Dr. Ravi Kumar

Personal Information

 Name: Dr. Ravi Kumar
 Designation: Assistant Professor (Management)
 Contact Number: 01881-242273
 Email:ravi.kumar@iitrpr.ac.in
 Address: 333, Transit Campus - I, IIT Ropar, Punjab

Areas Of Research

  • Production Management
  • Operations Research
  • Business Analytics
  • Supply Chain Management



Short Biography

Dr Ravi Kumar is an M.Sc. in Applied Operations Research from the University of Delhi, and a Ph.D. in Operations Research from IIT Delhi. He has published his work in various scholarly peer-reviewed international journals. His research interests include the development of mathematical models and their solution methodology. He is proficient in various optimization software like PYTHON, MATLAB, LINGO, GLPK, and SPSS. He is a professional body member of the GLOGIFT society. His expertise is to develop mathematical models and their solution methodologies. He is also an expert in the implementation of multi-criteria decision-making techniques.

Education

Higher Education

  • Ph.D. (Operations Research & Supply Chain Management)

Department of Management Studies, IIT Delhi.


  • M.Sc. (Applied Operational Research)

Department of Operational Research, DU, South Campus, Delhi.



Work Experience

Research

In Journals

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  • Kumar, R., & Singh, S. P. (2017). A similarity score-based two-phase heuristic approach to solve the dynamic cellular facility layout for manufacturing systems. Engineering Optimization, 49(11), 1848-1867. https://doi.org/10.1080/0305215X.2016.1274205
  • Kumar, R., & Singh, S. P. (2017). Simulated Annealing-Based Embedded Meta-Heuristic Approach to Solve Bi-objective Robust Stochastic Sustainable Cellular Layout. Global Journal of Flexible Systems Management, 1-25. https://doi.org/10.1007/s40171-017-0174-4
  • Kumar, R., & Singh, S. P. (2017). Designing robust stochastic bi-objective cellular layout in manufacturing systems. International Journal of Management Concepts and Philosophy, 10(2), 147-164. https://doi.org/10.1504/IJMCP.2017.084045
  • Kumar, R., & Singh, S. P. (2017). Optimal selection of multi-criteria unequal area facility layout problem: an integer linear program and Borda-Kendall-based method. International Journal of Business and Systems Research, 11(1-2), 62-81. doi: 10.1504/IJBSR.2017.080835
  • Kumar, R., Singh, S. P., & Lamba, K. (2018). Sustainable robust layout using Big Data approach: A key towards industry 4.0. Journal of Cleaner Production, 204, 643-659. https://doi.org/10.1016/j.jclepro.2018.08.327
  • Kumar, R. and Singh, S.P. (2019), "Cellular facility layout problem: a case of tower manufacturing industry", Management of Environmental Quality, Vol. 30 No. 6, pp. 1345-1360. https://doi.org/10.1108/MEQ-04-2018-0076
  • Lamba, K., Kumar, R., Mishra, S. et al. (2019). Sustainable dynamic cellular facility layout: a solution approach using simulated annealing-based meta-heuristic. Annals of Operations  Research https://doi.org/10.1007/s10479-019-03340-w
  • Kumar, R., Lamba, K., and Raman, A. (2021) Role of zero emission vehicles in the sustainable transformation of the Indian automobile industry. (Research in Transportation Economics)
  • Patyal, V. S., Kumar, R., & Kushwah, S. (2021). Modeling Electric Vehicle Adoption Barriers: An Interpretive structural modeling approach. Energy. 


In Conference Proceedings

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  • Kumar, R., & Singh, S. P. (2015). AHP-IRP: An integrated approach for decision making. In International conference on evidence based management 2015 (pp. 605-612). ISBN: 978- 93-84935-18-4



Book Chapters

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  • Kumar, R., & Singh, S. P. (2016). Designing Flexible Bi-objective Sustainable Cellular Layout in Manufacturing Systems. Proceedings of GLOGIFT 16, (pp. 114-120) ISBN 978-93-83893-00-3. (Springer)
  • Kumar, R., & Prakash Singh, S. (2017). Modified-SA Algorithm for Bi-objective Robust Stochastic Cellular Facility Layout in Cellular Manufacturing Systems. Advanced Computing and Communication Technologies, 19-33 ICACCT 2018. (Springer).

CV

Courses

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Other Information

Teaching

  • Operations Management 
  • Supply Chain Management
  • Management Science
  • Operations & Maintenance Practices
  • Business Analytics and Artificial Intelligence using Python
  • Advanced Decision Analysis
  • Forecasting tools & techniques