電子商務已經改變了全球的零售環境，統計到2022年底，由於COVID-19的影響，線上銷售已超過5.7兆美元。隨著線上購物的受歡迎程度提高，高效的倉儲變得越來越重要。傳統的倉庫經常無法跟上現代的需求，導致效率低下。但是，智動化揀貨系統（RMFS）這種新型揀貨系統中機器人運送商品確實提供了一個解決方案。由亞馬遜和阿里巴巴等行業巨頭採用的RMFS通過減少人工、提高揀貨率並加快訂單履行速度，比傳統設置提高了50%的生產力。
此研究基於真實的RMFS資料集，並建立了三個不同規模和複雜性的訂單資料集。然後在所提議的關聯規則（AR）中使用這些資料集，該規則集成了基因演算法（GA）和粒子群最佳化演算法（PSO）方法。研究的實證結果顯示，在評估“中等訂單”資料集時，PSO方法優於GA，獲得更高的每個貨架的平均支持值。對於“大型訂單”資料集，PSO方法產生的平均支持值與GA大致相同，但PSO稍占優勢。
通過檢查實驗結果，GA在“小型訂單”資料集中優於PSO，產生更高的每個貨架的平均支持值。相反，對於“中等訂單”資料集，PSO效果更佳，而在“大型訂單”場景中，PSO和GA產生的結果幾乎相同，PSO略具優勢。此外，從計算速度的角度看，由於其更簡單的突變機制，GA速度較快，而PSO在搜索空間內進行複雜的粒子調整則導致了更長的處理時間。

E-commerce has transformed the global retail environment, with online sales expected to exceed $5.7 trillion by 2022, thanks partly to the COVID-19 lockdowns. As the popularity of online shopping grows, efficient warehousing becomes increasingly important. Traditional warehouses frequently fail to meet modern needs, resulting in inefficiencies. The Robotic Mobile Fulfillment System (RMFS), an automated system in which robots deliver merchandise, does, however, provide a solution. RMFS, adopted by industry titans such as Amazon and Alibaba, increases productivity by decreasing manual labor, improving pick rates, and expediting order fulfillment by 50% compared to traditional setups.
This study is grounded on authentic RMFS datasets and constructs three-order datasets of varying scales and complexities. These datasets are then utilized in the proposed Associative Rule (AR) integrated with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methodologies. The study’s empirical findings show that the PSO approach outperforms GA while evaluating the “medium order” dataset, attaining a higher average support value per pod. For the “large order” dataset, the PSO approach produces approximately the same average support value per pod as GA. However, PSO has a little edge.
Upon examining experimental results, the GA outperformed PSO in the “small order” dataset, yielding higher average support values per pod. Conversely, for the “medium order” dataset, PSO was more effective, while in the “large order” scenario, PSO and GA delivered nearly identical results, with PSO having a marginal edge. Additionally, in terms of computation speed, GA was faster due to its simpler mutation mechanism, whereas PSO’s intricate particle adjustments within the search space led to extended processing times.

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