宋　哲博士生导师

联系电话： 通讯地址： 南京市鼓楼区金银街16号安中大楼，南京大学商学院工商管理系 邮编：210093
电子邮件 ：zsong1(AT)nju.edu.cn 系所 ：工商管理系


个人简介
研究方向
教学方向
教学奖励
科研奖励
科研项目
出版专著
出版教材
发表论文
会议与工作论文
个人简介
宋哲，南京大学商学院教授，美国爱荷华大学（University of Iowa）工业工程博士、博士后。国际知名期刊IEEE Transactions on Sustainable Energy（国际电子电气工程师协会会刊《可持续能源》，影响因子7.65，自引率7.6%）副主编。IEEE Power Engineering Society Letters,Industrial Engineering & Management编委成员。在美国留学期间参与多个被美国知名公司和机构资助的制造, 能源, 医疗等行业的系统, 决策优化项目, 这些公司和机构包括：Iowa Energy Center, John Deere, MidAmerican Energy, IAWIND, UIHC。在大数据分析建模和管理决策优化方向已经发表高影响因子国际期刊论文30多篇（均被SCI、EI索引，被引用2400多次；ESI近8年高被引论文一篇）, 获中国美国发明专利13项。担任十多个国际一流期刊的审稿人, 如 IEEE Trans. Industrial Informatics，European Journal of Operational Research、IEEE Trans. Industrial Electronics、IEEE Trans. Systems, Man, and Cybernetics等；INFORMS，IISE，IEEE协会会员。


研究方向
1.大数据分析与人工智能 （Big Data Analytics & AI）
2.复杂网络建模与仿真（Complex Networks Modeling & Simulation）
3.创新与研发管理（Innovation, R&D Management）
4.智慧能源管理（Smart Energy Management）
5.智能制造（Smart Manufacturing）
6.风能智慧运营管理（Wind Power Smart Operations Management）


教学方向
1. 大数据分析与管理决策优化
2. 创新与创业管理

教学奖励
宋哲, 2020.9, 南京大学魅力导师奖, 南京大学
宋哲, 2016.9, 南京大学杜厦奖教金, 南京大学
徐小林，宋哲, 2016, Operations Management, 江苏省英文精品课程建设项目
宋哲(指导教师)，2017年10月，首届工业大数据创新竞赛，二等奖（排名2/1460）,工业和信息化部
宋哲(指导教师)，2019年9月，第三届工业大数据创新竞赛，三等奖（排名4/1599）,工业和信息化部

科研奖励
Kusiak Andrew,宋哲, 2016.8, 基本科学指标数据库ESI 高被引论文, Web of Science
宋哲, 2016, 国家自然科学基金项目（#**）结题被评估为“优秀”, 国家自然科学基金委员会
宋哲, 2010.12, 科研新星奖, 南京大学商学院


科研项目
宋哲(PI), 2011-1 to 2013-12, 风电预测，并网调度与规划的决策优化模型, 国家自然科学基金, 编号:**, 17.7万元


出版专著
A. Kusiak, Zhe Song, November 5, 2013, DATA-DRIVEN APPROACH TO MODELING SENSORS WHEREIN OPTIMAL TIME DELAYS ARE DETERMINED FOR A FIRST SET OF PREDICTORS AND STORED AS A SECOND SET OF PREDICTORS, United States Patent Office Serial No. US 8,577,822 B2
A. Kusiak and Z. Song, 2009, Optimization in the Energy Industry: Improving Combustion Performance by Online Learning, P. Pardalos eds., Springer, ISBN: 978-3-540-88964-9.

