A Stochastic Integer Programming Approach to Air Traffic Scheduling and Operations
The speaker: Dr. Kai Wang (from Sloan School of Management, Massachusetts Institute of Technology)
Time: Starting from 8:00 pm, April. 24th, 2021 (Beijing time)
https://zoom.com.cn/j/7114333104?pwd=aHk1NTNKaGk4N3VMaWJHa2tLZTFXUT09 (Meeting ID: 711 433 3104; Meeting password:123456)
Speaker invited by: Prof. Sun Xiaoqian
Air traffic management measures comprise tactical operating procedures to minimize delay costs, and strategic scheduling interventions to control over-capacity scheduling. Although interdependent, these problems have been treated in isolation. This paper proposes an Integrated Model of Scheduling and Operations in Airport Networks that jointly optimizes scheduling interventions and ground-holding operations across airports networks, under operating uncertainty. It is formulated as a two-stage stochastic program with integer recourse. To solve it, we develop an original decomposition algorithm with provable quality guarantees. The algorithm relies on new optimality cuts-dual integer cuts-which leverage the reduced costs of the dual linear programming relaxation of the second-stage problem. The algorithm also incorporates neighborhood constraints, which shift from exploration to exploitation at later stages. Moreover, we propose a data-driven scenario generation procedure that constructs representative scenarios for stochastic programming from historical records of operations. Computational experiments show that our algorithm yields near-optimal solutions for networks of the size of the US National Airspace System. Ultimately, the proposed approach enhances airport demand management models through scale integration (by capturing network-wide interdependencies) and scope integration (by capturing interdependencies between scheduling and operations).
Short bio of the speaker:
Dr. Kai Wang is a Postdoctoral Associate from the Massachusetts Institute of Technology. He obtained his PhD degree from The Hong Kong Polytechnic University in 2019. He was also a visiting PhD student at Carnegie Mellon University. Kai Wang’s research spans large-scale, stochastic, and data-driven optimization, with applications in mobility and logistics systems. His research has tackled a wide range of real-world problems, spanning urban mobility, aviation, maritime transportation, and smart cities. His research has appeared in top-tier journals such as Operations Research, Transportation Science, and Transportation Research Part B. It has been recognized by several academic distinctions, e.g., the Best Paper Award in the Applied Track from the 15th INFORMS Workshop on Data Mining and Decision Analytics (2020).