Ling Wang, Ph.D.
Professor
Tsinghua University
Department of Automation, Tsinghua University
Beijing 100084, China
Tel: +86 (10) 623125-272
Fax: +86 (10) 62786911
Email: wangling@tsinghua.edu.cn  


 

Education background

Ph. D. in Control Theory and Control Engineering, Tsinghua University, Beijing, China, 1999

BS in Process Control Engineering, Tsinghua University, Beijing, China, 1995   

Experience

Full Professor, Department of Automation, Tsinghua University, 2008.12~

Visiting Scholar, Department of Industrial and Operations Engineering, University of Michigan, 2007.01-2008.01

Associate Professor, Department of Automation, Tsinghua University, 2002.12~2008.11

Assistant Professor, Department of Automation, Tsinghua University, 1999.10~2002.11  

Social service

[1] Academic and Social Service [1] Committee Member, Technique Committee of Process Control, CAA

[2] Exective Member, Technique Committee of Scheduling, ORSC

[3] Exective Member, Technique Committee of Intelligent Industrial Data Analysis and Optimization, ORS

[4] Exective Member, Technique Committee of Intelligent Optimization, CAAI

[5] Exective Member, Beijing Automation Association

[6] Co-Editor-in-Chief, The Open Operational Research Journal

[7] Editorial Board Member, International Journal of Automation and Control

[8] Editorial Board Member: International Journal of Artificial Intelligence and Soft Computing

[9] Editorial Board Member, Memetic Computing

[10] Associate Editor, Swarm and Evolutionary Computation 

Areas of Research Interests/ Research Projects

Intelligent optimization theory, algorithms and applications

Modeling, optimization and scheduling for production manufacturing systems  

Research Status

[1] NSFC for Distinguished Young Scholars (61525304): Theory and algorithms for intelligent optimization and scheduling. (PI) (2016.1~2020.12)

[2] NSFC Project (61174189): Study on complex resource constrained project scheduling problems and memetic algorithms. (PI) (2012.1~2015.12)

[3] NSFC Project (70871065): Study on learning-based swarm intelligent scheduling theory and algorithms. (PI) (2009.1~2011.12)

[4] NSFC Project (60774082): Optimization and scheduling theory and algorithms based on differential evolution and quantum evolution for complex manufacturing systems. (PI) (2008.1~2010.12)

[5] NSFC Project (60374060): Study on intelligent simulation optimization theory and algorithms for complex manufacturing systems. (PI) (2004.1~2006.12)

[6] NSFC Project (60204008): Computational intelligence based hybrid optimization theory and algorithms for complex systems. (PI) (2003.1~2005.12)

[7] NSFC Project (60834004): Research on theories and algorithms of real-time scheduling and optimization control for complex manufacturing process of chips and their applications. (Investigator) (2009.1~2012.12)

[8] Program for New Century Excellent Talents in University (NCET-10-0505). (PI) (2010.1~2012.12)

[9] Doctoral Program Foundation of Institutions of Higher Education of China (20130002110057): Study on distributed shop scheduling based on cooperative estimation of distribution algorithms. (PI) (2014.1~2016.12

[10] Doctoral Program Foundation of Institutions of Higher Education of China (20100002110014): Study on resource constrained project scheduling based on novel hybrid swarm intelligence. (PI) (2011.1~2013.12)

[11] Young Talent of Science and Technology of Beijing City (2004A41). (PI) (2004.7~2007.7)

[12] The Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry: Study on optimization and scheduling based on hybrid differential evolution. (PI) (2009.1~2010.12)

[13] 973 Sub-project (2013CB329503):Brain information based encoding and decoding oriented machine learning approaches. (Investigator) (2013.01~2017.12)

[14] 973 Sub-Project (2009CB320602):Study on real time intelligent operation optimization theory and methods based on data and knowledge for complex manufacturing total process. (Investigator) (2011.01~2013.08)

[15] 973 Sub-Project (2002CB312203): Study on real-time, intelligent operation and optimization theories and methods for complex manufacturing process. (Investigator) (2002.12~2008.5)

[16] 863 Project (2007AA04Z155): Intelligent planning and dynamic optimization & scheduling technologies for manufacturing processes in process industrial enterprises. (Co-PI) (2008.1~2009.12)

