The application of drone delivery networks in logistics represents a transformative step toward increasing the efficiency and accessibility of last-mile delivery services. Multi-drone systems offer significant potential for reducing delivery times, improving resource utilization, and enhancing the reach to remote or otherwise inaccessible areas such as mountainous regions or congested urban environments. As demand for faster, more efficient delivery services continues to rise, multi-drone networks have become an essential tool in modern logistics. However, their full potential of these systems can only be realized through effective route optimization, which must account for real-time environmental changes, such as traffic, weather, and terrain complexity.
无人机配送网络在物流中的应用代表着朝着提高最后一英里配送服务的效率和可达性迈出了变革性的一步。多无人机系统在缩短配送时间、提高资源利用率以及扩大对偏远地区或其他难以到达的地区（如山区或拥挤的城市环境）的覆盖范围方面具有巨大潜力。随着对更快、更高效的配送服务的需求不断增长，多无人机网络已成为现代物流中必不可少的工具。然而，这些系统的全部潜力只有通过有效的路线优化才能实现，而这必须考虑到交通、天气和地形复杂性等实时环境变化。

Efficient route optimization remains a crucial challenge in ensuring the success of multi-drone logistics networks. Traditional static route planning methods struggle to accommodate dynamic changes in traffic patterns, sudden weather shifts, or varying terrain features, especially in complex environments like urban centers or mountainous regions. Without dynamic decision-making capabilities, these systems often result in suboptimal routes, leading to increased delivery times, higher energy consumption, and elevated operational costs. This shortfall is particularly pronounced in multi-drone networks, where coordination between drones is critical to maximizing efficiency across all delivery points.
高效的路线优化仍然是确保多无人机物流网络成功的关键挑战。传统的静态路线规划方法难以适应交通模式的动态变化、突然的天气变化或不同的地形特征，尤其是在城市中心或山区等复杂环境中。如果没有动态决策能力，这些系统通常会导致路线不理想，从而导致配送时间增加、能源消耗增加和运营成本上升。这种不足在多无人机网络中尤为明显，因为无人机之间的协调对于最大限度地提高所有配送点的效率至关重要。

Given these challenges, there is a pressing need for a system that can dynamically adjust drone delivery routes based on real-time conditions. The integration of big data and genetic algorithms (GA) offers a promising solution. Genetic algorithms are uniquely suited to solving complex, multi-objective optimization problems, such as balancing delivery time and energy efficiency. By incorporating real-time traffic, weather, and terrain data, a GA can dynamically adjust drone routes to ensure optimal performance in varying conditions. This approach allows drones to adapt to sudden environmental changes, thereby maintaining operational efficiency and minimizing energy consumption.
鉴于这些挑战，迫切需要一个能够根据实时情况动态调整无人机送货路线的系统。大数据和遗传算法 (GA) 的结合提供了一个有希望的解决方案。遗传算法特别适合解决复杂的多目标优化问题，例如平衡送货时间和能源效率。通过整合实时交通、天气和地形数据，GA 可以动态调整无人机路线，以确保在不同条件下实现最佳性能。这种方法使无人机能够适应突然的环境变化，从而保持运营效率并最大限度地降低能耗。

The primary contribution of this research lies in the development of a dynamic route optimization framework that integrates big data with genetic algorithms. This system continuously processes real-time environmental inputs to adjust delivery routes in complex environments, optimizing both delivery speed and energy consumption. By leveraging genetic algorithms, the framework can manage multiple drones simultaneously, ensuring that routes are dynamically optimized as new data is received. Through comprehensive simulations in both urban and mountainous terrains, this study demonstrates the scalability and effectiveness of the proposed system in real-world applications.
本研究的主要贡献在于开发了一种将大数据与遗传算法相结合的动态路线优化框架。该系统不断处理实时环境输入，以调整复杂环境中的配送路线，从而优化配送速度和能耗。通过利用遗传算法，该框架可以同时管理多架无人机，确保在收到新数据时动态优化路线。通过在城市和山区地形中进行全面模拟，本研究证明了所提出的系统在实际应用中的可扩展性和有效性。

Research on the application of genetic algorithms (GA) in route optimization and drone delivery systems has seen significant progress, particularly since 2020, building on earlier foundational studies. Savuran and Karakaya (2016) demonstrated the effectiveness of using genetic algorithms to optimize UAV routes, reducing both delivery time and energy consumption ^[1]. This foundational work paved the way for further developments in GA-based route optimization. Campbell et al. (2017) explored truck-drone collaboration and applied genetic algorithms to reduce delivery time and optimize routes, highlighting the importance of dynamic route planning ^[2].
遗传算法 (GA) 在路线优化和无人机配送系统中的应用研究取得了重大进展，尤其是在 2020 年以来，这得益于早期的基础研究。Savuran 和 Karakaya (2016) 证明了使用遗传算法优化无人机路线的有效性，既减少了配送时间，又减少了能源消耗^[1] 。这项基础工作为基于 GA 的路线优化的进一步发展铺平了道路。Campbell 等人 (2017) 探索了卡车与无人机的协作，并应用遗传算法来缩短配送时间和优化路线，强调了动态路线规划的重要性^[2] 。

