IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v292y2020i1d10.1007_s10479-019-03155-9.html
   My bibliography  Save this article

A second-order cone programming based robust data envelopment analysis model for the new-energy vehicle industry

Author

Listed:
  • Chao Lu

    (Shanghai University)

  • Jie Tao

    (University of Shanghai for Science and Technology)

  • Qiuxian An

    (North China Electric Power University)

  • Xiaodong Lai

    (South China Normal University)

Abstract

The validity of performance evaluation is determined by, and therefore greatly influenced by, the accuracy of data set. To address such imprecise and negative data problems widely spread in the real world, this paper proposes a second-order cone based robust data envelopment analysis (SOCPR-DEA) model, which is more robust to data variety. Further, this new computational tractable model is applied to analyze 13 new-energy vehicle (NEV) manufacturers from China. The findings support that the SOCPR-DEA model could well mitigate the deficiency caused by data variety, and the evidence from Chinese NEV industry shows that a focus strategy is more likely to enhance a firm’s efficiency especially at its emerging stage, and the efficiency is more sensitive with production cost than other factors such as research and development, sales income, earnings per share, and predicted income. In addition, this paper also gives some industrial implications and policy suggestions based on these interesting findings.

Suggested Citation

  • Chao Lu & Jie Tao & Qiuxian An & Xiaodong Lai, 2020. "A second-order cone programming based robust data envelopment analysis model for the new-energy vehicle industry," Annals of Operations Research, Springer, vol. 292(1), pages 321-339, September.
  • Handle: RePEc:spr:annopr:v:292:y:2020:i:1:d:10.1007_s10479-019-03155-9
    DOI: 10.1007/s10479-019-03155-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-019-03155-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-019-03155-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. You, Yan Q. & Jie, Tao, 2016. "A study of the operation efficiency and cost performance indices of power-supply companies in China based on a dynamic network slacks-based measure model," Omega, Elsevier, vol. 60(C), pages 85-97.
    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. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    4. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    5. Lu, Chao & Liu, Hu-Chen & Tao, Jie & Rong, Ke & Hsieh, Ying-Che, 2017. "A key stakeholder-based financial subsidy stimulation for Chinese EV industrialization: A system dynamics simulation," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 1-14.
    6. Liu, Yingqi & Kokko, Ari, 2013. "Who does what in China’s new energy vehicle industry?," Energy Policy, Elsevier, vol. 57(C), pages 21-29.
    7. Yu, Chian-Son & Li, Han-Lin, 2000. "A robust optimization model for stochastic logistic problems," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 385-397, March.
    8. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
    9. R. G. Chambers & Y. Chung & R. Färe, 1998. "Profit, Directional Distance Functions, and Nerlovian Efficiency," Journal of Optimization Theory and Applications, Springer, vol. 98(2), pages 351-364, August.
    10. M P Estellita Lins & L Angulo-Meza & A C Moreira Da Silva, 2004. "A multi-objective approach to determine alternative targets in data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(10), pages 1090-1101, October.
    11. Sadjadi, S.J. & Omrani, H., 2008. "Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies," Energy Policy, Elsevier, vol. 36(11), pages 4247-4254, November.
    12. Aparicio, Juan & Mahlberg, Bernhard & Pastor, Jesus T. & Sahoo, Biresh K., 2014. "Decomposing technical inefficiency using the principle of least action," European Journal of Operational Research, Elsevier, vol. 239(3), pages 776-785.
    13. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    14. Manuel Laguna, 1998. "Applying Robust Optimization to Capacity Expansion of One Location in Telecommunications with Demand Uncertainty," Management Science, INFORMS, vol. 44(11-Part-2), pages 101-110, November.
    15. Emrouznejad, Ali & Yang, Guo-liang, 2018. "A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 4-8.
    16. Amy H. I. Lee & Chun Yu Lin & He-Yau Kang & Wen Hsin Lee, 2012. "An Integrated Performance Evaluation Model for the Photovoltaics Industry," Energies, MDPI, vol. 5(4), pages 1-21, April.
    17. Wei Chen & Yuxi Gai & Pankaj Gupta, 2018. "Efficiency evaluation of fuzzy portfolio in different risk measures via DEA," Annals of Operations Research, Springer, vol. 269(1), pages 103-127, October.
    18. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    19. Tone, Kaoru & Tsutsui, Miki, 2010. "Dynamic DEA: A slacks-based measure approach," Omega, Elsevier, vol. 38(3-4), pages 145-156, June.
    20. Talluri, Srinivas & Narasimhan, Ram & Nair, Anand, 2006. "Vendor performance with supply risk: A chance-constrained DEA approach," International Journal of Production Economics, Elsevier, vol. 100(2), pages 212-222, April.
    21. Vassiadou-Zeniou, Christiana & Zenios, Stavros A., 1996. "Robust optimization models for managing callable bond portfolios," European Journal of Operational Research, Elsevier, vol. 91(2), pages 264-273, June.
    22. John M. Mulvey & Robert J. Vanderbei & Stavros A. Zenios, 1995. "Robust Optimization of Large-Scale Systems," Operations Research, INFORMS, vol. 43(2), pages 264-281, April.
