Dr. Quan-lin Li received his doctorate degree from Institute of Applied Mathematics, Chinese Academy of Sciences, Beijing in 1998. After then, he was an associate professor at the State Key Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing from July 1999 to December 2003. From December 2003 to October 2009, he was an associate professor at Department of Industrial Engineering, Tsinghua University. From October 2009 to January 2019, he was a full professor at School of Economics and Management, Yanshan University. Up to now, he has been a full professor at the School of Economics and Management, Beijing University of Technology.
Since September 1999, Dr. Quan-lin Li has visited in some universities such as Winnipeg University in Canada, Carleton University in Canada, University of Hong Kong, Hong Kong University of Science and Technology, Chinese University of Hong Kong, Complutense University of Madrid in Spain, University of Macau and so forth. His research interests include stochastic models, Stochastic processes, queuing networks, Markov decision processes, game theory, computer networks, network security, network resource management, network entropy decision-making, mean-field theory, nonlinear Markov processes, supermarket models, work stealing models, RFID technologies, Internet of Things, big data, cloud computing, blockchain, data center networks, healthcare, COVID-19 stochastic modeling, bike sharing systems, sharing economy，supply chain management and so on.
For his theoretical research, Dr. Li Quan-Lin systematically developed The RG-factorization method of stochastic models, and set up the basic theory of RG-factorizations. By using RG-factorization method, it is lucky to be able to solve some difficult and complicated stochastic systems, such as queuing networks, computer networks, network security, Internet of Things, cloud computing, data center networks, stochastic modeling of blockchain, inventory control, sharing economy, bike sharing systems, and healthcare systems. Also, the RG-factorizations can strongly support performance evaluation, optimization and dynamic decision, risk management and others, and also provide effective computational methods and algorithms for dealing with large-scale or complex-structured stochastic models in practice. On this research line, Quan-Lin Li published an English monograph Constructive Computation in Stochastic Models with Applications: RG-Factorizations by Springer, the first edition in 2010, and the second edition in 2023.
 National Natural Science Foundation of China. Research on black hole effect, multi-stable domain and networking resource loss evaluation of large-scale networks. 2017.01-2020.12.
 National Natural Science Foundation of China. Nonlinear Markov process and super-exponential structure of supermarket networks under real-time queuing control. 2015.01-2018.12.
 National Natural Science Foundation of China. Research on random load balancing strategy and supermarket models of large-scale parallel queuing network. 2013.01-2016.12.
 National Natural Science Foundation of China. Research on multi-level approximation methods of self-similar networks and their performance models. 2009.01- 2011.12.
 National Natural Science Foundation of China. Stochastic models in network security and key techniques for performance evaluation. 2007.01-2009.12.
 National 973 Program (Tsinghua Sub-project). Stochastic methods in life science and network technologies. 2007.01-2011.12.
 National Natural Science Key Fund Project (Tsinghua Sub-project). Design, construction, modeling and analysis of engineering gene regulatory networks. 2008.01-2011.12.
 Hebei Natural Science Foundation Project. Research on multi-chain coupled queuing network and medical insurance incentive mechanism of hierarchical diagnosis and treatment and two-way referral. 2018.01-2020.12.
 Hebei Higher Education Innovation Team Leading Talent Program. Research on nonlinear calculation of large-scale network and risk management of port coal supply chain management. 2014.01-2016.12.
 Hebei Natural Science Foundation Project. Research on hierarchical calculation and data management technologies for multi-dimensional stochastic models in network security. 2012.01-2014.12.
Award Name: Best Paper Award. The 7th International Conference on Computational Data and Social Networks.
