Zhongshun “Tony” Shi, PhD

Assistant Professor

Dept. of Industrial and Systems Engineering
University of Tennessee Space Institute
411 B. H. Goethert Parkway, MS-19
Tullahoma, TN 37388-9700
Telephone: (931) 393-7101
Fax: (931) 393-7201
E-mail: tzshi@utsi.edu

 

Biography

Zhongshun “Tony” Shi is an Assistant Professor in the Department of Industrial and Systems Engineering and the Director of the Intelligent Decision and Engineering Analytics (IDEA) Lab at the University of Tennessee Space Institute, and Associate Director of the Logistics, Transportation, and Supply Chain (LTS) Engineering Lab at the University of Tennessee Knoxville. Prior to joining UT, he was a Research Associate in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison and the University of Tennessee Space Institute, respectively. He received his B.S. degree in Applied Mathematics from China University of Geosciences (Beijing) and his Ph.D. degree in Management Science and Engineering from Peking University. His research interests include mathematical optimization, approximation algorithm, artificial intelligence and data analytics. The main applications are the optimization and uncertainty quantification of complex systems in the areas of manufacturing, aerospace, energy, agriculture and supply chain. Dr. Shi is a member of IEEE, IISE, POMS, INFORMS and ASEE.

 

Education

  • PhD, Management Science and Engineering, Peking University, 2017
  • BS, Applied Mathematics, China University of Geosciences (Beijing), 2011

 

Professional Experience

  • 2020-present, Assistant Professor, Department of Industrial and Systems Engineering, University of Tennessee Space Institute
  • 2019-2020, Research Associate, Department of Industrial and Systems Engineering, University of Tennessee Space Institute
  • 2017-2019, Research Associate, Department of Industrial and Systems Engineering, University of Wisconsin-Madison
  • 2011-2017, Research Assistant, Department of Industrial Engineering and Management, Peking University

 

Research

  • Intelligent Decision and Complex Systems
  • Artificial Intelligence and Data Analytics
  • Applied Optimization and Approximation Algorithms
  • Smart Manufacturing and Industry 4.0
  • Planning and Scheduling in Manufacturing and Services Systems
  • Logistics, Transportation and Supply Chain Engineering

 

Courses Taught

  • Design of Experiments
  • Statistical Methods in Industrial Engineering
  • Manufacturing Systems Modeling and Analysis
  • Performance Analysis of Manufacturing Systems
  • Simulation Modeling and Analysis
  • Digital Manufacturing Technologies

 

Awards and Recognitions

  • Second Place, INFORMS Syngenta Crop Challenge in Analytics, 2017
  • Outstanding Doctoral Dissertation Prize, Peking University, 2017
  • Outstanding Graduate of Beijing, China, 2017
  • Presidential Research Fellowships, Peking University, 2016
  • Graduate National Scholarship of China, 2016
  • Top-Ten Outstanding Scholars Award, College of Engineering, Peking University, 2016

 

