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Mukesh Kumar

Not to be confused with: Mukesh Kumar

Personal Details

First Name:Mukesh
Middle Name:
Last Name:Kumar
Suffix:
RePEc Short-ID:pku139
https://www.researchgate.net/profile/Prof_Dr_Mukesh_Kumar
+6017-6495037

Affiliation

Escuela de Postgrado de Negocios (CENTRUM)
Pontificia Universidad Católica del Perú

Lima, Peru
http://www.centrum.pucp.edu.pe/
RePEc:edi:cepucpe (more details at EDIRC)

Research output

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Jump to: Articles

Articles

  1. Charles, Vincent & Kumar, Mukesh & Irene Kavitha, S., 2012. "Measuring the efficiency of assembled printed circuit boards with undesirable outputs using data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 136(1), pages 194-206.
  2. Udhayakumar, A. & Charles, V. & Kumar, Mukesh, 2011. "Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems," Omega, Elsevier, vol. 39(4), pages 387-397, August.
  3. Mukesh Kumar & Charles Vincent, 2010. "Benchmarking Indian banks using DEA in post-reform period: a progressive time-weighted mean approach," The Service Industries Journal, Taylor & Francis Journals, vol. 31(14), pages 2455-2485, June.
  4. Mukesh Kumar & Trupti Mishra, 2002. "Sources of Technical Efficiency in Indian Small-Scale Industry: Frontier Production Function Techniques," The IUP Journal of Applied Economics, IUP Publications, vol. 0(1), pages 88-98, November.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Charles, Vincent & Kumar, Mukesh & Irene Kavitha, S., 2012. "Measuring the efficiency of assembled printed circuit boards with undesirable outputs using data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 136(1), pages 194-206.

    Cited by:

    1. Holden, R. & Xu, B. & Greening, P. & Piecyk, M. & Dadhich, P., 2016. "Towards a common measure of greenhouse gas related logistics activity using data envelopment analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 105-119.
    2. Kao, Chiang & Hwang, Shiuh-Nan, 2023. "Separating the effect of undesirable outputs generation from the inefficiency of desirable outputs production in efficiency measurement," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1097-1102.
    3. Patricija Bajec & Danijela Tuljak-Suban, 2019. "An Integrated Analytic Hierarchy Process—Slack Based Measure-Data Envelopment Analysis Model for Evaluating the Efficiency of Logistics Service Providers Considering Undesirable Performance Criteria," Sustainability, MDPI, vol. 11(8), pages 1-18, April.
    4. Pérez, Karen & González-Araya, Marcela C. & Iriarte, Alfredo, 2017. "Energy and GHG emission efficiency in the Chilean manufacturing industry: Sectoral and regional analysis by DEA and Malmquist indexes," Energy Economics, Elsevier, vol. 66(C), pages 290-302.
    5. Congcong Yang & Alfred Taudes & Guozhi Dong, 2017. "Efficiency analysis of European Freight Villages: three peers for benchmarking," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(1), pages 91-122, March.
    6. Chia-Nan Wang & Han-Khanh Nguyen, 2017. "Enhancing Urban Development Quality Based on the Results of Appraising Efficient Performance of Investors—A Case Study in Vietnam," Sustainability, MDPI, vol. 9(8), pages 1-22, August.
    7. Charles, Vincent & Aparicio, Juan & Zhu, Joe, 2019. "The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 279(3), pages 929-940.

  2. Udhayakumar, A. & Charles, V. & Kumar, Mukesh, 2011. "Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems," Omega, Elsevier, vol. 39(4), pages 387-397, August.

    Cited by:

