III: Small: Collaborative Research: A Multi-source Data Driven Optimization Framework for Inter-connected Express Delivery System Design and Inventory Rebalance

互连的快递系统是最近迅速增长的一类递送服务。例子包括公共自行车租赁服务、电动汽车共享服务和新鲜产品递送。成功部署互联互通的快递系统可以极大地改善交通、节能、食品供应和城市可持续发展。与传统的快递系统相比，互联快递系统具有以下独特特点：第一，每个站点覆盖的服务区域很小；第二，所有站点都是内部连接的，因为它们可以相互充当库存或供应商。互联快递系统的发展面临着两个基本的研究挑战：如何确定特定区域的站点位置和如何及时地重新平衡站点之间的库存。为了使互联的快递系统更加有效、高效和可持续，解决这些根本性挑战是非常重要的。该项目旨在开发一种数据驱动的解决方案来解决这些挑战。本研究将推进互联快递系统的研究领域，拓展数据挖掘与优化的课程内容，培养本科生和研究生。本项目重点研究站点选址和站点库存再平衡优化两个基础研究问题。为了解决第一个问题，本项目收集和分析了来自不同来源的各种数据，如历史需求数据和地理数据，并结合了基于神经网络的预测方法和组合优化技术。为了解决第二个问题，该项目确定了库存再平衡问题的两个截然不同的案例：静态再平衡和动态再平衡。静态再平衡的研究目标是使总行程最小。本项目开发了一种基于分簇的启发式解决方案来解决静态再平衡问题，以使解决方案具有可伸缩性，便于实际使用。动态再平衡的研究目标是使总体未满足的需求最小化，这比静态再平衡具有更多的不确定性。该项目开发了一种结合高级数据挖掘和随机优化技术的混合方法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Inter-connected express delivery systems are a recent and rapidly growing category of delivery services. Examples include public bike rental services, electric car sharing services, and fresh product delivery. The successful deployment of inter-connected express delivery systems can greatly improve transportation, energy saving, food supply, and urban sustainability. Compared with traditional delivery systems, an inter-connected express delivery system has the following unique characteristics: first, each station covers a small service area; second, all stations are internally connected because they can act as inventories or suppliers to each other. There are two fundamental research challenges for the development of the inter-connected express delivery system: how to decide the station locations for a given area and how to timely rebalance the inventories among stations. It is very important to address these fundamental challenges in order to make the inter-connected express delivery system more effective, efficient and sustainable. This project aims to develop a data driven solution for solving these challenges. This study will advance the field of inter-connected express delivery systems, expand the curricular content of data mining and optimization, and train undergraduate and graduate students.This project focuses on two basic research problems: station site selection and station inventory rebalancing optimization. To solve the first problem, this project collects and analyzes a variety of data from different sources, such as historical demand data and geographic data, and combines neural network-based prediction method and combinatorial optimization techniques. To solve the second problem, this project identifies two distinct cases of the inventory rebalancing problem: static rebalancing and dynamic rebalancing. The research objective of the static rebalancing is to minimize the overall travel distance. This project develops a clustering-based heuristic solution for solving the static rebalancing in order to make the solution scalable for practical use. The research objective of the dynamic rebalancing is to minimize the overall unsatisfied demand, which involves much more uncertainty than the static one. This project develops a hybrid approach that combines advanced data mining and stochastic optimization techniques.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.