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

III：小：协作研究：多源数据驱动的互联快递系统设计和库存再平衡优化框架

• 批准号: 1814510
• 负责人: Hui Xiong
• 金额: $ 25万
• 依托单位: Rutgers University Newark
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1814510&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Multi

The inter-connected express delivery system is very needed for many emerging applications, such as public bike rental service, electric car sharing service, 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, the inter-connected express delivery system has the following unique characteristics: (1) each station covers a small service area; (2) 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 system, 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.

互联的快递系统对于许多新兴应用是非常需要的，例如公共自行车租赁服务，电动汽车共享服务和新鲜产品交付。互联快递系统的成功部署可以极大地改善交通、节能、食品供应和城市可持续性。与传统的配送系统相比，互联快递系统具有以下独特的特点：（1）每个站点覆盖的服务区域很小;（2）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：如何确定给定区域的站点位置以及如何及时重新平衡站点之间的库存。应对这些根本性挑战对于使互联互通的快递系统更加有效、高效和可持续发展非常重要。该项目旨在开发一个数据驱动的解决方案来解决这些挑战。本研究将推动互联快递系统领域的发展，拓展数据挖掘与优化课程内容，培养本科生和研究生。本项目重点研究两个基础研究问题：站点选址和站点库存再平衡优化。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，本项目确定了两种不同的情况下的库存再平衡问题：静态再平衡和动态再平衡。静态再平衡的研究目标是最小化总行程。本计画提出一个以丛集为基础的启发式解决方案来解决静态再平衡问题，以使解决方案具有实际应用的可扩充性。动态再平衡的研究目标是最小化总的未满足需求，这比静态再平衡涉及更多的不确定性。该项目开发了一种结合了先进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。

项目成果

Domain-oriented Language Modeling with Adaptive Hybrid Masking and Optimal Transport Alignment

• DOI: 10.1145/3447548.3467215
• 发表时间: 2021-08
• 期刊: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Denghui Zhang;Zixuan Yuan;Yanchi Liu;Hao Liu;Fuzhen Zhuang;Hui Xiong;Haifeng Chen

Semi-Supervised Multi-Modal Clustering and Classification with Incomplete Modalities

• DOI: 10.1109/tkde.2019.2932742
• 发表时间: 2021-02
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Yang Yang-Yang;De-chuan Zhan;Yi-Feng Wu;Zhi-Bin Liu;Hui Xiong;Yuan Jiang

Incremental Spatio-Temporal Graph Learning for Online Query-POI Matching

• DOI: 10.1145/3442381.3449810
• 发表时间: 2021-04
• 期刊: Proceedings of the Web Conference 2021
• 作者: Zixuan Yuan;Hao Liu;Junming Liu;Yanchi Liu;Yang Yang-Yang;Renjun Hu;Hui Xiong

Scalable Heterogeneous Graph Neural Networks for Predicting High-potential Early-stage Startups

• DOI: 10.1145/3447548.3467383
• 发表时间: 2021-08
• 期刊: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Shengming Zhang;Hao Zhong;Zixuan Yuan;Hui Xiong

Spatio-Temporal Dual Graph Attention Network for Query-POI Matching

用于查询 POI 匹配的时空双图注意力网络

• DOI: 10.1145/3397271.3401159
• 发表时间: 2020
• 期刊: International ACM SiGIR Conference on Research and Development in Information Retrieval
• 作者: Yuan, Zixuan;Liu, Hao;Zhang, Denghui;Yi, Fei;Zhu, Nengjun;Xiong, hui
• 通讯作者: Xiong, hui