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郭永江
发布时间:2011-03-22

  数学系 教授 郭永江

 
姓名 郭永江 
职称 教  授
所在部门 数学系
职务  
办公室 主楼1019
电话 010-62281800
电子邮件 yongerguo@bupt.edu.cn

郭永江/ Yongjiang Guo

1975年生,博士,教授 (Born in 1975, Ph.D, Professor)

200606,中国科学院数学与系统科学研究院获运筹学与控制论专业博士(I received my Ph.D in Institute of Applied Mathematics, AMSS, CAS in June 2006)

200607--200812,北京邮电大学理学院讲师 (I was an Assistant Professor in School of Science at Bejing University of Posts and Telecommunications (BUPT) from July 2006 to December 2008)

200812--201612,副教授 (I was an Associate Professor in School of Science at BUPT from December 2008 to December 2016)

201612至今,教授 (I am a Professor in School of Science at BUPT from December 2016)

访学经历 (Visiting experience)

20090701--801,加拿大Dalhousie大学工业工程系(Industrial and Systems Engineering Department at Dalhousie University)

20101201--15,新加坡国立大学商学院 (Business School at National University of Singapore)

20130126--20140126,美国北卡州立大学工业工程系 ( Industrial and Systems Engineering Department at NC State University)

20160128--302, 香港中文大学商学院 (Business School at Chinese University of Hong Kong)

科研兴趣 (Research interests):

  1. 应用概率模型(Applied probability model)
  2. 随机排队网络的渐进分析 (Asymptotic analysis for stochastic queueing model )
  3. 供应链管理模型分析 (Supply chain management analysis)

主要学术成果 (Main academic achievements):

  1. 对随机排队网络的重对数律和泛函重对数律建立了一套完整的强逼近分析方法 (We developed a strong approximation method for the functional law of iterated logarithm (FLIL) and the law of iterated logarithm (LIL) for stochastic queueing networks, see our papers [12, 15, 16, 17, 18, 19, 20] below)
  2. 证明了具有无限到达供应源的随机排队网络的稳定性 (We obtained the stability condition for a class of stochastic queueing model with infinite supply, see our paper [14] below)

主持项目(Research grants as project director):

(1)数学天元基金:几类排队网络的逼近和稳定性(10628047),200901--200912,项目主持人;

(2)青年基金:随机分析与计算机通信网络中的排队模型(10901023),201001--201212,项目主持人;

(3)面上项目:随机排队网络的强逼近及其相关渐近分析(11471053),201501--201812,项目主持人;

(4)北京邮电大学青年专项:因特网中的数据包传输与相应的排队网络模型分析,201104-201212,项目主持人;

参与项目(Research grants as principal investigator):

(1)面上项目:不确定条件下供应链系统的随机比较(70971001), 201001--201212,第三参与人;

(2)青年基金:随机排队网络在优化策略下的行为分析(11101050), 201101--201412,第一参与人。

(3)北京邮电大学青年专项:传输多类数据包的通讯网络资源优化配置及行为分析,201001--201112,第一参与人。

学生培养 (Master and Ph.D):

2010年至今,培养硕士生10人

2016年至今,培养博士生1人

学术兼职(academic activity):

2004年至今,中国运筹学会会员

2015年至今,中国中国运筹学会不确定系统分会副理事长

2015年至今,国际电子商务联合会中国分会常务理事

2012年至今,中国运筹学会随机服务与运作管理分会理事

2015年至今,中国运筹学会智能计算分会理事

主讲课程(Teaching):

线性代数;高等数学(上);概率论与随机过程(本科生);概率论与数理统计;概率论与随机过程(研究生),运筹学随机模型;随机排队网络。

部分论文 (Selected papers):

1. Guo Y. and Zhang H. Optimal production policy in a stochastic manufacturing system. Stochastic Processes, Optimization, and Control Theory Applications, Springer, Houmin Yan, George Yin and Qing Zhang (eds), 141-158, 2006.