Paper Name: Blockchain Queue Theory
Authors Name: Quan-Lin Li, Jing-Yu Ma, Yan-Xia Chang
Award Name: International INFORMS Paper Award
Paper Name: Operational Performance Evaluation of Reverse Referral Partnership in the Chinese Healthcare System
Authors Name: Na Li, Nan Kong, Quan-Lin Li, Zhibin Jiang
Award Name: The Second Prize of Hebei Science and Technologies (Natural Science)
Project Name: Research on Basic Theory of Numerical Calculation of Multidimensional Stochastic Models
Award Winners: Quan-Lin Li, Chuang Li, Na Li, Nai-shuo Tian
Award Name: The Leading Talents Program of the Innovation Team of Hebei Higher Education Institutions
Project Name: Research on Nonlinear Calculation of Large-Scale Supermarket Network and Risk Management of Port Coal Supply Chain Management
Award Winner: Quan-Lin Li
Award Name: The Second Prize of Beijing Science and Technologies (Natural Science)
Project Name: Basic Research on Computer Network Service Quality (QoS) Evaluation and Control
Award Winners: Chuang Li, Feng-yuan Ren, Hao Yin, Quan-Lin Li
Award Name: The First Prize of the Ministry of Education Science and Technologies (Natural Science)
Project Name: Stochastic Models and Performance Evaluation of Computer Systems
Award Winners: Chuang Li, Quan-Lin Li, Hao Yin, Feng-yuan Ren, Zhi-guang Shan, Zhang-xi Tan, Ya-ya Wei, Yang Qu
Award Name: New Century Outstanding Talents of the Ministry of Education
Project Name: Research on the Calculation Method of Stochastic Network
Award Winner: Quan-Lin Li
Award Name: Excellent courses in Beijing
Course Name: Operations Research
Award Winners: Xiaobo Zhao (Stochastic models), Hongxuan Huang (Optimal theory), Quan-Lin Li (Decision method)
2012, 2014, The 8, 9th International Conferences on Matrix-Analytic Methods in Stochastic Models. Technical Programme Committee, TPC member
2007-2019, The 7, 8, 9, 11, 12th International Workshop on Retrial Queues. Technical Programme Committee, TPC Chairman or member
2015-Now, The 14, 15, 16, 17, 18, 19, 20th International Conference (hosted in Russia) on Information Technology and Mathematical Modeling. International Program Committee, TPC member
2006-2017, The 3th to 12th International Conference on Queueing Theory and Network Applications. Technical Programme Committee, TPC member
1999-Now, The Vice Chairman, the Reliability Society of China Operations Research Society
2008-Now, The Commentator of the American MathSciNet. Reviewer
The 8th International Workshop on Retrial Queues Conference. Chairman
The 12th International Conference on Queueing Theory and Network Applications Conference. Chairman.
(1) Stochastic Models
In the study of stochastic models, our works focus on setting up a unified theoretic framwwork through using the RG-factorizations of, such as, Markov processes, Markov reward processes, Markov decision processes, stochastic games, evolutionary game and so on. See Li's 2010 Springer book: Constructive Computation in Stochastic Models with Applications: The RG-Factorizations. For the RG-factorizations, some interesting and useful remarks were given by V. Vigon (2013): LU-Factorization Versus Wiener-Hopf Factorization for Markov Chains.
So far, the RG-factorizations have played an important role in performance evaluation, optimization and dynamic decision for many practical stochastic systems. Using the RG-factorizations, we gave several more general and interesting results including the R-measure expression for the stationary probability vector, two types of expressions for the quasi-stationary probability vector, the general solution to the Poisson equations, numerical methods for Markov processes, the mean-field theory of nonlinear Markov processes, stochastic games and evolutionary games. When applying general Markov processes, the RG-factorizations show a stronger applicable ability, for example, dealing with retrial queues, processor-sharing queues, fluid queues, matched queues; computer networks, manufacturing systems, blockchain systems, sharing economics, healthcare and so on.
