张瑜教授领导美国南佛罗里达大学下一代交通系统实验室，主要研究领域包括网络建模和系统分析，空中交通流管理，多式联运及交通运输可持续性发展等。张瑜教授正在进行的航空方面的研究项目包括：分析美国下一代空中交通系统新技术和新程序在空域中的运用对国家空域系统的绩效产生的影响，尤其是在恶劣的对流天气情况，现代化系统将如何增加民航的飞行效率；协助美国联邦航空管理局和国际民航组织重新审视空侧绩效评价指标系统；单个机场或区域多机场作为乘客和货运多模式联运中心的设计和管理；枢纽支线机场网络的优化设计。正在进行的可持续交通方面的研究项目包括：消费者对自动驾驶车辆技术的感知和未来的采纳；共享自动驾驶车辆对家庭车辆所有权的潜在影响；对未来自动驾驶和车联网时代的交通管理系统建模虚拟网络；自主浮动（非站式）自行车共享系统的规划和管理；机场可持续性发展评价指标的确立和网上跟踪及监控系统的开发。
张瑜教授累计在高水平交通期刊上发表论近三十篇，现任交通研究期刊C部分的编委，国际运输科学技术杂志的编委，为交通研究期刊A，B，D, E, F，交通科学期刊等十余份交通领域顶级期刊审稿。张瑜教授于2010年获得Fred Burggraf奖，该奖项由美国科学院运输研究委员会颁发给35岁以下有杰出表现的年轻研究人员。张瑜教授指导的博士生多次获得美国国家科学基金会运输研究委员会机场合作研究项目的研究生奖学金，该奖项资助学生进行前瞻性和创造性的民航方向短期研究项目。
张瑜教授在中国东南大学获得学士学位，美国加州大学伯克利获得硕士和博士学位。张瑜教授 现任美国运输委员会空侧和空域容量和延误分会（TRBAV060）副会长，海外华人交通协会（COTA）主席（2016-2018）。张瑜教授于2016年被中国民航大学聘为蓝天学者讲座教授，并于2016-2017年学术休假期间被同济大学聘为高峰学科兼职讲座研究员。
讲座题目：浮动式自行车共享系统的静态再平衡优化
摘要：Free-floating bike sharing (FFBS) is an innovative bike sharing model. FFBS saves on start-up cost, in comparison to station-based bike sharing (SBBS), by avoiding construction of expensive docking stations and kiosk machines. FFBS prevents bike theft and offers significant opportunities for smart management by tracking bikes in real-time with built-in GPS. However, like SBBS, the success of FFBS depends on the efficiency of its rebalancing operations to serve the maximal demand as possible.
Bicycle rebalancing refers to the reestablishment of the number of bikes at sites to desired quantities by using a fleet of vehicles transporting the bicycles. Static rebalancing for SBBS is a challenging combinatorial optimization problem. FFBS takes it a step further, with an increase in the scale of the problem. This article is the first effort in a series of studies of FFBS planning and management, tackling static rebalancing with single and multiple vehicles.
We present a Novel Mixed Integer Linear Program for solving the Static Complete Rebalancing Problem. The proposed formulation, can not only handle single as well as multiple vehicles, but also allows for multiple visits to a node by the same vehicle. We present a hybrid nested large neighborhood search with variable neighborhood descent algorithm, which is both effective and efficient in solving static complete rebalancing problems for large-scale bike sharing programs.
Computational experiments were carried out on the 1 Commodity Pickup and Delivery Trav- eling Salesman Problem (1-PDTSP) instances used previously in the literature and on three new sets of instances, two (one real-life and one general) based on Share-A-Bull Bikes (SABB) FFBS program recently launched at the Tampa campus of University of South Florida and the other based on Divvy SBBS in Chicago. Computational experiments on the 1-PDTSP instances demon- strate that the proposed algorithm outperforms a tabu search algorithm and is highly competitive with exact algorithms previously reported in the literature for solving static rebalancing problems in SBSS. Computational experiments on the SABB and Divvy instances, demonstrate that the proposed algorithm is able to deal with the increase in scale of the static rebalancing problem pertaining to both FFBS and SBBS, while deriving high-quality solutions in a reasonable amount of CPU time.
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