I-Corps: A Scalable Cloud-based Route Optimization Software for Efficient Aerial and Road Logistics

这个I-Corps项目更广泛的影响/商业潜力是开发一个软件应用程序，以生成优化的包裹投递路线计划。其目标是授权使用电动卡车和混合卡车-无人机车队的最后一英里服务提供商。在美国，由于包裹量的增加、成本的上升、客户期望的变化以及企业气候承诺的增加，电动卡车和自动无人机在最后一英里物流领域的应用稳步上升。现有技术的能力不足限制了这些新兴地面和空中交通工具的安全、无缝和有效使用。拟议中的软件应用程序可以满足新兴市场的需求，使物流经理、路线规划者、调度员和卡车司机能够更快地规划、更大的可视性、实时跟踪和逐弯导航。这可能使服务提供商降低供应链成本并具有竞争力，同时促进最后一英里交付解决方案，从而降低碳排放并加快履行速度。这些能力可能会加速电动卡车和无人机包裹递送的采用，并缓解配送系统日益增长的压力。I-Corps项目的基础是开发一种基于云的路线优化软件，该软件将为电动卡车和混合动力卡车-无人机系统生成最后一英里分配计划。提出的机器学习和基于优化的分解算法旨在利用特定问题的特征，如电池约束、充电操作和有效载荷容量，以有效解决复杂的路由问题。该算法还可以考虑现实生活中的空间（例如，禁飞区）、时间（例如，一天中的操作时间限制）和后勤（例如，客户可用性）限制，以确保实际的路线计划。此外，所提出的技术还可以通过使用基于深度强化学习的动态重路由模型实时生成替代路线计划来实现主动交通管理策略。所提出的路线优化软件可能会产生新的理论，并为路线规划方法的最新知识做出贡献，并提高路线优化软件处理新的物流技术的能力，以实现高效的地面和空中最后一英里交付服务。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of a software application to generate optimized route plans for parcel delivery. The goal is to empower last-mile service providers who employ electric trucks and a mixed truck-drone fleet. The adoption of electric trucks and autonomous drones for last-mile logistics has risen steadily in the United States due to growing parcel volumes, rising costs, changing customer expectations, and increasing corporate climate pledges. Lack of capabilities in the existing technology limits the safe, seamless and efficient use of such emerging ground and aerial vehicles. The proposed software application may meet the emerging market needs by enabling logistic managers, route planners, dispatchers, and truck drivers with faster planning, greater visibility, real-time tracking and turn-by-turn navigation. This may allow service providers to lower supply chain costs and be competitive while facilitating last-mile delivery solutions that lead to lower carbon emissions and faster fulfillment. These capabilities may accelerate the adoption of electric trucks and drones for package delivery and alleviate the growing strain on this distribution system.This I-Corps project is based on the development of a cloud-based route optimization software that will generate last-mile distribution plans for electric trucks and hybrid truck-drone systems. The proposed machine learning and optimization-based decomposition algorithms are designed to exploit problem-specific characteristics, such as battery constraints, charging operations, and payload capacity, to efficiently solve complex routing problems. The algorithm also may account for real-life spatial (e.g., no-fly zones), temporal (e.g., time-of-day operating restrictions) and logistical (e.g., customer availability) constraints to ensure practical route plans. In addition, the proposed technology also may allow an active traffic management strategy by generating an alternative route plan in real-time using a deep reinforcement learning-based dynamic rerouting model. The proposed route optimization software may lead to new theories and contribute to the state-of-the-art knowledge on route planning methods and advance the capabilities of route optimization software to handle new logistics technologies for efficient ground and aerial last-mile delivery service.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.