王凌 教授

过程控制工程研究所

通信地址:北京清华大学自动化系  邮政编码:100084
联系电话:010-62783125-272  Fax: 010-62786911
Email: wangling@tsinghua.edu.cn

教育背景

1990年9月至1995年7月 在清华大学自动化系过程控制专业学习,获学士学位

1995年9月至1999年10月 在清华大学自动化系控制理论与控制工程专业学习,获博士学位

工作履历

1999年10月至2002年11月 清华大学自动化系过程控制工程研究所    讲师

2002年12月至2008年11月 清华大学自动化系过程控制工程研究所    副教授

2007年1月至2008年1月 美国密西根大学工业与运作工程系    访问学者

2008年12月至今 清华大学自动化系过程控制工程研究所    教授 博士生导师

学术兼职

1.中国自动化学会过程控制专委会委员

2.中国运筹学会排序专委会常务理事

3.中国运筹学会智能工业数据解析与优化专委会常务理事

4.中国人工智能学会智能优化专委会常务理事

5.北京市自动化学会常务理事

6.The Open Operational Research Journal协同主编

7.International J of Automation and Control编委

8.International J of Artificial Intelligence and Soft Computing编委

9.Memetic Computing编委

10.Swarm and Evolutionary Computation编委

研究领域

智能优化理论、方法与应用

复杂生产过程建模、优化与调度

研究概况

[1]国家杰出青年科学基金(61525304):智能优化调度理论与方法。(负责人) (2016.1~2020.12)

[2]国家自然科学基金项目(61174189):复杂资源受限项目调度问题及其混合智能算法研究。(负责人) (2012.1~2015.12)

[3]国家自然科学基金项目(70871065):基于学习机制的群智能调度理论与方法研究。(负责人) (2009.1~2011.12)

[4]国家自然科学基金项目(60774082):复杂生产系统基于差分进化和量子进化的优化调度理论与方法。(负责人) (2008.1~2010.12)

[5]国家自然科学基金项目(60374060):复杂生产系统的智能仿真优化理论与方法研究。(负责人) (2004.1~2006.12)

[6]国家自然科学基金项目(60204008):复杂系统基于计算智能的混合优化理论与方法。(负责人) (2003.1~2005.12)

[7]国家自然科学基金项目(60834004):复杂芯片制造过程实时调度与优化控制理论和算法研究及应用。(骨干) (2009.1~2012.12)

[8]教育部新世纪优秀人才支持计划(NCET-10-0505)。(负责人) (2010.1~2012.12)

[9]高等学校博士学科点专项科研基金(20130002110057):基于协同分布估计算法的分布式车间调度研究。(负责人) (2014.1~2016.12)

[10]高等学校博士学科点专项科研基金(20100002110014):基于新型混合群智能的资源约束项目调度研究。(负责人) (2011.1~2013.12)

[11]北京市科技新星计划(2004A41):混合智能优化调度理论与算法研究。(负责人) (2004.7~2007.7)

[12]教育部留学回国启动基金:基于混合差分进化的优化调度研究。(负责人) (2009.1~2010.12)

[13]973计划课题(2013CB329503):面向脑信息编解码的机器学习方法。(骨干) (2013.01~2017.12)

[14]973计划课题(2009CB320602):复杂生产制造全流程基于数据和知识的实时智能运行优化理论和方法研究。(骨干) (2011.01~2013.08)

[15]973计划课题(2002CB312203):复杂生产制造过程实时、智能控制与优化理论和方法研究。(骨干) (2002.12~2008.5)

[16]863计划项目(2007AA04Z155):流程工业企业生产过程的智能计划与动态优化调度技术。(副组长) (2008.1~2009.12)

[17]国家科技重大专项(2011ZX02504-008):集成电路生产线智能调度与质量优化控制技术研究。(骨干) (2011.1~2013.12)

奖励与荣誉

[1]2015年度国家杰出青年科学基金

[2]2009年度教育部新世纪优秀人才支持计划

[3]2009年度清华大学学术新人奖

[4]2004年度北京市科技新星

[5]2010年度Scopus青年科学之星新人奖

[6]2014年度国家自然科学奖二等奖

[7]2011年度中国电子学会电子信息科学技术奖二等奖

[8]2008年度北京市科学技术奖三等奖

[9]2007年度高等学校科学技术奖自然科学奖二等奖

[10]2003年度教育部提名国家自然科学一等奖

[11]2014年度自动化学报优秀论文奖

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

[13]国际和声搜索算法会议最佳论文奖, ICHSA’2015

[14]中国过程控制年会Poster论文奖, CPCC’2014

[15]高等计算智能与智能信息国际会议最佳论文奖, IWACIII’2013

[16]IEEE国际智能计算会议最佳论文奖, ICIC’2011

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

[18]IET咨询与控制技术国际会议优秀论文, ICT’2006

[19]中国控制与决策年会优秀论文, CCDC’2004

[20]IEEE机器学习和控制论国际会议优秀论文奖, ICMLC’2002

[21]清华大学优秀博士论文一等奖

[22]清华大学优秀教材二等奖 (2004, 2008, 2012)

[23]清华大学第14届良师益友 (2014)

[24]清华大学优秀班主任一等奖 (2004, 2005)

学术成果

主要论著:
[1] 王凌, 钱斌. 混合差分进化与调度算法. 北京: 清华大学出版社, 2012.

[2] 王凌, 刘波. 微粒群优化与调度算法. 北京: 清华大学出版社, 2008.

[3] 王京春, 王凌, 金以慧 (译). 过程的动态特性与控制. 北京: 电子工业出版社, 2006.

[4] 王凌. 车间调度及其遗传算法. 北京: 清华大学&Springer出版社, 2003.

[5] 王凌. 智能优化算法及其应用. 北京: 清华大学&Springer出版社, 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.


 

主要课程:
[1] 智能优化算法及其应用 (本科生课程)

[2] 自动控制原理 (本科生课程) [北京市精品课程] [国家精品课程]

[3] 生产调度及其智能优化 (研究生课程)

[4] 人工神经网络 (研究生课程)

[5] 文献检索与论文写作 (工程硕士课程)