报告题目：The Traveling Salesman Problem with Drone: Algorithms, Neural Acceleration, and Probabilistic Analysis
报告人：：Changhyun Kwon 教授
邀请人：程春 教授
报告时间及地点：2026年10月05日10:00--12:00 经管学院B312
报告人简介：
Changhyun Kwon is a Professor and Interim Head of the Department of Industrial and Systems Engineering at KAIST, where he leads the Computational Optimization Methods Lab (COMET). His research focuses on computational optimization for transportation and logistics, with an emphasis on integrating machine learning with heuristic and exact algorithms for large-scale routing and mobility problems. He is also the co-founder and Chief Technology Officer of Omelet. Before joining KAIST, he held faculty positions at the University at Buffalo and the University of South Florida. He received his Ph.D. in Industrial Engineering from Penn State in 2008 and his B.S. in Mechanical Engineering from KAIST in 2000. He is a recipient of the NSF CAREER Award, the author of Julia Programming for Operations Research, and a member of the JuMP steering committee.
报告摘要：
The traveling salesman problem with drone (TSP-D) coordinates a truck and a drone to serve customers while minimizing delivery completion time. We examine this problem through algorithm design, neural acceleration, and probabilistic analysis. We first present an iterative chainlet partitioning algorithm that combines dynamic programming and local search to obtain high-quality solutions efficiently. We then show how graph neural networks can guide the search, reducing computational effort while largely preserving solution quality. Finally, we establish asymptotic scaling results and bounds for optimal completion times under randomly distributed customer locations, revealing how relative vehicle speeds influence the benefits of drone assistance. These results connect practical optimization methods with a theoretical understanding of truck–drone delivery systems.
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