Following these early works, Peng et al. (2019) introduced a hybrid GA model for vehicle-assisted multi-drone delivery, achieving enhanced delivery efficiency through heuristic optimization. Post-2020, Katoch et al. (2021) reviewed the applications of GAs in multi-objective optimization, particularly in logistics and route planning. Chen et al. (2021) advanced GA research by integrating machine learning to improve energy efficiency in complex terrains for drone deliveries. Wang et al. (2023) proposed a rescheduling-based GA to optimize dynamic drone delivery routes, enabling real-time adaptation to changes.
在这些早期研究之后，Peng 等人 (2019) 引入了一种用于车辆辅助多无人机配送的混合 GA 模型，通过启发式优化提高了配送效率。2020 年后，Katoch 等人 (2021) 回顾了 GA 在多目标优化中的应用，特别是在物流和路线规划中。Chen 等人 (2021) 通过整合机器学习来推进 GA 研究，以提高无人机配送在复杂地形中的能源效率。Wang 等人 (2023) 提出了一种基于重新调度的 GA 来优化动态无人机配送路线，从而实现实时适应变化。

In the context of big data in logistics, Dong et al. (2021) emphasized how big data analytics can transform logistics by enabling real-time adjustments in freight transportation. Huang and Liu (2022) expanded on this by integrating big data and IoT to improve decision-making for supply chains, particularly in drone delivery networks. Sabet and Farooq (2022) applied big data and GAs to address the Green Vehicle Routing Problem (VRP), focusing on sustainability in logistics.
在物流大数据的背景下，Dong 等人 (2021) 强调了大数据分析如何通过实时调整货运来改变物流。Huang 和 Liu (2022) 通过整合大数据和物联网来改进供应链决策，特别是在无人机配送网络中，从而进一步扩展了这一概念。Sabet 和 Farooq (2022) 应用大数据和 GA 来解决绿色车辆路径问题 (VRP)，重点关注物流的可持续性。

These studies strongly support the present research, which integrates genetic algorithms with big data for dynamic multi-drone route optimization. Early work by Savuran and Karakaya (2016) laid the groundwork for GA-based UAV route planning, with Campbell et al. (2017) and Peng et al. (2019) contributing to truck-drone coordination and multi-drone delivery optimization. Post-2020 research by Katoch et al. (2021) and Wang et al. (2023) focused on real-time adaptability, aligning with this study’s emphasis on dynamic optimization. Dong et al. (2021) and Huang and Liu (2022) emphasized the importance of real-time data, while Sabet and Farooq (2022) highlighted the role of sustainability, all reinforcing the potential of GA and big data to improve multi-drone logistics efficiency.
这些研究有力地支持了本研究，该研究将遗传算法与大数据相结合，以实现动态多无人机路线优化。 Savuran 和 Karakaya (2016) 的早期工作为基于 GA 的无人机路线规划奠定了基础，Campbell 等人 (2017) 和 Peng 等人 (2019) 为卡车无人机协调和多无人机配送优化做出了贡献。 Katoch 等人 (2021) 和 Wang 等人 (2023) 在 2020 年后的研究侧重于实时适应性，与本研究对动态优化的强调一致。 Dong 等人 (2021) 和 Huang 和 Liu (2022) 强调了实时数据的重要性，而 Sabet 和 Farooq (2022) 强调了可持续性的作用，所有这些都加强了 GA 和大数据在提高多无人机物流效率方面的潜力。

The research methodology presented in this thesis offers significant improvements and innovations in the field of multi-drone delivery route optimization. By integrating genetic algorithms with big data, this approach allows for real-time dynamic route adjustments, significantly enhancing the system's adaptability. One of the key innovations is the ability to continuously process real-time traffic, weather, and environmental data, which enables the genetic algorithm to optimize delivery times and minimize energy consumption. Unlike traditional static route planning methods, this methodology allows for real-time avoidance of congested areas, high-energy terrain, and adverse weather conditions, resulting in faster and more energy-efficient deliveries. Additionally, the system's scalability is evident in high-demand scenarios, where tasks are successfully distributed across multiple drones without compromising performance. This highlights the algorithm's capability to manage substantial increases in delivery demand, making it well-suited for both urban and rural logistics challenges. Moreover, the focus on energy optimization ensures that the genetic algorithm minimizes energy consumption even in difficult terrains or low-battery conditions, extending the operational range of drones and reducing overall operational costs.These improvements make this methodology a robust and scalable solution for modern logistics.
本论文提出的研究方法在多无人机配送路线优化领域提供了重大改进和创新。通过将遗传算法与大数据相结合，这种方法可以实现实时动态路线调整，从而显著提高系统的适应性。关键创新之一是能够持续处理实时交通、天气和环境数据，这使遗传算法能够优化配送时间并最大限度地降低能耗。与传统的静态路线规划方法不同，这种方法可以实时避开拥堵区域、高能地形和恶劣天气条件，从而实现更快、更节能的配送。此外，该系统的可扩展性在高需求场景中显而易见，在这些场景中，任务可以成功地分布在多架无人机上，而不会影响性能。这凸显了该算法管理配送需求大幅增加的能力，使其非常适合应对城市和农村的物流挑战。此外，对能源优化的关注确保了遗传算法即使在困难的地形或电池电量不足的情况下也能最大限度地降低能源消耗，扩大无人机的运行范围并降低总体运营成本。这些改进使该方法成为现代物流的强大且可扩展的解决方案。