出版教材
1.Jeffrey Camm et al.;耿修林，宋哲 译, 2017年3月, 商业数据分析（Essentials of Business Analytics）, 机械工业出版社 数据科学、商务数量解析、商务智能系列教材
发表论文
1.蔡霞，宋哲等, 2020, 稠密网络中的竞争创新扩散机制研究-以双寡头同时竞争扩散市场为例, 科学学与科学技术管理, CSSCI，国家自然科学基金委管理科学部重要期刊
2.X. Liu, Z. Zhang and Z. Song,2020, Acomparative study of the data-driven day-ahead hourly provincial load forecastingmethods: From classical data mining to deep learning, Renewable and SustainableEnergy Reviews, Vol. 119, 109632. SCI 一区, IF 10.6
3.蔡霞，宋哲等, 2019, 动态稠密人际网络中的创新扩散研究—基于多智能体仿真的分析, 《科技进步与对策》, CSSCI
4.蔡霞，宋哲等, 2019, 社会网络结构和采纳者创新性对创新扩散的影响—以小世界网络为例, 《软科学》, CSSCI
5.J. Zhu, Y. Shen, Z. Song, D. Zhou, Z.Zhang, and A. Kusiak, Data-Driven Building Load Profiling and Energy Management,Sustainable Cities and Society, Vol. 49, 2019, pp. 1-15.
6.Z. Song, Z. Zhang, Y. Jiang and J. Zhu,2018, Wind turbine health state monitoring based on a Bayesian data-drivenapproach, Renewable Energy, Vol. 125, pp.172-181.
7.Y. Jiang, H. Long, Z. Zhang and Zhe Song,2017, Day-ahead Prediction of Bi-hourly Solar Radiance with a Markov SwitchApproach, IEEE Transactions on Sustainable Energy, Vol. 8, No. 4, pp.1536-1547,SCI一区.
8.L. Huan, Z. Zhang, Zhe Song, A. Kusiak,2017, Formulation and Analysis of Grid and Coordinate Models for Planning WindFarm Layouts, IEEE Access, SCI,Vol. 5, pp.1810-1819
9.蔡霞，宋哲等, 2016, 社会网络环境下的创新扩散研究述评与展望, 科学学与科学技术管理, CSSCI，国家自然科学基金委管理科学部重要期刊
10.蔡霞，宋哲等, 2017, 先发企业的崛起和后进企业的逆袭, 南开管理评论, CSSCI，国家自然科学基金委管理科学A类重要期刊（在管理学学科类目期刊中荣获复合类、期刊综合类和人文社科影响因子三项指标第一，影响力指数第二）
11.Zhang Z. and Song Zhe, 2016, Mining SCADAData Offers a New Roadmap of Wind Farm Operations and Management, IndustrialEngineering & Management,Vol.5, No.2 邀请稿（社评）
12.Zhe Song, Z. Zhang and X. Chen,2016, Thedecision model of 3-dimensional wind farm layout design, Renewable Energy, Vol.85 SCI，二区
13.蔡霞，宋哲等, 2016, 基于自我保护动机的内隐建言信念对员工沉默的影响-一项中国情景的研究, 科学学与科学技术管理, CSSCI, 国家自然科学基金委管理科学部重要期刊
14.Z. Zhang, Zhe Song and J. Xu,2015, Data-DrivenWind Turbine Power Generation Performance Monitoring, IEEE Transactions onIndustrial Electronics,Vol. 62, No. 10 SCI, 一区, Impact factor, 6.498
15.Z Song, Z. Zhang, X.L. Xu, C. Liu, 2015, Anagent-based model to study the market dynamics of perpetual and subscriptionlicensing, Journal of the Operational Research Society,66:845-857SSCI/SCI, 三区
16.Z Song, Y Jiang, Z Zhang, 2014,Short-termwind speed forecasting with Markov-switching model, Applied Energy,130SCI, 一区
17.Yu Jiang, Zhe Song, Andrew Kusiak, 2013,Very short-term wind speed forecasting with Bayesian structural break model,Renewable Energy,Vol. 50, Pp. 637-647 SCI，二区
18.Z. Zhang, Andrew Kusiak, Zhe Song, 2013,Scheduling electric power production at a wind farm, European Journal ofOperational Research,Vol.224,pp. 227-238 SCI，二区
19.C. Xu, Z. Song, L.D. Chen and Y. Zheng,2011, Numerical investigation on porous media heat transfer in a solar towerreceiver, Renewable Energy, Vol. 36, No. 3, pp.Vol. 36, No. 3, pp.1138-1144 SCI，二区
