王凌(清華大學自動化系教授)

王凌，清華大學自動化系教授，過程控制工程研究所教授。

基本介紹

• 中文名：王凌
• 職業：清華大學自動化系教授
• 畢業院校：清華大學
• 主要成就：國家傑出青年科學基金獲得者
  國家自然科學二等獎
• 學位：博士
• 職稱：教授

人物經歷

教育背景

1990年9月至1995年7月 在清華大學自動化系過程控制專業學習，獲學士學位。

1995年9月至1999年10月 在清華大學自動化系控制理論與控制工程專業學習，獲博士學位。

工作履歷

1999年10月至2002年11月 清華大學自動化系過程控制工程研究所 講師。

2002年12月至2008年11月 清華大學自動化系過程控制工程研究所 副教授。

2007年1月至2008年1月 美國密西根大學工業與運作工程系 訪問學者。

2008年12月至今 清華大學自動化系過程控制工程研究所 教授 博士生導師。

主講課程

[1] 智慧型最佳化算法及其套用 (本科生課程)。

[2] 自動控制原理 (本科生課程) [北京市精品課程] [國家精品課程]。

[3] 生產調度及其智慧型最佳化 (研究生課程)。

[4] 人工神經網路 (研究生課程)。

[5] 文獻檢索與論文寫作 (工程碩士課程)。

研究方向

智慧型最佳化理論、方法與套用。

複雜生產過程建模、最佳化與調度。

主要貢獻

學術兼職

[1] 中國仿真學會智慧型最佳化與調度專委會副主任委員

[2] 中國自動化學會控制理論與套用專委會委員

[3] 中國自動化學會過程控制專委會委員

[4] 中國自動化學會能源網際網路專委會常務理事

[5] 中國運籌學會排序專委會常務理事

[6] 中國運籌學會智慧型工業數據解析與最佳化專委會常務理事

[7] 中國人工智慧學會智慧型最佳化專委會常務理事

[8] 北京市自動化學會常務理事

[9] International Journal of Automation and Control主編

[10] IEEE Transactions on Evolutionary Computation副編輯

[11] Swarm and Evolutionary Computation副編輯

[12] Int J of Applied and Computational Mathematics副編輯

[13] Int J of Artificial Intelligence and Soft Computing編委

[14] Journal of Optimization編委

[15] Memetic Computing編委

[16] 《控制理論與套用》編委

[17] 《控制與決策》編委

[18] 《控制工程》編委

[19] 《系統工程與電子技術》編委

研究概況

[1] 國家傑出青年科學基金(61525304)：智慧型最佳化調度理論與方法。(負責人) (2016.1~2020.12)

[2] 國家自然科學基金項目(61873328)：分散式生產調度的協同群智慧型最佳化理論與方法。(負責人) (2019.1~2022.12)

[3] 國家自然科學基金項目(61174189)：複雜資源受限項目調度問題及其混合智慧型算法研究。(負責人) (2012.1~2015.12)

[4] 國家自然科學基金項目(70871065)：基於學習機制的群智慧型調度理論與方法研究。(負責人) (2009.1~2011.12)

[5] 國家自然科學基金項目(60774082)：複雜生產系統基於差分進化和量子進化的最佳化調度理論與方法。(負責人) (2008.1~2010.12)

[6] 國家自然科學基金項目(60374060)：複雜生產系統的智慧型仿真最佳化理論與方法研究。(負責人) (2004.1~2006.12)

[7] 國家自然科學基金項目(60204008)：複雜系統基於計算智慧型的混合最佳化理論與方法。(負責人) (2003.1~2005.12)

[8] 國家自然科學基金重點項目(60834004)：複雜晶片製造過程實時調度與最佳化控制理論和算法研究及套用。(骨幹) (2009.1~2012.12)

[9] 教育部新世紀優秀人才支持計畫(NCET-10-0505)。(負責人) (2010.1~2012.12)