Using the censoring technique (also called watched Markov chain given in Lévy [1951, 1952, 1958]), we systematically constructed and devoloped two types of RG-factorizations: 1) UL-type RG-factorization, and 2) LU-type RG-factorization. These RG-factorizations are very useful in the study of, such as, QBD processes, Markov processes, Markov renewal processes, Markov reward processes, Markov decision processes, stochastic game theory and others. Our recent works indicated that the two types of RG-factorizations play a different important role in the study of stochastic models, for example, a) the UL-type RG-factorization is a key to deal with stationary solution to stochastic models, while LU-type RG-factorization can be used to analyze transient solution to stochastic models. We have an optimistic prediction that the RG-factorizations are very useful for sharpening many important research on stochastic processes, stochastic models, computer networks, manufacturing systems, transportation systems, biological sciences and others.
(3) QBD processes
QBD processes provided a useful tool in the study of stochastic models, such as queueing systems, manufacturing systems, computer networks and so on. Chapter 3 of Neuts  gave a complete picture of a level independent QBD process. Latouche and Ramaswami  gave a logarithmic reduction algorithm for the rate matrix. Ramaswami  gave an excellent overview for the QBD processes. Bright and Taylor  provided some effective algorithms for computation of level dependent QBD processes. Naoumov  proposed an algebraically matrix-multiplicative approach to study the stationary probability vector of a QBD process. Ramaswami and Taylor  discussed useful properties of the rate operator in a level dependent QBD processes with a countable number of phases. Li and Cao  gave two types of RG-factorizations, which lead to a unified and complete framework to deal with stationary or transient solution to various QBD processes. Li and Liu  provided a sensitivity analysis for a perturbed QBD process. Nielsen and Ramaswami  and Li and Lin  studied QBD processes with continuous phases.
(4) Quasi-stationary distributions
The study of the quasi-stationary behavior was initiated by Yaglom . Since then, significant advances have been made through the efforts of many researchers, e.g., see a continuously updating overview by Pollett . For the quasi-stationary distributions of block-structured Markov chains, some preliminary results for Markov chains of GI/M/1 type and of M/G/1 type were obtained by Quan-Lin Li , while a complete work were obtained in Li and Zhao [2002, 2003], and further results in Li's book . The quasi-stationarity distributions of the QBD processes can be found in Makimoto , Bean, Bright, Latouche, Pearce, Pollett and Taylor , and Bean, Pollett and Taylor [1998, 2000].
(5) Sensitivity-based optimization theory
Infinitesimal perturbation analysis is a sensitivity-analytic technique for discrete event dynamic systems using single sample path performance measures. The basic approach is to obtain unbiased or strongly consistent estimates for derivatives of the stationary performance measures. For infinitesimal perturbation analysis of Markov chains, Cao, Yuan and Qiu  proposed a novel approach in terms of the realization factor. Perturbation analysis of Markov chains is quite extensive, see Chapter 11 in Li's book: Constructive Computation in Stochastic Models with Applications: RG-Factorizations. Also, he further applied the perturbation analysis of Markov chains to dealing with ESS of stochastic evolutionary games.x
Li et al. applied the sensitivity-based optimization to the optimal energy saving strategy of data center in [2019, 2022], to the optimal dynamic rationing strategy of inventory systems in [2021, 2022], and to the optimal dynamic selfish-mining strategy of blockchain systems in [2022, 2023].
(6) Mean-field theory, and nonlinear Markov processes
(7) Sharing economy, and bike-sharing systems
(8) Platform economy, and medical platform systems
(9) Stochastic models of blockchain systems
(10) Machine learning of stochastic models: Performance optimization, and dynamic decision
(11) Network security
(12) Multiple classes of queueing systems
Multiple-server queue with server vacation (conditional stochastic decomposition), queues with repairable servers (non-exponential life times), queues with negative customers, processor-sharing queues, fluid queues, matched queues, closed queueing networks.
Dr. Quan-lin Li has completed the courses as follows:
(1) Service operation management
(2) Risk management
(3) National Strategies: A Perspective of Chinese History, Culture, Art and Religion
Name: Quan-Lin Li (李泉林)
Degree: Ph D.
School of Economics and Management
Address: Beiijing University of Technology, Beijing 100124, China
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