Jian Hu

Associate Professor, director of the Smart Mobility Analytics Research and Training Lab
Industrial & Manufacturing System Engineering
University of Michigan - Dearborn
4901 Evergreen Rd.
Dearborn, MI 48128.
Phone: (313) 583-6745
Fax: (313) 593-3692
E-mail: jianhu@umich.edu

Dr. Hu is an Associate Professor in the Department of Industrial & Manufacturing Systems Engineering at the University of Michigan - Dearborn. He received his PhD in the area of operations research from Northwestern University 2011, M.S. in the area of control theory and applications from the University of Arkansas at Little Rock and the Chinese Academy of Sciences, and B.S. from the Xi'an Jiaotong University. Dr. Hu's research interests focus on methodologies of data-driven risk management and decision making uncertainty and their applications including

  • risk analysis and management;
  • decision making under uncertainty;
  • data analytics and machine learning;
  • stochastic models in supply chain management, transportation, logistics, finance, and health care.

He has led and participated in numerous federally and industrially funded research projects on robust assessment of consumer preference, advanced robust optimization models and algorithms, driving behavior data analytics, emergency management of traffic incidents, risk-adjusted homeland security budget allocation, resilient nurse scheduling and rescheduling, and plant workforce planning.


  • Robust averse machine learning driven project management
    This project is to pursue the best portfolio investment among vehicle development projects for enhancing vehicle performance, economy, and after-sales service.

  • Plant workforce planning optimization (collaboration with Ford)
    This project is to find an optimal quantity of legacy and TPT employees for minimizing staff cost and controlling the risk that staff headcounts cannot meet the rise and fall in production demand for a long-term planning horizon.

  • Deployment of public charging infrastructure (collaboration with the Argonne National Laboratory)
    This project is to assess EV drivers’ charging preferences, predict charging demands, and on this basis design an optimal long-term plan for deploying public charging infrastructure and upgrading current power distribution networks.

  • Counterfactual explanation for machine learning
    This project is to interpret and explain the predictions of individual instance given by machine learning models.

  • Robust decision making of coordinating pricing and inventory control (funded by the NSF)
    TThis project develops a data-driven robust model to seek the best-selling price and order quantity for maximizing a retailer’s profit when facing the challenge that there is a lack of market data to accurately predict the price-demand function.

  • Risk adjusted homeland security budget allocation (funded by the NSF)
    This project is to model a budget allocation problem for the Urban Areas Security Initiative (UASI) against property losses, fatalities, and damages of important infrastructure in terrorist attacks.

  • Risk averse multi-attribute decision making based on stochastic dominance (funded by the NSF)
    This project is to develop a novel risk-averse decision making framework which maximizes the profit while requiring the decision policy preferable to the benchmark to the extent that the random consequence yielded by the policy stochastically dominated one at the benchmark.

  • Traffic incident response and management (funded by the FHWA)
    This project is to develop a traffic incident response and management model for seeking the best strategy of dispatching service vehicles in Central Arkansas to fast handle existing traffic incidents and well prepare for potential ones.

  • Human driving behaviors and traffic waves (collaboration with Ford)
    This project is to demonstrate a bifurcation perspective of the discrete-time dynamical system in a car-following context.

  • Resilient nurse rescheduling model under demand uncertainty and labor absenteeism (collaboration with Henry Ford Macomb Hospital)
    This project is to find an optimal policy for both maintaining the quality of patient care and respecting the welfare of nurses. A crucial problem worthy of consideration is uncertainty on a daily basis due to emergency responses for patients and absenteeism among registered nurses. The uncertainty leads to an unforeseen shortage of nursing workload, and as a result, decreases standards of care.

  • Effect of production and consumption taxes on emission control under market uncertainty
    This research proposes a game theory based model for evaluating and comparing the effect of production and consumption taxes on promoting the usage of new technology and controlling emission under market uncertainty and instability

  • Car following model using V2V communication-based technology
    This research models homogeneous car following behavior with information sharing between vehicles using advanced V2V communication-based technology. This information sharing assists drivers’ interactions and promotes safety pilots.