    1. Zhengping Liu & Wang Zhang & Hongxian Liu & Guohe Huang & Jiliang Zhen & Xin Qi, 2019. "Characterization of Renewable Energy Utilization Mode for Air-Environmental Quality Improvement through an Inexact Factorial Optimization Approach," Sustainability, MDPI, vol. 11(8), pages 1-19, April.
    2. Chen, Kun & Zhu, Joe, 2019. "Computational tractability of chance constrained data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1037-1046.
    3. Ballings, Michel & Van den Poel, Dirk & Bogaert, Matthias, 2016. "Social media optimization: Identifying an optimal strategy for increasing network size on Facebook," Omega, Elsevier, vol. 59(PA), pages 15-25.
    4. Wanke, Peter & Barros, C.P., 2017. "Efficiency thresholds and cost structure in Senegal airports," Journal of Air Transport Management, Elsevier, vol. 58(C), pages 100-112.
    5. Simsek, Serhat & Dag, Ali & Tiahrt, Thomas & Oztekin, Asil, 2021. "A Bayesian Belief Network-based probabilistic mechanism to determine patient no-show risk categories," Omega, Elsevier, vol. 100(C).
    6. Kao, Chiang & Liu, Shiang-Tai, 2019. "Stochastic efficiency measures for production units with correlated data," European Journal of Operational Research, Elsevier, vol. 273(1), pages 278-287.
    7. Chen, Zhongfei & Matousek, Roman & Wanke, Peter, 2018. "Chinese bank efficiency during the global financial crisis: A combined approach using satisficing DEA and Support Vector Machines☆," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 71-86.
    8. Lin, Yi-Kuei & Yeh, Cheng-Ta, 2012. "Determining the optimal double-component assignment for a stochastic computer network," Omega, Elsevier, vol. 40(1), pages 120-130, January.
    9. Wei, Guiwu & Chen, Jian & Wang, Jiamin, 2014. "Stochastic efficiency analysis with a reliability consideration," Omega, Elsevier, vol. 48(C), pages 1-9.
    10. Rashed Khanjani Shiraz & Madjid Tavana & Hirofumi Fukuyama, 2021. "A joint chance-constrained data envelopment analysis model with random output data," Operational Research, Springer, vol. 21(2), pages 1255-1277, June.
    11. Sungmin Park & Pansoo Kim, 2021. "Operational Performance Evaluation of Korean Ship Parts Manufacturing Industry Using Dynamic Network SBM Model," Sustainability, MDPI, vol. 13(23), pages 1-20, November.
    12. Percy Marquina & Vincent Charles, 2021. "A Bayesian resampling approach to estimate the difference in effect sizes in consumer social responses to CSR initiatives versus corporate abilities," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 28(6), pages 1680-1699, November.
    13. Reza Sanei & Farhad Hosseinzadeh lotfi & Mohammad Fallah & Farzad Movahedi Sobhani, 2022. "An Estimation of an Acceptable Efficiency Frontier Having an Optimum Resource Management Approach, with a Combination of the DEA-ANN-GA Technique (A Case Study of Branches of an Insurance Company)," Mathematics, MDPI, vol. 10(23), pages 1-21, November.
    14. Vincent Charles & Ioannis E. Tsolas & Tatiana Gherman, 2018. "Satisficing data envelopment analysis: a Bayesian approach for peer mining in the banking sector," Annals of Operations Research, Springer, vol. 269(1), pages 81-102, October.
    15. Wanke, Peter & Araujo, Claudia & Tan, Yong & Antunes, Jorge & Pimenta, Roberto, 2023. "Efficiency in university hospitals: A genetic optimized semi-parametric production function," Operations Research Perspectives, Elsevier, vol. 10(C).
    16. Wang, S. & Huang, G.H., 2014. "An integrated approach for water resources decision making under interactive and compound uncertainties," Omega, Elsevier, vol. 44(C), pages 32-40.
    17. Sun, Qinghe & Chen, Li & Meng, Qiang, 2022. "Evaluating port efficiency dynamics: A risk-based approach," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 333-347.
    18. Zhao, Ze & Wang, Jianzhou & Zhao, Jing & Su, Zhongyue, 2012. "Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China," Omega, Elsevier, vol. 40(5), pages 525-532.
    19. Rashed Khanjani Shiraz & Adel Hatami-Marbini & Ali Emrouznejad & Hirofumi Fukuyama, 2020. "Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs," Operational Research, Springer, vol. 20(3), pages 1863-1898, September.
    20. Ali Ebrahimnejad & Madjid Tavana & Seyed Hadi Nasseri & Omid Gholami, 2019. "A New Method for Solving Dual DEA Problems with Fuzzy Stochastic Data," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 147-170, January.

  3. Mukesh Kumar & Charles Vincent, 2010. "Benchmarking Indian banks using DEA in post-reform period: a progressive time-weighted mean approach," The Service Industries Journal, Taylor & Francis Journals, vol. 31(14), pages 2455-2485, June.

    Cited by:

    1. Ravi Kumar Jain & Ramachandran Natarajan & Amlan Ghosh, 2016. "Decision Tree Analysis for Selection of Factors in DEA: An Application to Banks in India," Global Business Review, International Management Institute, vol. 17(5), pages 1162-1178, October.
    2. Udhayakumar, A. & Charles, V. & Kumar, Mukesh, 2011. "Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems," Omega, Elsevier, vol. 39(4), pages 387-397, August.
    3. Chia-Jung Tu & Ming-Chung Chang & Chiang-Ping Chen, 2016. "Progressive Time-Weighted Dynamic Energy Efficiency, Energy Decoupling Rate, and Decarbonization: An Empirical Study on G7 and BRICS," Sustainability, MDPI, vol. 8(9), pages 1-17, September.
    4. Kashif Rashid & Adeela Rustam, 2014. "Comparative Analysis of Local and Foreign Banks Efficiency: A Case Study of Pakistan," Oeconomics of Knowledge, Saphira Publishing House, vol. 6(3), pages 7-52, August.
    5. Vincent Charles & Ioannis E. Tsolas & Tatiana Gherman, 2018. "Satisficing data envelopment analysis: a Bayesian approach for peer mining in the banking sector," Annals of Operations Research, Springer, vol. 269(1), pages 81-102, October.
    6. Muhammad Imran Qureshi & Adeela Rustam & Sehrish Rustam & Abdullah Bin Umar & Khalid Zaman, 2012. "Measuring Bank Branch Performance in Pakistan: Data Envelopment Analysis (DEA)," Oeconomics of Knowledge, Saphira Publishing House, vol. 4(4), pages 25-40, October.

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