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@mail.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]Vice Chairman, Technique Committee of Intelligent Simulation Optimization & Scheduling

[2]Committee Member, Technique Committee of Control Theory, CAA

[3]Committee Member, Technique Committee of Process Control, CAA

[4]Executive Member, Technique Committee of Internet of Energy, Chinese Association of Automation

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

[6]Exective Member, Technique Committee of Intelligent Industrial Data Analysis and Optimization, ORSC

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

[8]Exective Member, Beijing Automation Association

[9]Editor-in-Chief, International Journal of Automation and Control

[10]Associate Editor, IEEE Transactions on Evolutionary Computation

[11]Associate Editor, Swarm and Evolutionary Computation

[12]Associate Editor, International J of Applied and Computational Mathematics

[13]Editorial Board Member, International Journal of Artificial Intelligence and Soft Computing

[14]Editorial Board Member, Journal of Optimization

[15]Editorial Board Member, Memetic Computing

[16]Editorial Board Member, Control Theory and Applications

[17]Editorial Board Member, Control and Decision

[18]Editorial Board Member, Control Engineering

[19]Editorial Board Member, Systems Engineering and Electronics

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 (61873328): Collaborative swarm intelligence optimization theories and algorithms for distributed production scheduling. (PI) (2019.1~2022.12)

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

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

[5]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)

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

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

[8]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)

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

[10]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)

[11]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)

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

[13]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)

[14]National Key R&D Program of China (2016YFB0901900): Basic theory of planning, operation and transaction of energy Internet. (PI of Project #1) (2016.07~2020.06)

[15]973 Sub-project (2013CB329503):Brain information based encoding and decoding oriented machine learning approaches. (Investigator) (2013.01~2017.12)

[16]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)

[17]973 Sub-Project (2002CB312203): Study on real-time, intelligent operation and optimization theories and methods for complex manufacturing process. (Investigator) (2002.12~2008.5)

[18]863 Project (2007AA04Z155): Intelligent planning and dynamic optimization & scheduling technologies for manufacturing processes in process industrial enterprises. (Co-PI) (2008.1~2009.12)

[19]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]2016’ Young Scientist Award by CAA

[7]2014’ National Natural Science Award (2nd Place Prize)

[8]2017’ Natural Science Award (3rd Place Prize) by Yunnan Province.

[9]2011’ Electronics and Information Science and Technology Award (2nd Place Prize) by Chinese Institute of Electronics

[10]2008’ Science and Technology Award (3rd Place Prize) by Beijing City

[11]2007’ Natural Science Award (2nd Place Prize) by MOE of China

[12]2003’ National Natural Science Award (1st Place Prize) nominated by MOE of China

[13]2017’ Best Paper Award of Control & Decision

[14]2016’ Best Paper Award of Control Theory & Applications

[15]2014’ Best Paper Award of ACTA Automatica Sinica

[16]Frontrunner 5000, Top Articles in Outstanding S&T Journals of China (No. 7597: S026201203014)

[17]Frontrunner 5000, Top Articles in Outstanding S&T Journals of China (No. 10418: R060201402004)

[18]2005-2010 Engineering Applications of Artificial Intelligence Top Cited Article Awarded by Elsevier

[19]IEEE ICIC Outstanding Leadership Award

[20]2018’ Best Paper Award of ICIC’2018

[21]2015’ Best Paper Award of ICHSA’2015

[22]2014’ Poster Award of CPCC’2014

[23]2013’ Best Paper Award of IWACIII’2013

[24]2011’ Best Paper Award of ICIC’2011

[25]2010’ Finalist for Zhang Si-Ying Outstanding Youth Paper Award, CCDC’2010

[26]2006’ Excellent Paper of IET-ICT’2006

[27]2002’ Outstanding Paper Award of IEEE-ICMLC’2002

[28]2004’ Excellent Paper of CCDC'2004

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

[30]Excellent Textbook of Tsinghua University (2nd Place Prize) (2004, 2008, 2012, 2016)

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

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

Academic Achievement

SELECTED PUBLICATIONS:

[1]Wang L, Wang SY, Fang C. Estimation of distribution algorithms for scheduling. Beijing: Tsinghua University Press, 2017.