Google Scholar Citations

Papers Published or Accepted for Publication in Refereed Journals

  • Hu, J., Zhang, D. and Xu, H., Data-Driven Distributionally Preference Robust Optimization Models Based on Random Utility Representation in Multi-Attribute Decision Making, http://www.optimization-online.org/DB_HTML/2021/09/8582.html, 2022.
  • Ren, J., Chen, X. and Hu, J., The Effect of Production- Versus Consumption-Based Emission Tax under Demand Uncertainty. International Journal of Production Economics, 219:82-98, 2020.
  • Hu, J., Li, J., and Mehrotra, S., A Data-Driven Functionally Robust Approach for Simultaneous Pricing and Order Quantity Decisions with Unknown Demand Function, Operations Research, 67(6):1564-12585, 2019.
  • Hu, J., Bansal, M., and Mehrotra, S., Robust Decision Making Using a General Utility Set, European Journal of Operational Research, 269(2):699-714, 2018.
  • Hu, J. and Stepanyan, G., Optimization with Reference-Based Robust Preference Constraints, SIAM Journal on Optimization, 27(4):2230-2257, 2017.
  • Hu, J. and Mehrotra, S., Robust Decision Making over a Set of Random Targets or Risk-Averse Utilities with an Application to Portfolio Optimization, IIE Transactions, 47(4):358-372, 2015.
  • Hu, J., Homem-de-Mello, T., and Mehrotra, S., Stochastically Weighted Stochastic Dominance Concepts with an Application in Capital Budgeting, European Journal of Operational Research, 223(3):572-583, 2014.
  • Hu, J. and Chan, Y., Stochastic Incident-Management of Asymmetrical Network-Workloads, Transportation Research Part C, 27: 140-158, 2013.
  • Hu, J. and Mehrotra, S., Robust and Stochastically Weighted Multi-Objective Optimization Models and Reformulations, Operations Research, 60(4):936-953, 2012.
  • Hu, J., Homem-de-Mello, T., and Mehrotra, S., Sample Average Approximation for Stochastic Dominance Constrained Programs, Mathematical Programming Series A, 133(1-2): 171-201, 2012.
  • Hu, J., Homem-de-Mello, T. and Mehrotra, S., Risk Adjusted Budget Allocation Models with Application in Homeland Security, IIE Transactions, 43(12): 819-839, 2011.
  • Hu, J., and Chan, Y., A Dynamic Shortest-Path Algorithm for Continuous Arc Travel-Times: Implication for Traffic Incident Management, Transportation Research Record No. 2089:51-57, 2008.

Conference Proceedings

  • Guo, Y., Hu, J., and Su, W., Stochastic Optimization for Economic Operation of Plug-in Electric Vehicle Charging Stations at a Municipal Parking Deck Integrated with On-site Renewable Energy Generation, in Proceedings of 2014 IEEE Transportation Electrification Conference and Expo, Dearborn, Michigan, U.S.A. June 15-18, 2014.
  • Hu, J. and Chan, Y., Dynamic Routing to Minimize Travel Time and Incident Risks, in Proceedings of the 10th International Conference on Application of Advanced Technologies in Transportation, Paper ID 403. pp. 28-30, Athens, Greece, 2008.
  • Hu, J. and Chan, Y., Stochastic Incident-Management of Asymmetrical Network-Workloads, TRB 85th Annual Meeting, #06-1596, Washington, DC, 2006.
  • Hu, J. and Chan, Y., A Multi-criteria Routing Model for Incident Management, in Proceedings of 2005 IEEE International Conference on Systems, Man and Cybernetics, pp. 832 – 839, Waikoloa, Hawaii, October 10-12, 2005.


Courses taught at the University of Michigan Dearborn

  • IMSE 606 Advanced Stochastic Processes
  • IMSE 605 Advanced Optimization
  • IMSE 505 Optimization
  • IMSE 500 Models of Operations Research
  • IMSE 3005 Introduction of Operations Research
  • IMSE 317 Engineering Probability and Statistics


Current graduate students

  • PhD students

    • Rajul Jian
    • Shixin Liu

Former graduate students

  • Jun Long, M.S. “Decision making applications with optimization and predictive modeling”, MSE in Industrial Systems Engineering, April 2014, at Ford GDIA.
  • Junxuan Li, M.S. “Functional Sensitivity Analysis Method and Functionally Robust Optimization in Decision-Making under Uncertainty”, June 2016, at Georgia Institute of Technology.
  • Wen Feng, M.S. “Assessment of social preference in automotive market using generalized multinomial logistic regression”, December 2016, at Ford GDIA.
  • Wenjie Wang, M.S. “A two-stage dynamic programming model for nurse rostering problem under uncertainty,” December 2017, at Fiat Chrysler.
  • Jie Ren, M.S. (co-advised with Dr. Xi Chen) “The effect of production versus consumption based emission tax under demand uncertainty”, December 2017, at Ford GDIA.
  • Chuan Cheng, M.S. “Labor optimization under uncertainty of workers' absenteeism and workforce operation & management system design”, November 2018, at Ford GDIA.
  • Dania Ammar, M.S. “Two-stage risk adjusted cross-training stochastic programming model for improving daily operations under staff absenteeism”, April 2019, at the University of Michigan - Dearborn.
  • Gevorg Stepanyan, ph.D. "Preference ambiguity averse decision making using robust optimization and sensitivity analysis", January 2021, at the University of Michigan - Dearborn.

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Phone Number

(313) 583-6745

Fax Number

(313) 593-3692



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