[17] National Science and Technology Major Project of China (2011ZX02504-008):Study on intelligent scheduling and quality optimization techniques for integrated circuits manufacturing line. (Investigator) (2011.1~2013.12)

Honors And Awards

[1] 2015’ National Science Fund for Distinguished Young Scholars of China

[2] 2009’ Program for New Century Excellent Talents in University

[3] 2009’ Academic Young Talent of Tsinghua University

[4] 2004’ Young Talent of Science and Technology of Beijing City

[5] 2010’ SCOPUS Young Researcher New Star Scientist Award

[6] 2014’ National Natural Science Award (2nd Place Prize)

[7] 2011’ Electronics and Information Science and Technology Award (2nd Place Prize) by Chinese Institute of

Electronics

[8] 2008’ Science and Technology Award (3rd Place Prize) by Beijing City

[9] 2007’ Natural Science Award (2nd Place Prize) by MOE of China

[10] 2003’ National Natural Science Award (1st Place Prize) nominated by MOE of China

[11] 2014’ Best Paper Award of ACTA Automatica Sinica

[12] 2005-2010 Engineering Applications of Artificial Intelligence Top Cited Article Awarded by Elsevier

[13] 2015’ Best Paper Award of ICHSA’2015

[14] 2014’ Poster Award of CPCC’2014

[15] 2013’ Best Paper Award of IWACIII’2013

[16] 2011’ Best Paper Award of ICIC’2011

[17] 2010’ Finalist for Zhang Si-Ying Outstanding Youth Paper Award, CCDC’2010

[18] 2006’ Excellent Paper of IET-ICT’2006

[19] 2002’ Outstanding Paper Award of IEEE-ICMLC’2002

[20] 2004’ Excellent Paper of CCDC'2004

[21] Outstanding Ph.D. Dissertation Award of Tsinghua University (1st Place Prize)

[22] Excellent Textbook of Tsinghua University (2nd Place Prize) (2004, 2008, 2012)

[23] Outstanding Professor and Mentor of Tsinghua University (2014)

[24] Excellent Class Advisor of Tsinghua University (1st Place Prize) (2004, 2005)

Academic Achievement

SELECTED PUBLICATIONS:

[1] Wang L, Qian B. Hybrid differential evolution and scheduling algorithms. Beijing: Tsinghua University Press, 2012.

[2] Wang L, Liu B. Particle swarm optimization and scheduling algorithms. Beijing: Tsinghua University Press, 2008

[3] Wang JC, Wang L, Jin YH (Translation). Process dynamics and control (2nd edition). Beijing: Publishing House of Electronics Industry, 2006.

[4] Wang L. Shop scheduling with genetic algorithms. Beijing: Tsinghua University & Springer Press, 2003.

[5] Wang L. Intelligent optimization algorithms with applications. Beijing: Tsinghua University & Springer Press, 2001.

[6] Wang SY, Wang L. An estimation of distribution algorithm-based memetic algorithm for the

distributed assembly permutation flow-shop scheduling problem. IEEE Transactions on Systems, Man,

and Cybernetics: Systems. (Regular Paper).

[7] Shi L, Jiang YH, Wang L, Huang DX. Efficient Lagrangian decomposition approach for solving

refinery production scheduling problems involving operational transitions of mode switching.

Industrial & Engineering Chemistry Research, 2015, 54(25): 6508-6526.

[8] Zheng HY, Wang L. Reduction of carbon emissions and project makespan by a Pareto-based

estimation of distribution algorithm. International Journal of Production Economics, 2015, 164:

421-432.

[9] Wang SY, Wang L, Liu M, Xu Y. An order-based estimation of distribution algorithm for

stochastic hybrid flow-shop scheduling problem. International Journal of Computer Integrated

Manufacturing, 2015, 28(3): 307-320.

[10] Zheng HY, Wang L. An effective teaching-learning-based optimization algorithm for RCPSP with

ordinal interval numbers. International Journal of Production Research, 2015, 53(6): 1777-1790

.[11] Zhang X, Chen MY, Wang L, Peng ZH, Zhou DH. Connection-graph-based event-triggered output

consensus in multi-agent systems with time-varying couplings. IET Control Theory and Applications,

2015, 9(1): 1-9.

