隨著物流倉儲產業逐漸規模化，自動化設備被廣泛採用以提升作業效率，像是在揀貨作業中，導入自動揀貨系統能夠提高八倍的作業效率及提升訂單準確度達99%。然而，在倉儲出貨端的疊棧作業仍嚴重仰賴人力，依照過往經驗的判斷將箱子堆疊至棧板上，容易造成棧板空間未達到有效利用及缺乏作業效率。若能針對疊棧作業開發啟發式演算法並結合自動化設備(如:機械手臂)，便可實現箱體疊棧的智動化。本研究考慮國內物流業者常用之兩種棧板尺寸，解決多尺寸棧板限制下的三維配銷商棧板疊棧問題(Three-Dimensional Distributor’s Pallet Loading Problem, 3D-DPLP)，將多尺寸箱體裝載到棧板上，並在箱體堆疊穩定下允許箱體最多懸掛30%面積，旨在開發啟發式演算法找出多尺寸箱體的堆疊方案，目標最小化棧板使用數量。
首先，使用兩階段的方法，利用快速堆疊演算法獲得初始解，再以模擬退火為基底的演算法進一步優化初始解，以尋找箱體裝載方案，最小化棧板數量。本研究提出了改善及混合的啟發式演算法，將改善模擬退火和差分進化演算法結合，以加快收斂速度並增強可行解的搜索能力，來提升求解的品質。最後，使用實際的案例進行實驗與比較，研究結果顯示所提出的演算法可有效地解決該問題，為物流倉儲中的智動化疊棧作業提供有價值的見解。

Intelligent operation in logistic warehousing plays a crucial role in optimizing efficiency and enhancing productivity. As logistics operations become increasingly complex and scaled, automation has been widely adopted to streamline processes such as automatic picking, resulting in improved efficiency and order accuracy. However, the stacking operation in warehousing still heavily relies on manual labor and experience to load the boxes on the pallet. By integrating metaheuristic algorithms and automated equipment, such as robotic arms, the intelligence of pallet loading can be realized. This research focuses on the Three-Dimensional Distributor’s Pallet Loading Problem (3D-DPLP) with multiple-size pallets. Our study aims to load heterogeneous boxes onto multi-size pallets, allowing for overhang, while considering practical constraints. We developed algorithms that can find the loading patterns to minimize the number of required pallets.
First, we use the two-phase approach, an initial solution is created by employing the fast loading heuristic in this approach. Subsequently, a simulated annealing (SA)-based algorithm is utilized to refine the solution by searching for the loading pattern. Furthermore, we propose improved and hybrid metaheuristic algorithms, integrating SA and Differential Evaluation, to accelerate convergence and enhance exploration capability. Through comprehensive experimentation with real-world logistics companies, the results demonstrate the effectiveness of our proposed algorithms in solving the 3D-DPLP.

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