85% pod occupancy. Scenarios involve 10 to 70 robots in increments of 10 and six different stock keeping unit (SKU) levels ranging from 100 to 5,000. Results show that the proposed algorithm consistently outperforms the others, achieving higher item throughput rates and reducing total robot travel distances across all layouts and scenarios. In the medium-sized layout with 1,848 pod storage locations and scenarios involving all robot numbers and 1,000 SKUs, it delivers a 27% increase in throughput and a 35% reduction in distance, though with three times the computational time of the sequential algorithm. However, it still requires only 1/6 of the time needed by the high-performance algorithm, which does not achieve better throughput or distance results.

In summary, this thesis presents an algorithm that not only increases picking efficiency but also offers high applicability and scalability, providing an effective solution for e-commerce fulfillment centers.

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