[12] Shi L, Jiang YH, Wang L, Huang DX. Refinery production scheduling involving operational

transitions of mode switching under predictive control system. Industrial & Engineering Chemistry

Research, 2014, 53(19): 8155-8170.

[13] Pan QK, Wang L, Li JQ, Duan JH. A novel discrete artificial bee colony algorithm for the

hybrid flowshop scheduling problem with makespan minimization. OMEGA-International Journal of

Management Science, 2014, 45: 42-56.

[14] Wang L, Fang C, Mu CD, Liu M. A Pareto-archived estimation-of-distribution algorithm for

multi-objective resource-constrained project scheduling problem. IEEE Transactions on Engineering

Management, 2013, 60(3): 617-626. (Regular Paper).

[15] Wang SY, Wang L, Liu M, Xu Y. An effective estimation of distribution algorithm for solving

the distributed permutation flow-shop scheduling problem. International Journal of Production

Economics, 2013, 145(1): 387-396.

[16] Pan QK, Wang L, Sang HY, Li JQ, Liu M. A high performing memetic algorithm for the flowshop

scheduling problem with blocking. IEEE Transactions on Automation Science and Engineering, 2013,

10(3): 741-756. (Regular Paper).

[17] Wang L, Zhou G, Xu Y, Liu M. A hybrid artificial bee colony algorithm for the fuzzy flexible

job-shop scheduling problem. International Journal of Production Research, 2013, 51(12): 3593-

3608.

[18] Wang L, Wang SY, Liu M. A Pareto-based estimation of distribution algorithm for the multi-

objective flexible job-shop scheduling problem. International Journal of Production Research,

2013, 51(12): 3574-3592.

[19] Wang L, Zheng XL, Wang SY. A novel binary fruit fly optimization algorithm for solving the

multidimensional knapsack problem. Knowledge-Based Systems, 2013, 48: 17-23.

[20] Pan QK, Wang L, Mao K, Zhao JH, Zhang M. An effective artificial bee colony algorithm for a

real-world hybrid flowshop problem in steelmaking process. IEEE Transactions on Automation Science

and Engineering, 2013, 10(2): 307-322. (Regular Paper).

[21] Wang L, Wang SY, Xu Y, Zhou G, Liu M. A bi-population based estimation of distribution

algorithm for the flexible job-shop scheduling problem. Computers & Industrial Engineering, 2012,

62(4): 917-926.

[22] Fang C, Wang L. An effective shuffled frog-leaping algorithm for resource-constrained project

scheduling problem. Computers & Operations Research, 2012, 39(5): 890-901.

[23] Pan QK, Wang L. Effective heuristics for the blocking flowshop scheduling problem with

makespan minimization. OMEGA-International Journal of Management Science, 2012, 40(2): 218-229.

[24] Wang L, Fang C. An effective estimation of distribution algorithm for the multi-mode

resource-constrained project scheduling problem. Computers & Operations Research, 2012, 39(2):

449-460.

[25] Wang L, Li LP. Fixed-structure H∞ controller synthesis based on differential evolution with

level comparison. IEEE Transactions on Evolutionary Computation, 2011, 15(1): 120-129. (Regular

paper)

[26] Wang L, Fang C. An effective shuffled frog-leaping algorithm for multi-mode resource-

constrained project scheduling problem. Information Sciences, 2011, 181(20): 4804-4822.

[27] Liu B, Wang L, Liu Y, Wang SY. A unified framework for population-based metaheuristics.

Annals of Operations Research, 2011, 186(1): 231-262.

[28] Wang L, Pan QK, Tasgetiren MF. A hybrid harmony search algorithm for the blocking permutation

flow shop scheduling problem. Computers & Industrial Engineering, 2011, 61(1): 76-83.

[29] Pan QK, Wang L, Gao L, Li WD. An effective hybrid discrete differential evolution algorithm

for the flow shop scheduling with intermediate buffers. Information Sciences, 2011, 181(3): 668-

685.

[30] Pan QK, Suganthan PN, Wang L, Gao L, Mallipeddi R. A differential evolution algorithm with

self-adapting strategy and control parameters. Computers & Operations Research, 2011, 38(1): 394-

408.