20.Z. Song, X. Geng, A. Kusiak, and C. Xu,2011, Mining Markov Chain Transition Matrix from Wind Speed Time Series Data,Expert Systems with Applications, Vol. 38, No. 8, pp.Vol. 38, No.8, pp. 10229-10239 SCI, 二区
21.A. Kusiak, W. Li and Z. Song,2010, DynamicControl of Wind Turbines, Renewable Energy,Vol.35, No.2, pp.456-463 SCI, 二区
22.Z. Song and A. Kusiak, 2010, MiningPareto-Optimal Modules for Delayed Differentiation, European Journal of OperationalResearch,Vol.201, No. 1, pp.123-128. SCI, 二区
23.Z. Song and A. Kusiak, 2010, Multi-objectiveOptimization of Temporal Processes, IEEE Trans. Systems, Man, and Cybernetics,Part B,Vol.40, No.3, pp.845-856. SCI, 一区
24.A. Kusiak and Z. Song,2010,Design ofWind Farm Layout for Maximum Wind Energy Capture, Renewable Energy,Vol.35, No. 3, pp. 685-694. SCI, 二区
25.A. Kusiak, H. Zheng and Z. Song,2010, Poweroptimization of wind turbines with data mining and evolutionary computation,Renewable Energy,Vol. 35, No. 3, pp. 695-702. SCI, 二区
26.程德俊，宋哲，王蓓蓓, 2010, 认知信任还是情感信任：高参与工作系统对组织创新绩效的影响, 《经济管理》,11期，pp81-90
27.Z. Song and A. Kusiak, 2009, OptimizingProduct Configurations with a Data Mining Approach, International Journal ofProduction Research,Vol. 47, No. 7, pp. 1733-1751. SCI, 三区
28.A. Kusiak, H. Zheng and Z. Song, 2009, WindFarm Power Prediction: A Data-Mining Approach, Wind Energy,Vol. 12, No.3, pp. 275-293 SCI, 二区
29.A. Kusiak, H. Zheng and Z. Song, 2009,On-Line Monitoring of Power Curves, Renewable Energy,Vol. 34, No. 6, pp.1487-1493. SCI, 二区
30.A. Kusiak, H. Zheng and Z. Song, 2009, Modelsfor Monitoring Wind Farm Power, Renewable Energy,Vol. 34, No. 3, pp.583-590. SCI, 二区
31.A. Kusiak, H. Zheng and Z. Song, 2009,Short-Term Prediction of Wind Farm Power: A Data Mining Approach, IEEETransactions on Energy Conversion,Vol. 24, No. 1, pp. 125-136. SCI, 二区
32.A. Kusiak and Z. Song, 2009, Sensor FaultDetection in Power Plants, ASCE Journal of Energy Engineering, Vol.135, No.4,pp.127-137SCI, 三区
33.A. Kusiak, Z. Song and H. Zheng, 2009,Anticipatory Control of Wind Turbines with Data-Driven Predictive Models, IEEETransactions on Energy Conversion,Vol. 24, No. 3, pp. 766-774. SCI, 二区
34.Z. Song and A. Kusiak, 2009, Optimizationof Temporal Processes: A Model Predictive Control Approach, IEEE Transactionson Evolutionary Computation,Vol. 13, No. 1, pp. 169-179. SCI, 一区
35.A. Kusiak and Z. Song, 2008, Clustering-BasedPerformance Optimization of Boiler-Turbine System, IEEE Transactions on EnergyConversion,Vol.23, No. 2, pp. 651-658 SCI, 二区
36.A. Kusiak, M.R. Smith and Z. Song, 2007,Planning Product Configurations Based on Sales Data, IEEE Transactions onSystems, Man and Cybernetics, Part C,Vol. 37, No. 4, pp. 602-609. SCI, 二区
37.Z. Song and A. Kusiak, 2007, Constraint-BasedControl of Boiler Efficiency:A Data-Mining Approach, IEEE Transactions onIndustrial Informatics,Vol. 3, No. 1, pp. 73-83.SCI, 一区
38.A. Kusiak and Z. Song, 2006, CombustionEfficiency Optimization and Virtual Testing: A Data-Mining Approach, IEEETransactions on Industrial Informatics,Vol. 2, No. 3, pp. 176-184. SCI, 一区

会议与工作论文