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

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

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

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

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

[7]Wang JJ, Wang L. A knowledge-based cooperative algorithm for energy-efficient scheduling of distributed flow-shop. IEEE Transactions on Systems, Man, and Cybernetics: Systems. (Regular Paper).

[8]Wu CG, Li W, Wang L, Zomaya AY. Hybrid evolutionary scheduling for energy-efficient fog-enhanced internet of things. IEEE Transactions on Cloud Computing. (Regular Paper).

[9]Liao ZW, Gong WY, Yan XS, Wang L, Hu CY. Solving nonlinear equations system with dynamic repulsion-based evolutionary algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems. (Regular Paper).

[10]Gong WY, Wang Y, Cai ZH, Wang L. Finding multiple roots of nonlinear equation systems via a repulsion-based adaptive differential evolution. IEEE Transactions on Systems, Man, and Cybernetics: Systems. (Regular Paper).

[11]Chen HK, Tian Y, Pedrycz W, Wu GH, Wang R, Wang L. Hyperplane assisted evolutionary algorithm for many-objective optimization problems. IEEE Transactions on Cybernetics. (Regular Paper).

[12]Sun BQ, Wang L. A decomposition-based matheuristic for supply chain network design with assembly line balancing. Computers & Industrial Engineering.

[13]Wang JJ, Wang L. Decoding methods for the flow shop scheduling with peak power consumption constraints. International Journal of Production Research.

[14]Hu CY, Dai LG, Yan XS, Gong WY, Liu XB, Wang L. Modified NSGA-III for sensor placement in water distribution system. Information Sciences.

[15]Xiang S, Xing LN, Wang L, Zou K. Comprehensive learning pigeon-inspired optimization with tabu list. SCIENCE CHINA Information Sciences.

[16]Lei DM, Li M, Wang L. A two-phase meta-heuristic for multi-objective flexible job shop scheduling problem with total energy consumption threshold. IEEE Transactions on Cybernetics, 2019, 49(3): 1097-1109. (Regular Paper).

[17]Wang L, Lu JW. A memetic algorithm with competition for the capacitated green vehicle routing problem. IEEE/CAA Journal of Automatica Sinica, 2019, 6(2): 516-526.

[18]Jiang ED, Wang L. An improved multi-objective evolutionary algorithm based on decomposition for energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time. International Journal of Production Research, 2019, 57(6): 1756-1771.

[19]Zhang JW, Wang L, Xing LN. Large-scale medical examination scheduling technology based on intelligent optimization. Journal of Combinatorial Optimization, 2019, 37(1): 385-404.

[20]Zheng XL, Wang L. A collaborative multi-objective fruit fly optimization algorithm for the resource constrained unrelated parallel machine green scheduling problem. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 48(5): 790-800. (Regular Paper).

[21]Wang Y, Shi JM, Wang R, Liu Z, Wang L. Siting and sizing of fast charging stations in highway network with budget constraint. Applied Energy, 2018, 228: 1255-1271.

[22]Gong WY, Yan XS, Hu CY, Wang L, Gao L. Fast and accurate parameter extraction for different types of fuel cells with decomposition and nature-inspired optimization method. Energy Conversion and Management, 2018, 174: 913-921.

[23]Wang R, Lai SM, Wu GH, Xing LN, Wang L, Ishibuchi H. Multi-clustering via evolutionary multi-objective optimization. Information Sciences, 2018, 450: 128-140.

[24]Wu CG, Wang L. A multi-model estimation of distribution algorithm for energy efficient scheduling under cloud computing system. Journal of Parallel and Distributed Computing, 2018, 117: 63-72.

[25]Wang L, Zheng XL. A knowledge-guided multi-objective fruit fly optimization algorithm for the multi-skill resource constrained project scheduling problem. Swarm and Evolutionary Computation, 2018, 38: 54-63.

[26]Gao KZ, Wang L, Luo JP, Jiang H, Sadollah A, Pan QK. Discrete harmony search algorithm for scheduling and rescheduling the re-processing problems in remanufacturing: A case study. Engineering Optimization, 2018, 50(6): 965-981.

