Full-time Professor

Baolong Liu

Publish Time:2020-05-29

Baolong Liu

Assistant Professor



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Research Area

Sustainable Supply Chain Management, Supply Chain Co-ordination, Operations Management, Operations Research


Contact Information

liubl@shanghaitech.edu.cn




Biography

Dr. Baolong Liu obtained his bachelor's (2011) and master's (2014) degrees both in the Department of Industrial Engineering at Tsinghua University. In September of 2018, he earned his Ph.D. degree at ESSEC Business School (École Supérieure des Sciences Economiques et Commerciales) in France. Right after, he began to work as a postdoctoral research associate at the Department of Supply Chain Management, W. P. Carey School of Business at Arizona State University. He joined ShanghaiTech University in May 2020. He is an assistant professor, PI at the School of Entrepreneurship and Management.


Research Interests

The main research interests of Dr. Liu include dynamic inventory, pricing management in the framework of the green supply chain; operations management and analysis with emerging technologies; supply chain coordination. The main methodologies he uses are operations research (integer programming, stochastic optimization, Markov decision processes, etc.) and game-theoretical modeling techniques. He serves as an ad hoc reviewer for several academic journals such as Production & Operations Management and European Journal of Operational Research.


Selected Publications

Publication:

1. Baolong Liu and Pietro De Giovanni. Green Process Innovation Through Industry 4.0 Technologies and Supply Chain Coordination. Annals of Operations Research (2019).


Working Papers:

1. Baolong Liu and Felix Papier. Remanufacturing of Multi-Component Systems with Product Substitution. Submitted to European Journal of Operational Research (2020).


2. Joint Dynamic Pricing and Return Quality Strategies Under Cannibalization (Working paper).

 

3. The Value of Online Influencers in Fast Fashion: Flexible Supply Chain and Advertising (Working paper).

 

4. Core Optimization: Heuristic Algorithms for Facility Location Problems (Work in progress).