Rakesh Kumar

Rakesh Kumar's picture

Staff Details

Telephone Number: 
+264 61 207 2977
Office Location: 
Room 2.211, Second Floor, FHAS Building
Ph.D. Stats., M.Sc. Stats. (Kurukshetra University, India), B. Sc. Non-Medical (H. P. University. Shimla, India), B. Ed. (University of Kashmir, India)


Godlove Suilla Kuaban, Rakesh Kumar, Bhavneet Singh Soodan and Piotr Czekalski, A multi-server queuing model with balking and correlated reneging with application in health care management. IEEE Access, 8, (2020) 169623-169639. [SCIE, I.F.=3.367].


Bhavneet Singh Soodan, and Rakesh Kumar, A single server queuing system with correlated reneging and feedback of served customers. Communications in Statistics: Theory and Methods. (2020) https://doi.org/10.1080/03610926.2020.1861300 [SCIE I.F. = 0.893].


Rakesh Kumar, Sapana Sharma and S.I. Ammar, Transient and steady-state analysis of a queuing system having customers’ impatience with threshold, RAIRO Operations Research, Vol. 53, No. 5 (2019), 1861-1876. [SCIE, I.F.= 1.393].

Rakesh Kumar and Sapana Sharma, Transient analysis of a Markovian queuing model with multiple-heterogeneous servers’ and customers’ impatience, Opsearch, Vol. 58, 540-556 (2021), https://doi.org/10.1007/s12597-020-00495-0 Pages 1-17. [SCOPUS, ESCI].


Rakesh Kumar, Bhavneet Singh Soodan and Sapana Sharma, Modelling health care queue management system facing patients’ impatience using queuing theory, Reliability: Theory and Applications (2021), Vol. 16, No. 1, 161-165. [SCOPUS].


Research Interests

Stochastic Modeling of Queuing Systems and their applications in Health sector, Banking Sector, Computer-communication, Supply chain management, online shopping, Order-picking systems, and Blockchain systems.


Proposed Research Projects:

  1. Study of non-Markovian queuing systems with customers’ impatience and retention
  2. Transient numerical solutions of queuing systems with customers’ impatience and server breakdowns
  3. Performance analysis of blockchain systems using queuing theory
  4. Performance analysis of cloud computing systems using queuing models
  5. Healthcare capacity planning using queuing theory
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