Qie HeAssistant Professor, Industrial & Systems Engineering
Qie He is an assistant professor at the Department of Industrial and Systems Engineering, University of Minnesota. His primary research interests are to develop analytics and optimization models, algorithms, and software to improve public transportation and logistics. He is also interested in optimal control theory with its application in cancer treatment. The analytics tools his group developed have been used by Metro Transit, the primary public transportation service provider in Minnesota, and the Transportation Department of Hennepin County, the largest county in Minnesota by population. He has been a faculty scholar at the Center for Transportation Studies at the University of Minnesota since 2017.
Professor He received a Ph.D. in Industrial Engineering from Georgia Tech and an M.S. in Control Science and Engineering and a B.S. in Automation, both from Tsinghua University.
More information can be found on He's personal website.
My main research goal is to improve the service quality and efficiency of transportation and healthcare. Specifically, I develop prediction and optimization algorithms to help public transportation agencies improve their planning and operational practices; I develop efficient algorithms with performance guarantee to solve large-scale routing and scheduling problems faced daily by logistics and e-retail companies; I collaborate with doctors and applied mathematicians to discover new cancer treatment plans. The central theme of my research is to build new models, algorithms, and practical tools to bridge the gap between massive available data and complex decision making.
The projects I have worked on include exact algorithms for various vehicle routing and scheduling problems (for reducing energy consumption, for on-demand delivery system, and for restoring power networks), public transit workforce optimization, data-driven fleet management, capacity design and information release of parking systems, design of on-demand delivery systems, and personalized cancer treatment.
- Zeyang Wu, Qie He, and Kameng Nip. A fast exact algorithm for discrete resource allocation with nested constraints.
- Zeyang Wu, Qie He. Optimal switching sequence for switched linear systems.
Refereed Journal Publications:
- Qie He, Stefan Irnich, and Yongjia Song. Branch-cut-and-price for the vehicle routing problems with time windows and convex node costs. Transportation Science, 2018.
- Ricardo Fukasawa, Qie He, Fernando Santos, and Yongjia Song. A joint vehicle routing and speed optimization problem. INFORMS Journal On Computing, 2018.
- Qie He, Xiaochen Zhang, and Kameng Nip. Speed optimization over a path with heterogeneous arc costs. Transportation Research Part B 104 (2017): 198-214. pdf
- Ricardo Fukasawa, Qie He, and Yongjia Song. A disjunctive convex programming approach to the pollution-routing problem. Transportation Research Part B 94 (2016): 61-79.
- Qie He, Junfeng Zhu, David Dingli, Jasmine Foo, and Kevin Leder. Optimized treatment schedules for chronic myeloid leukemia. PLOS Computational Biology 12.10 (2016): e1005129.
- Shabbir Ahmed, Qie He, Shi Li, and George L. Nemhauser. On the computational complexity of minimum-concave-cost flow in a two-dimensional grid. SIAM Journal on Optimization 26.4 (2016): 2059-2079.
- Ricardo Fukasawa, Qie He, and Yongjia Song. A branch-cut-and-price algorithm for the energy minimization vehicle routing problem. Transportation Science 50.1 (2016): 23-34.
- Qie He, Shabbir Ahmed, and George L. Nemhauser. Minimum concave cost flow over a grid network. Mathematical Programming 150.1 (2015): 79-98.
- Qie He, Shabbir Ahmed, and George L. Nemhauser. Sell or hold: A simple two-stage stochastic combinatorial optimization problem. Operations Research Letters 40.2 (2012): 69-73.
- Qie He, Shabbir Ahmed, and George L. Nemhauser. A Probabilistic Comparison of Split and Type 1 Triangle Cuts for Two-Row Mixed-Integer Programs. SIAM Journal on Optimization 21.3 (2011): 617-632.
- Qie He, and Ling Wang. A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Applied Mathematics and Computation 186.2 (2007): 1407-1422.
- Fuzhuo Huang, Ling Wang, and Qie He. An effective co-evolutionary differential evolution for constrained optimization. Applied Mathematics and Computation 186.1 (2007): 340-356.
- Qie He, Ling Wang, and Bo Liu. Parameter estimation for chaotic systems by particle swarm optimization. Chaos, Solitons & Fractals 34.2 (2007): 654-661.
- Qie He, and Ling Wang. An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence 20.1 (2007): 89-99.
Refereed Conference Proceedings:
- Qie He, Stefan Irnich, and Yongjia Song. Branch-cut-and-price for the VRP with time windows and convex node costs (Extended abstract). Odysseus 2018, 7th International Workshop on Freight Transportation and Logistics.
- Qie He, Ling Wang, and Fu-zhuo Huang. Nonlinear constrained optimization by enhanced co-evolutionary PSO. CEC 2008.
- Fuzhuo Huang, Ling Wang, and Qie He. A hybrid differential evolution with double populations for constrained optimization. CEC 2008.