However, the research methodology is not without limitations. Computational complexity poses a challenge, as the genetic algorithm requires considerable computational power to process large-scale data inputs, especially as the number of drones, delivery points, and real-time data sources increases. This complexity could limit the method's applicability in large-scale logistics operations with thousands of delivery points, where computational resources may become a bottleneck. Furthermore, the system's success is heavily dependent on the availability and accuracy of real-time data. In regions where traffic or weather data is unreliable or insufficient, the genetic algorithm's ability to optimize routes in real-time may be compromised, leading to suboptimal performance. This reliance on high-quality data poses challenges for implementation in less-developed areas or regions with poor data infrastructure. Additionally, while the methodology excels in handling predefined scenarios, unexpected real-world conditions or regulatory changes may require further adaptability beyond what is currently embedded in the genetic algorithm. These limitations point to the need for further refinement and consideration in real-world implementations, particularly in enhancing the system's robustness and computational efficiency^[6].
然而，该研究方法并非没有局限性。计算复杂性带来了挑战，因为遗传算法需要相当大的计算能力来处理大规模数据输入，尤其是随着无人机、交付点和实时数据源数量的增加。这种复杂性可能会限制该方法在具有数千个交付点的大规模物流业务中的适用性，在这些业务中，计算资源可能成为瓶颈。此外，该系统的成功在很大程度上取决于实时数据的可用性和准确性。在交通或天气数据不可靠或不足的地区，遗传算法实时优化路线的能力可能会受到影响，导致性能不佳。这种对高质量数据的依赖对在欠发达地区或数据基础设施较差的地区实施带来了挑战。此外，虽然该方法在处理预定义场景方面表现出色，但意外的现实条件或监管变化可能需要超出遗传算法目前嵌入的范围的进一步适应性。这些局限性表明需要在实际实现中进一步完善和考虑，特别是在增强系统的鲁棒性和计算效率方面^[6] 。

This research has demonstrated that the integration of genetic algorithms (GA) and big data is a highly effective approach for optimizing multi-drone delivery routes. The system's ability to process real-time traffic, weather, and terrain data allows it to adapt swiftly to changing conditions, enhancing delivery efficiency and minimizing energy consumption. By applying GA, the system dynamically adjusted routes across varying scenarios, showing notable improvements in performance, especially in high-demand or challenging environments. The results confirm that this method is not only robust but also scalable, capable of managing both complex terrains and fluctuating operational demands. This approach presents a significant advancement in logistics, offering solutions for improving delivery times while reducing operational costs through energy-efficient drone routing.
这项研究表明，遗传算法 (GA) 与大数据的结合是优化多无人机配送路线的一种高效方法。该系统能够处理实时交通、天气和地形数据，因此能够迅速适应不断变化的条件，提高配送效率并最大限度地降低能耗。通过应用 GA，系统可以动态调整不同场景中的路线，性能显著提高，尤其是在高需求或具有挑战性的环境中。结果证实，该方法不仅稳健，而且可扩展，能够管理复杂地形和波动的运营需求。这种方法代表了物流领域的重大进步，通过节能的无人机路线提供解决方案，缩短配送时间，同时降低运营成本。

Further development of this system should focus on several critical areas to extend its applicability and efficiency. Machine learning models could be incorporated to predict traffic patterns, weather shifts, and demand surges, allowing the system to proactively adjust routes before disruptions occur, thus enhancing both resilience and efficiency. Additionally, expanding the model to operate in more diverse terrains, such as coastal and desert regions where wind or sandstorms may pose operational challenges, would increase its versatility. Enhancing battery management through predictive usage models and more efficient recharging strategies could also further optimize drone operations in low-battery conditions. Moreover, as regulatory environments evolve, future work should focus on creating flexible algorithms that can seamlessly adapt to updated airspace restrictions, ensuring compliance while maintaining high levels of efficiency in drone routing.
该系统的进一步开发应侧重于几个关键领域，以扩展其适用性和效率。可以结合机器学习模型来预测交通模式、天气变化和需求激增，使系统能够在中断发生之前主动调整路线，从而提高弹性和效率。此外，扩展模型以在更多样化的地形上运行，例如风或沙尘暴可能对操作造成挑战的沿海和沙漠地区，将提高其多功能性。通过预测使用模型和更高效的充电策略增强电池管理，也可以进一步优化无人机在低电量条件下的操作。此外，随着监管环境的发展，未来的工作应侧重于创建灵活的算法，这些算法可以无缝适应更新的空域限制，确保合规性，同时保持无人机路线的高效率。