隨著電子商務的蓬勃發展，對於貨物及時運輸的要求越來越高，而貨物在配送的過程中的旅行時間以及需求量可能會隨著環境變化而發生改變，像是道路擁塞導致旅行時間上升或式特殊節日下的需求上升等等，故在不確定情況下該如何在客戶期望之時間窗(Time window)完成配送是很重要的議題。因此，本研究探討在需求以及旅行時間不確定下卡車結合無人機配送問題，並且建立卡車結合無人機之確定性和不確定性的數學模型，而為了處理大規模問題，本研究發展自適應變鄰域搜索演算法來求解，最後使用蒙地卡羅模擬來分析穩健解的穩健性。結果顯示，在穩健解下完成配送時間普遍上升，將無人機納入考量的結果表明，15、30以及50個需求規模下，卡車無人機分開配送分別能夠降低15.12%、17.12%以及26.97%的旅行時間，而卡車無人機一起配送有較大的下降幅度，分別能夠降低17.86%、23.2%以及38.51%的旅行時間；穩健性分析結果顯示，卡車無人機一起配送的模式的穩健解具有最高的穩健性，而卡車無人機分開配送的模式在大規模需求下的穩健性較低，上述的分析結果提供了相關業者決策卡車以及無人機協作模式之參考。

With the development of e-commerce, there is an increasing demand for timely transportation. However, during the delivery process, the travel time and demand for goods may change due to various uncertainties. Therefore, the use of a combination of trucks and drones improves transportation efficiency. In this study, we will explore the truck and drone delivery problem under uncertain demand and travel time. We will establish deterministic and robust mathematical models for truck and drone integration, and develop an adaptive variable neighborhood search algorithm to solve large-scale problems. Finally, Monte Carlo simulations will be conducted to analyze the robustness of robust solutions. The results show that total travel time increases under uncertain conditions, and indicate that for demand scales of 15, 30, and 50, the separated delivery of trucks and drones can reduce travel time by 15.12%, 17.12%, and 26.97%, the combined delivery of trucks and drones has a greater decline, which can reduce the travel time by 17.86%, 23.2% and 38.51% respectively. The robustness analysis reveals that the combined delivery mode of trucks and drones exhibits the highest level of robustness, while the separated delivery mode shows lower robustness for large-scale demands. These results provide valuable insights for relevant stakeholders regarding the collaboration between trucks and drones.

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