[27]Wang R, Li GZ, Ming MJ, Wu GH, Wang L. An efficient multi-objective model and algorithm for sizing a stand-alone hybrid renewable energy system. Energy, 2017, 141: 2288-2299.

[28]Zheng HY, Wang L, Zheng XL. Teaching-learning-based optimization algorithm for multi-skill resource constrained project scheduling problem. Soft Computing, 2017, 21(6): 1537-1548.

[29]Deng J, Wang L. A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem. Swarm and Evolutionary Computation, 2017, 32: 121-131.

[30]Zheng XL, Wang L. A knowledge-guided fruit fly optimization algorithm for dual resource constrained flexible job-shop scheduling problem. International Journal of Production Research, 2016, 54(18): 5554-5566.

[31]Tian MM, Jiang YH, Gao XY, Wang L, Huang DX. Plantwide scheduling model for the typical polyvinyl chloride production by calcium carbide method. Industrial & Engineering Chemistry Research, 2016, 55(21): 6161-6174.

[32]Zheng XL, Wang L. A two-stage adaptive fruit fly optimization algorithm for unrelated parallel machine scheduling problem with additional resource constraints. Expert Systems with Applications, 2016, 65: 28-39.

[33]Wang L, Wang SY, Zheng XL. A hybrid estimation of distribution algorithm for unrelated parallel machine scheduling with sequence-dependent setup times. IEEE/CAA Journal of Automatica Sinica, 2016, 3(3): 235-246.

[34]Shen JN, Wang L, Zheng HY. A modified teaching-learning-based optimization algorithm for bi-objective re-entrant hybrid flowshop scheduling. International Journal of Production Research, 2016, 54(12): 3622-3639.

[35]Deng J, Wang L, Wang SY, Zheng XL. A competitive memetic algorithm for the distributed two-stage assembly flow-shop scheduling problem. International Journal of Production Research, 2016, 54(12): 3561-3577.

[36]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, 2016, 46(1): 139-149. (Regular Paper).

[37]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.

[38]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.

[39]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.

[40]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.

[41]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.

[42]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.

[43]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.

[44]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).

[45]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.

[46]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).

[47]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.

[48]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.

[49]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.

[50]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).

[51]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.

[52]Fang C, Wang L. An effective shuffled frog-leaping algorithm for resource-constrained project scheduling problem. Computers & Operations Research, 2012, 39(5): 890-901.

[53]Pan QK, Wang L. Effective heuristics for the blocking flowshop scheduling problem with makespan minimization. OMEGA-International J of Management Science, 2012, 40(2): 218-229.

[54]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.

[55]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).

[56]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.

[57]Liu B, Wang L, Liu Y, Wang SY. A unified framework for population-based metaheuristics. Annals of Operations Research, 2011, 186(1): 231-262.

[58]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.

[59]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.

[60]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.

[61]Wang L, Li LP. An effective differential evolution with level comparison for constrained engineering design. Structural and Multidisciplinary Optimization, 2010, 41(6): 947-963.

[62]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.

[63]Wang L, Huang FZ. Parameter analysis based on stochastic model for differential evolution algorithm. Applied Mathematics and Computation, 2010, 217(7): 3263-3273.

[64]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.

[65]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.

[66]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.

[67]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.

[68]Qian B, Wang L, Huang DX, Wang X. An effective hybrid DE-based algorithm for flow shop scheduling with limited buffers. International J of Production Research, 2009, 47(1): 1-24.

[69]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.

[70]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)

[71]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.

[72]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).

[73]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)

[74]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)

[75]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.

[76]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)

[77]Wang L, Zheng DZ. An effective hybrid heuristic for flow shop scheduling. International J of Advanced Manufacturing Technology, 2003, 21(1): 38-44.

[78]Jiang YH, Wang L, Jin YH. Bottleneck analysis for network flow model. Advances in Engineering Software, 2003, 34(10): 641-651.

[79]Zhou T, Wang L, Sun ZS. Closed-loop model set validation under a stochastic framework. Automatica, 2002, 38(9): 1449-1461.

[80]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)