[31] Wang L, Li LP. An effective differential evolution with level comparison for constrained

engineering design. Structural and Multidisciplinary Optimization, 2010, 41(6): 947-963.

[32] Liu B, Wang L, Liu Y, Qian B, Jin YH. An effective hybrid particle swarm optimization for

batch scheduling of polypropylene processes. Computers & Chemical Engineering, 2010, 34(4): 518-

528.

[33] Wang L, Huang FZ. Parameter analysis based on stochastic model for differential evolution

algorithm. Applied Mathematics and Computation, 2010, 217(7): 3263-3273.

[34] Wang L, Pan QK, Suganthan PN, Wang WH, Wang YM. A novel hybrid discrete differential

evolution algorithm for blocking flow shop scheduling problems. Computers & Operations Research,

2010, 37(3): 509-520.

[35] Qian B, Wang L, Hu R, Huang DX, Wang X. A DE-based approach to no-wait flow-shop scheduling.

Computers & Industrial Engineering, 2009, 57(3): 787-805.

[36] Qian B, Wang L, Huang DX, Wang X. Multi-objective no-wait flow-shop scheduling with a memetic

algorithm based on differential evolution. Soft Computing, 2009, 13(8-9): 847-869.

[37] Pan QK, Wang L, Qian B. A novel differential evolution algorithm for bi-criteria no-wait flow

shop scheduling problems. Computers & Operations Research, 2009, 36(8): 2498-2511.

[38] Qian B, Wang L, Huang DX, Wang X. An effective hybrid DE-based algorithm for flow shop

scheduling with limited buffers. International Journal of Production Research, 2009, 47(1): 1-24.

[39] Qian B, Wang L, Huang DX, Wang WL, Wang X. An effective hybrid DE-based algorithm for multi-

objective flow shop scheduling with limited buffers. Computers & Operations Research, 2009, 36(1):

209-233.

[40] Li BB, Wang L, Liu B. An effective PSO-based hybrid algorithm for multi-objective permutation

flow shop scheduling. IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and

Humans, 2008, 38(4): 818-831. (Regular paper)

[41] Liu B, Wang L, Jin YH. An effective hybrid PSO-based algorithm for flow shop scheduling with

limited buffers. Computers & Operations Research, 2008, 35(9): 2791-2806.

[42] Li BB, Wang L. A hybrid quantum-inspired genetic algorithm for multi-objective flow shop

scheduling. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 2007, 37(3):

576-591. (Regular paper).

[43] Liu B, Wang L, Jin YH. An effective PSO-based memetic algorithm for flow shop scheduling.

IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 2007, 37(1): 18-27.

(Regular paper). (ESI)

[44] He Q, Wang L. An effective co-evolutionary particle swarm optimization for constrained

engineering design problems. Engineering Applications of Artificial Intelligence, 2007, 20(1): 89

-99. (ESI)

[45] Wang L, Zhang L, Zheng DZ. An effective hybrid genetic algorithm for flow shop scheduling

with limited buffers. Computers & Operations Research, 2006, 33(10): 2960-2971.

[46] Liu B, Wang L, Jin YH, Tang F, Huang DX. Improved particle swarm optimization combined with

chaos. Chaos, Solitons and Fractals, 2005, 25(5): 1261-1271. (ESI)

[47] Wang L, Zheng DZ. An effective hybrid heuristic for flow shop scheduling. International

Journal of Advanced Manufacturing Technology, 2003, 21(1): 38-44.

[48] Jiang YH, Wang L, Jin YH. Bottleneck analysis for network flow model. Advances in Engineering

Software, 2003, 34(10): 641-651.

[49] Zhou T, Wang L, Sun ZS. Closed-loop model set validation under a stochastic framework.

Automatica, 2002, 38(9): 1449-1461.

[50] Wang L, Zheng DZ. An effective hybrid optimization strategy for job-shop scheduling problems.

Computers & Operations Research, 2001, 28(6): 585-596.


Curriculum

[1] Intelligent optimization algorithms and applications. (for undergraduate students)

[2] Principles of Automatic Control. (for undergraduate students)

[3] Production scheduling and intelligent optimizations. (for graduate students)

[4] Neural networks. (for graduate students)

[5] Literatures retrieving and paper writing. (for engineering graduate students)