2. Guo Y. and Zhang H.  On the stability of a simple re-entrant line with infinite supply. OR Transactions,Vol.10 (2): 75-85, 2006.

3. Guo Y. Yang Y. and Yu J.  Rate of convergence of fluid approximation for a multi-class single-server queue. Acta Mathematicae Applicatae Sinica, (Chinese Series)Vol.29, 6, 1125-1138, 2006.

4. Guo Y. Convergence rate of fluid approximation for Jackson network with multi-server in heavy traffic. Journal of Systems Science and mathematical Sciences, Vol.28,9,1118-1133, 2008.

5. Guo Y., Yang J. and Wang X.  Stability of a 2-station-5-class re-entrant line with infinite supply of work. Asia-pacific journal of operational research, Vol. 25, No. 4, 477-493, 2008. (SCI)

6. Guo Y. Stability of generalized Jackson networks with infinite supply of work. Journal of systems science and complexity, Vol.21, 283-295, 2008. (SCI)

7. Guo Y. Fluid model criterion for instability of re-entrant line with infinite supply of work. TOP, Vol.17(2): 305-319, 2009. (SCI)

8. Guo Y. Diffusion Approximation for re-entrant lines under non-preemptive FBFS service discipline. Acta Mathematicae Applicatae Sinica, (Chinese Series)Vol.32, 6, 1008-1026, 2009.

9. Guo Y. Fluid approximation and its convergence rate for GI/G/1 queue with vacations. Acta Mathematicae Applicatae Sinica, English Series, Vol. 27(1): 43-58, 2011. (SCI)

10. Guo Y. Rate of convergence of fluid approximation for re-entrant lines under FBFS discipline. Asia-Pacific Journal of Operational Research, Vol. 28(3): 401-417, 2011. (SCI)

11. Guo Y. Fluid approximation for a multiclass queue under non-preemptive SBP service discipline. Acta Mathematica Sinica, English Series, Vol.28(2): 379-404, 2012. (SCI)

12. Guo Y. and Huang J. Functional law of iterated logarithm for single server queue with Bernoulli feedback. Acta Mathematicae Applicatae Sinica, (Chinese Series), 35(4) 586-594 2012.

13. Guo Y. Fluid approximation for generalized Jackson network with vacations. Frontiers of Mathematics in China, 7(3), 459-485, 2012. (SCI)

14. Guo Y., Lefeber E., Nazarathy Y., Weiss, G. and Zhang H. Stability and performance for multi-class queueing networks with infinite virtual queues. Queueing Systems, 76(3): 309–342, 2014. (SCI)

15. Guo,Y. and Liu,Y. A law of iterated logarithm for multiclass queues with preemptive priority service discipline. Queueing Systems,79(3): 251-291, 2015. (SCI)

16. Guo, Y. Asymptotic Variability Analysis for Multi-Server Generalized Jackson Network in Overloaded. Acta Mathematicae Applicatae Sinica, English Series, 32(3), 713–730, 2016. (SCI)

17. Guo, Y. and Li, Z. Asymptotic variability analysis for a two-stage tandem queue, part I: The functional law of the iterated logarithm. Journal of Mathematical Analysis and Applications, 450 (2), 1479-1509, 15 June 2017. (SCI)

18. Guo, Y. and Li, Z. Asymptotic variability analysis for a two-stage tandem queue, part II: The law of the iterated logarithm. Journal of Mathematical Analysis and Applications, 450 (2), 1510-1534, 15 June 2017. (SCI)

19. Guo, Y. and Hou, X. Strong approximation method and the (functional) law of iterated logarithm for GI/G/1 queue. Forcoming at Journal of Systems Science and Complexity, 2017. (SCI)

20. Guo, Y, Liu, Y. and Pei, R. Functional law of iterated logarithm for multi-server queues with batch arrivals and customer feedback. Forcoming at Annals of Operations Research, 2017.