Publications

  • T. Wu, Z. Shi and C. Zhang, “The Intermodal Hub Location Problem with Market Selection”, Computers and Operations Research, accepted, 2020.
  • M. Qin, Z. Shi, W. Chen, S. Gao and L. Shi, “Wafer Defect Inspection Optimization with Partial Coverage – A Numerical Approach”, IEEE Transactions on Automation Science and Engineering, accepted, 2020.
  • T. Wu, Z. Shi, Z. Liang, X. Zhang and C. Zhang, “Dantzig-Wolfe Decomposition for the Facility Location and Production Planning Problem”, Computers and Operations Research, accepted, 2020.
  • H. Ma, H.K. Lee, Z. Shi and J. Li, Workforce Allocation in Motorcycle Transmission Assembly Lines: A Case Study on Modeling, Analysis, and Improvement, IEEE Robotics and Automation Letters, 5(3): 4164-4171, 2020.
  • M. Qin, R. Wang, Z. Shi, L. Liu, and L. Shi, “A Genetic Programming based Scheduling Approach for Hybrid Flow Shop with a Batch Processor and Waiting Time Constraint”, IEEE Transactions on Automation Science and Engineering, accepted, 2019.
  • Z. Shi, S. Gao, H. Xiao and W. Chen, “A Worst-Case Formulation for Constrained Ranking and Selection with Input Uncertainty”, Naval Research Logistics, 66(8): 648-662, 2019.
  • F. Gao, Z. Shi, S. Gao and H. Xiao, “Efficient Simulation Budget Allocation for Subset Selection Using Regression Metamodels”, Automatica, 106: 192-200, 2019.
  • W. Wang, Z. Shi, L. Shi and Q. Zhao, “Integrated Optimization on Flow Shop Production with Cutting Stock”, International Journal of Production Research, 57(19): 5996-6012, 2019.
  • Y. Peng, E. Huang, J. Xu, Z. Shi and C.H. Chen, “A Coordinate Optimization Approach for Concurrent Design”, IEEE Transactions on Automatic Control, 64(7): 2913 – 2920, 2019.
  • F. Gao, S. Gao, H. Xiao and Z. Shi, “Advancing Constrained Ranking and Selection with Regression in Partitioned Domains”, IEEE Transactions on Automation Science and Engineering, 16(1): 382 – 391, 2019.
  • Z. Shi, S. Gao, J. Du, H. Ma and L. Shi, “Automatic Design of Dispatching Rules for Real-Time Optimization of Complex Production Systems”, Proceedings of the 2019 IEEE/SICE International Symposium on System Integrations, pp. 55-60, 2018.
  • Z. Shi, Z. Huang and L. Shi, “Customer Order Scheduling on Batch Processing Machines with Incompatible Job Families”, International Journal of Production Research, 56(1-2): 795-808, 2018.
  • L. Liu, Z. Shi and L. Shi, “Minimization of Total Energy Consumption in an m-machine Flow Shop with an Exponential Time-Dependent Learning Effect”, Frontier of Engineering Management, 5(4): 487-498, 2018.
  • Z. Huang, Z. Shi and L. Shi, “Minimizing Total Weighted Completion Time on Batch and Unary Processors with Incompatible Job Families”, International Journal of Production Research, 57(2): 567-581, 2018.
  • Y. Zhao, X. Zhang, Z. Shi and L. He, “Grain Price Forecasting using a Hybrid Stochastic Method”, Asia-Pacific Journal of Operational Research, 34(05): 1750020, 2017.
  • P. Liu, X. Zhang, Z. Shi and Z. Huang, “Simulation Optimization for MRO Systems Operations”, Asia-Pacific Journal of Operational Research, 34(02): 1750003, 2017.
  • Z. Shi, Z. Huang and L. Shi, “Two-Stage Scheduling on Batch and Single Machines with Limited Waiting Time Constraint”, Frontier of Engineering Management, 4(3): 368-374, 2017.
  • C. Zhang, Z. Shi, Z. Huang, Y. Wu and L. Shi, “Flow Shop Scheduling with a Batch Processor and Limited Buffer”, International Journal of Production Research, 55(11): 3217-3233, 2017.
  • Z. Huang, Z. Shi, C. Zhang and L. Shi, “A Note on “Two New Approaches for a Two-Stage Hybrid Flowshop Problem with a Single Batch Processing Machine under Waiting Time Constraint””, Computers & Industrial Engineering, 110: 590-593, 2017.
  • Z. Shi, L. Wang, P. Liu and L. Shi, “Minimizing Completion Time for Order Scheduling: Formulation and Heuristic Algorithm”, IEEE Transactions on Automation Science and Engineering, 14(4): 1558-1569, 2017.
  • Z. Shi, Z. Huang and L. Shi, “Two-stage Flow Shop with a Batch Processor and Limited Buffer”, Proceedings of the 2016 IEEE Conference on Automation Science and Engineering, pp. 395-400, 2016.
  • J. Song, Z. Shi, B. Sun and L. Shi, “Treatment Planning for Volumetric-modulated Arc Therapy: Model and Heuristic Algorithms”, IEEE Transactions on Automation Science and Engineering, 12(1): 116-126, 2015.
  • Z. Shi, P. Liu, H. Gao and L. Shi, “Production Planning for a Class of Batch Processing Problem”, Proceedings of the 2015 IEEE Conference on Automation Science and Engineering, pp. 1188-1193, 2015.
  • Z. Shi, L. Wang and L. Shi, “Approximation Method to Rank-One Binary Matrix factorization”, Proceedings of the 2014 IEEE Conference on Automation Science and Engineering, pp. 800-805, 2014.
  • B. Sun, Z. Shi, J. Song, G. Zhu and L. Shi, “A Linearized Model and Nested Partitions Heuristics for VMAT Radiation Treatment Planning Optimization”, Proceedings of the 2013 IEEE Conference on Automation Science and Engineering, pp. 629-633, 2013.
  • L. Wang, Z. Shi and L. Shi, “A Novel Quadratic Formulation for Customer Order Scheduling Problem”, Proceedings of the 2013 IEEE Conference on Automation Science and Engineering, pp. 576-580, 2013.