[10] 高等學校博士學科點專項科研基金(20130002110057)：基於協同分布估計算法的分散式車間調度研究。(負責人) (2014.1~2016.12)

[11] 高等學校博士學科點專項科研基金(20100002110014)：基於新型混合群智慧型的資源約束項目調度研究。(負責人) (2011.1~2013.12)

[12] 北京市科技新星計畫(2004A41)：混合智慧型最佳化調度理論與算法研究。(負責人) (2004.7~2007.7)

[13] 教育部留學回國啟動基金：基於混合差分進化的最佳化調度研究。(負責人) (2009.1~2010.12)

[14] 國家重點研發計畫(2016YFB0901900)：能源網際網路的規劃、運行與交易基礎理論。(課題一負責人) (2016.07~2020.06)

[15] 973計畫課題(2013CB329503)：面向腦信息編解碼的機器學習方法。(骨幹) (2013.01~2017.12)

[16] 973計畫課題(2009CB320602)：複雜生產製造全流程基於數據和知識的實時智慧型運行最佳化理論和方法研究。(骨幹) (2011.01~2013.08)

[17] 973計畫課題(2002CB312203)：複雜生產製造過程實時、智慧型控制與最佳化理論和方法研究。(骨幹) (2002.12~2008.5)

[18] 863計畫項目(2007AA04Z155)：流程工業企業生產過程的智慧型計畫與動態最佳化調度技術。(副組長) (2008.1~2009.12)

[19] 國家科技重大專項(2011ZX02504-008)：積體電路生產線智慧型調度與質量最佳化控制技術研究。(骨幹) (2011.1~2013.12)

學術成果

[1] 王凌, 王聖堯, 方晨. 分布估計調度算法. 北京: 清華大學出版社, 2017

[2] 王凌, 錢斌. 混合差分進化與調度算法. 北京: 清華大學出版社, 2012.

[3] 王凌, 劉波. 微粒群最佳化與調度算法. 北京: 清華大學出版社, 2008.

[4] 王京春, 王凌, 金以慧 (譯). 過程的動態特性與控制. 北京: 電子工業出版社, 2006.

[5] 王凌. 車間調度及其遺傳算法. 北京: 清華大學&Springer出版社, 2003.

[6] 王凌. 智慧型最佳化算法及其套用. 北京: 清華大學&Springer出版社, 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 Journal 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 Journal 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 Journal 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.

獎勵與榮譽

[1] 2015年度國家傑出青年科學基金。

[2] 2009年度教育部新世紀優秀人才支持計畫。

[3] 2009年度清華大學學術新人獎。

[4] 2004年度北京市科技新星。

[5] 2010年度Scopus青年科學之星新人獎。

[6] 2014年度國家自然科學獎二等獎。

[7] 2011年度中國電子學會電子信息科學技術獎二等獎。

[8] 2008年度北京市科學技術獎三等獎。

[9] 2007年度高等學校科學技術獎自然科學獎二等獎。

[10] 2003年度教育部提名國家自然科學一等獎。

[11] 2014年度自動化學報優秀論文獎、2016年度控制理論與套用優秀論文獎。

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

[13] 國際和聲搜尋算法會議最佳論文獎, ICHSA’2015。

[14] 中國過程控制年會Poster論文獎, CPCC’2014。

[15] 高等計算智慧型與智慧型信息國際會議最佳論文獎, IWACIII’2013。

[16] IEEE國際智慧型計算會議最佳論文獎, ICIC’2011。

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

[18] IET諮詢與控制技術國際會議優秀論文, ICT’2006。

[19] 中國控制與決策年會優秀論文, CCDC’2004。

[20] IEEE機器學習和控制論國際會議優秀論文獎, ICMLC’2002。

[21] 清華大學優秀博士論文一等獎。

[22] 清華大學優秀教材二等獎 (2004, 2008, 2012)。

[23] 清華大學第14屆良師益友 (2014)。

[24] 清華大學優秀班主任一等獎 (2004, 2005)。