Qie He

Office: Lind Hall 359
Mailing address: 111 Church Street SE, Minneapolis, MN 55455
Phone: +1-612-625-3828

I have left the University of Minnesota, so this page will no longer be actively maintained.

My new email address is: the concatenation of my first name, my last name, and 01 {at} gmail.

Research Interests

My main research goal is to develop optimization and analytics models and algorithms to improve public transportation and logistics. I am also interested in optimal control theory and its application in cancer treatment.

Specifically, I develop efficient algorithms with performance guarantee to solve large-scale routing and scheduling problems faced daily by logistics and e-retail companies; I develop prediction and optimization algorithms to help public transportation agencies address the driver shortage issue; 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/been working 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. Below is a brief introduction to some of these projects.



  1. Zeyang Wu, Qie He, and Kameng Nip. A new combinatorial algorithm for separable convex resource allocation with nested bound constraints.

Refereed Journal Publications:

  1. Zeyang Wu, Qie He. Optimal switching sequence for switched linear systems. SIAM Journal on Control and Optimization, 58(2), 1183-1206, 2020. (Test instances and related code can be found here.)
  2. 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, 53(5), 1409-1426, 2019.
  3. Ricardo Fukasawa, Qie He, Fernando Santos, and Yongjia Song. A joint vehicle routing and speed optimization problem. INFORMS Journal On Computing, 30(4), 694-709, 2018.
  4. 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
  5. 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. pdf
  6. 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 pdf
  7. 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. pdf
  8. 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. pdf
  9. Qie He, Shabbir Ahmed, and George L. Nemhauser. Minimum concave cost flow over a grid network. Mathematical Programming 150.1 (2015): 79-98. link
  10. 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. pdf
  11. 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. pdf
  12. 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. link
  13. 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. link
  14. Qie He, Ling Wang, and Bo Liu. Parameter estimation for chaotic systems by particle swarm optimization. Chaos, Solitons & Fractals 34.2 (2007): 654-661. link
  15. 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. link

Refereed Conference Proceedings:

  1. 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.
  2. Qie He, Ling Wang, and Fu-zhuo Huang. Nonlinear constrained optimization by enhanced co-evolutionary PSO. CEC 2008. link
  3. Fuzhuo Huang, Ling Wang, and Qie He. A hybrid differential evolution with double populations for constrained optimization. CEC 2008. link

Data and Test Instances

The data and test instances used in some papers can be found here.


University of Minnesota

IE8531 Discrete Optimization (Fall 2015)
IE8534 Topics in Operations Research: Stochastic Nonlinear Optimization (Spring 2016)
IE8534 Topics in Operations Research: Stochastic Dynamic Optimization (Spring 2019)
IE5801 Capstone Project for Analytics (Fall 2018)
IE4041 Senior Design (Spring 2015, 2016, 2018, 2019, 2020)
IE3012 Optimization II (Fall 2016, 2017, Spring 2018, 2019)
IE3521 Statistics, Quality and Reliability (Fall 2014, 2018, Spring 2014, 2015)
IE4521 Statistics, Quality and Reliability (Fall 2013)

Georgia Tech

ISyE3133 Engineering Optimization (Summer 2012)