Professor and Department Chair Haitao Li
Office: 208 Express Scripts Hall
Dr. Li's primary goal as an educator is two-fold. First, to disseminate knowledge in my domains of expertise in Supply Chain and Analytics, and second, to cultivate students’ interest and ability to acquire and apply knowledge.
PhD, Production and Operations Management, The University of Mississippi, 2005
M.A. Economics, The University of Mississippi, 2002
B.E. International Trade and Aeronautical Engineering, Beijing University of Aeronautics and Astronautics, 2000
SCMA 3301 Intro to Supply Chain Management
SCMA 5320 Operations Management
SCMA 6331 Supply Chain Modeling
SCMA 7302 Advanced Optimization Methods
Dr. Li's research is motivated by his passion to enhance the quality and efficiency of real-life decision-making, with the ultimate goal of benefiting society. His research centers on developing models and algorithms that combine the complementary strengths of descriptive, predictive, and prescriptive analytics/optimization for applications in manufacturing, service, agriculture, construction, and healthcare. Dr. Li's research projects have been funded by the U.S. Army Research Office, U.S. DOT, Association Supply Chain Management (ASCM), International Agricultural Trade and Research Consortium (IATRC), Missouri Agriculture and Small Business Development Authority (MASBDA), and HP Labs, Ameren and the ESI private sector.
Dr. Li is the Director of Laboratory of Advanced Supply Chain Analytics (LASCA).
Li, H., C. Halmen and L. Yang (2020). Physical Resource Optimization System and Associated Method of Use, U.S. National Patent Application Publication No.: US 2020/0226518, Publication Date: July 16, 2020.
Santos, P., H. Li and I. Lopez (2014). Method Generating Optimal Project Portfolios, Invention Disclosure, submitted.
Li, H. (2013). System and Method of Stochastic Resource-Constrained Project Scheduling, U.S. Patent Application 61/795,574, filed October 2013. Not granted.
Li, H. (2012). Hybrid Architecture in Approximate Dynamic Programming for Project Scheduling, Provisional Patent Application submitted, Attorney Docket Number: 13UMS002prov.
Li, H. (2012). Stochastic Resource-Constrained Project Scheduling, Provisional Patent Application submitted, Attorney Docket Number: 11UMS001prov.
Santos, C. A., H. Li, T. Gonzalez, H. Davis, S. Perez, F. Orozco, O. Fernandez and C. Bartolini (2013). Project Portfolio Optimization (PPO), U.S. Patent Application 13/874,181.
Santos, C. and Tere Gonzalez, Xin Zhang, Shelen Jain, Andrei Fuciec, Haitao Li, Claudia Marquez-Nava, Christopher Mejia (2011). Optimizing Workforce Capacity and Capability, Patent Application submitted, Attorney Docket Number: 201001914.
Xie, F., H. Li and Zhe Xu (2021), An Approximate Dynamic Programming Approach to Project Scheduling with Uncertain Resource Availabilities, Applied Mathematical Modeling, Vol. 97, p.226-243.
Li, F., Z. Xu and H. Li (2021), A Multi-Agent Based Cooperative Approach to Decentralized Multi-Project Scheduling and Resource Allocation, Computer and Industrial Engineering, Vol. 151, 106961.
Tang, W., H. Li and J. Chen (2021), Optimizing Carbon Taxation Target and Level: Enterprises, Consumers, or Both?, Journal of Cleaner Production, Vol. 282, 124515.
Li, H., D. Jiang and D. Li (2021), Optimizing the Configuration of a Food Supply Chain, International Journal of Production Research, Vol. 59, No. 12, p.3722-3746.
Yang, L., H. Li and J. Campbell (2020), Improving Order Fulfillment Performance through Integrated Inventory Management in a Multi-Item Finished Goods System, Journal of Business Logistics, Vol. 41, No. 1, p.54-66.
Solomon, S., H. Li, K. Womer, C. Santos (2019), Multi-Period Stochastic Resource Planning in Professional Service Organizations, Decision Sciences, Vol. 50, No. 6, p1281-1318.
Li, H., L. Mai, W. Zhang, X. Tian (2019), Optimizing the Credit Term Decisions in Supply Chain Finance, Journal of Purchasing and Supply Management, Vol. 25, No. 2, p146-156.
Li, H., C. Santos, A. Fuciec, T. Gonzalez, S. Jain, C. Marquez, C. Mejia, A. Zhang (2018), Optimizing the Labor Strategy of a Professional Service Firm, IEEE Transactions on Engineering Management, Vol. 66, No. 3, p443-458.
Li, H. (2017), Stochastic Single-Machine Scheduling with Learning Effect, IEEE Transactions on Engineering Management, Vol. 64, No. 1, p94-102.
Womer, K., H. Li, J. Camm, C. Osterman, R. Radhakrishnan (2017), Learning and Bayesian Updating in Long Cycle Made-to-order (MTO) Production, Omega, Vol. 69, p29-42.
Li, H. and B. Alidaee (2016), Tabu Search for Solving the Black-and-White Traveling Salesman Problem, to appear in the Journal of the Operational Research Society, Vol. 67, No. 8, p1061-1079.
Li, H. and K. Womer (2015), Solving Stochastic Resource-Constrained Project Scheduling Problems by Closed-loop Approximate Dynamic Programming, European Journal of Operational Research, Vol. 246, No. 1, p20-33.
Santos, C., T. Gonzalez, H. Li, K.-Y. Chen, D. Beyer, S. Biligi, Q. Feng, R. Kumar, S. Jain, R. Ramanujan, A. Zhang (2013), HP Enterprise Services Uses Optimization for Resource Planning, Interfaces, Vol. 43, No. 2, p152-169.
Li, H. and K. Womer (2012), Optimizing the Supply Chain Configuration for Make-to-Order Manufacturing, European Journal of Operational Research, Vol. 221, No. 1, p118-128.
Li, H. and D. Jiang (2012), New Model and Heuristics for Safety Stock Placement in General Acyclic Supply Chain Networks, Computers and Operations Research, Vol. 39, No. 7, p1333-1344.
Amini, M. and H. Li (2011), Supply Chain Configuration for Dynamic Diffusion of New Products: An Integrated Optimization Approach, Omega, Vol. 39, No. 3, p313-322.
Rego, C., H. Li and F. Glover (2011), A Filter-and-Fan Approach to the 2D Lattice Model of the Protein Folding Problem, to appear in Annals of Operations Research, Vol. 188, No. 1, p389-414.
Li, H. and K. Womer (2009), A Decomposition Approach for Shipboard Manpower Scheduling, Military Operations Research, Vol. 14, No. 3, p1-23.
Li, H. and K. Womer (2009), Scheduling Projects with Multi-Skilled Personnel by a Hybrid MILP/CP Benders Decomposition Algorithm, Journal of Scheduling, Vol. 12, No. 3, p281-298.
Hewlett-Packard Laboratory (HP Lab)
Naval Personnel Research, Study and Technology (NPRST)