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.
