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

I-Corps：可扩展的基于云的路线优化软件，用于高效的空中和公路物流

• 批准号: 2240977
• 负责人: Sharan Srinivas
• 金额: $ 5万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240977&HistoricalAwards=false
• 关键词: Corps; Scalable; Cloud; based; Route

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.

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，为包裹递送生成优化的路线计划。 其目标是为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提供支持。在美国，由于包裹量的增长、成本的上升、客户期望的变化以及企业气候承诺的增加，电动卡车和自动驾驶无人机在最后一英里物流中的应用稳步上升。现有技术能力的缺乏限制了安全、无缝和有效地使用这种新兴的地面和空中交通工具。所提出的软件应用程序可以通过使物流管理员、路线规划者、调度员和卡车司机具有更快的规划、更高的可见性、实时跟踪和逐向导航来满足新兴市场的需求。这可能使服务提供商能够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。这些功能可能会加速电动卡车和无人机在包裹递送中的采用，并缓解配送系统日益增长的压力。I-Corps项目基于基于云的路线优化软件的开发，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配送计划。 所提出的基于机器学习和优化的分解算法旨在利用特定于问题的特征，例如电池约束、充电操作和有效载荷容量，以有效地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，一天中的时间操作限制）和后勤（例如，客户可用性）约束，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新路由模型实时生成替代路线计划来允许主动交通管理策略。所提出的路线优化软件可能会导致新的理论和有助于国家的最先进的知识路线规划方法和推进路线优化软件的能力，以处理新的物流技术，为有效的地面和空中最后，该奖项反映了NSF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进行评估，被认为值得支持的搜索.