    23. SHOKOUHI, Amir H. & HATAMI-MARBINI, Adel & TAVANA, Madjid & SAATI, Saber, 2010. "A robust optimization approach for imprecise data envelopment analysis," LIDAM Reprints CORE 2215, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    2. Zhang, Tinglong & Li, Sasa & Li, Yifan & Wang, Weizhong, 2023. "Evaluation of technology innovation efficiency for the listed NEV enterprises in China," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 1445-1458.
    3. Emmanuel Kwasi Mensah, 2020. "Robust data envelopment analysis via ellipsoidal uncertainty sets with application to the Italian banking industry," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(2), pages 491-518, December.
    4. Pejman Peykani & Jafar Gheidar-Kheljani & Reza Farzipoor Saen & Emran Mohammadi, 2022. "Generalized robust window data envelopment analysis approach for dynamic performance measurement under uncertain panel data," Operational Research, Springer, vol. 22(5), pages 5529-5567, November.
    5. Hatami-Marbini, Adel & Arabmaldar, Aliasghar, 2021. "Robustness of Farrell cost efficiency measurement under data perturbations: Evidence from a US manufacturing application," European Journal of Operational Research, Elsevier, vol. 295(2), pages 604-620.
    6. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2022. "Innovation efficiency and technology heterogeneity within China's new energy vehicle industry: A two-stage NSBM approach embedded in a three-hierarchy meta-frontier framework," Energy Policy, Elsevier, vol. 161(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hatami-Marbini, Adel & Arabmaldar, Aliasghar, 2021. "Robustness of Farrell cost efficiency measurement under data perturbations: Evidence from a US manufacturing application," European Journal of Operational Research, Elsevier, vol. 295(2), pages 604-620.
    2. Arabmaldar, Aliasghar & Sahoo, Biresh K. & Ghiyasi, Mojtaba, 2023. "A generalized robust data envelopment analysis model based on directional distance function," European Journal of Operational Research, Elsevier, vol. 311(2), pages 617-632.
    3. Adel Hatami-Marbini & Aliasghar Arabmaldar & John Otu Asu, 2022. "Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(4), pages 1213-1254, December.
    4. Emmanuel Kwasi Mensah, 2020. "Robust data envelopment analysis via ellipsoidal uncertainty sets with application to the Italian banking industry," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(2), pages 491-518, December.
    5. Toloo, Mehdi & Mensah, Emmanuel Kwasi & Salahi, Maziar, 2022. "Robust optimization and its duality in data envelopment analysis," Omega, Elsevier, vol. 108(C).
    6. Pejman Peykani & Jafar Gheidar-Kheljani & Reza Farzipoor Saen & Emran Mohammadi, 2022. "Generalized robust window data envelopment analysis approach for dynamic performance measurement under uncertain panel data," Operational Research, Springer, vol. 22(5), pages 5529-5567, November.
    7. Hosseini, Keyvan & Stefaniec, Agnieszka, 2019. "Efficiency assessment of Iran's petroleum refining industry in the presence of unprofitable output: A dynamic two-stage slacks-based measure," Energy, Elsevier, vol. 189(C).
    8. Roya Soltani & Seyed J Sadjadi, 2014. "Reliability optimization through robust redundancy allocation models with choice of component type under fuzziness," Journal of Risk and Reliability, , vol. 228(5), pages 449-459, October.
    9. Omrani, Hashem & Valipour, Mahsa & Emrouznejad, Ali, 2021. "A novel best worst method robust data envelopment analysis: Incorporating decision makers’ preferences in an uncertain environment," Operations Research Perspectives, Elsevier, vol. 8(C).
    10. Antonio G. Martín & Manuel Díaz-Madroñero & Josefa Mula, 2020. "Master production schedule using robust optimization approaches in an automobile second-tier supplier," 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. 28(1), pages 143-166, March.
    11. Fukuyama, Hirofumi & Weber, William L., 2010. "A slacks-based inefficiency measure for a two-stage system with bad outputs," Omega, Elsevier, vol. 38(5), pages 398-409, October.
    12. Kai Xu & Bart Bossink & Qiang Chen, 2019. "Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs," Sustainability, MDPI, vol. 12(1), pages 1-21, December.
    13. Tatiana Bencova & Andrea Bohacikova, 2022. "DEA in Performance Measurement of Two-Stage Processes: Comparative Overview of the Literature," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 111-129.
    14. Suvvari Anandarao & S. Raja Sethu Durai & Phanindra Goyari, 2019. "Efficiency Decomposition in two-stage Data Envelopment Analysis: An application to Life Insurance companies in India," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 271-285, June.
    15. Bram L. Gorissen, 2015. "Robust Fractional Programming," Journal of Optimization Theory and Applications, Springer, vol. 166(2), pages 508-528, August.
    16. Juan Aparicio & Magdalena Kapelko & Juan F. Monge, 2020. "A Well-Defined Composite Indicator: An Application to Corporate Social Responsibility," Journal of Optimization Theory and Applications, Springer, vol. 186(1), pages 299-323, July.
    17. Laura Calzada-Infante & Ana María López-Narbona & Alberto Núñez-Elvira & Javier Orozco-Messana, 2020. "Assessing the Efficiency of Sustainable Cities Using an Empirical Approach," Sustainability, MDPI, vol. 12(7), pages 1-13, March.
    18. Shixiong Cheng & Jiahui Xie & De Xiao & Yun Zhang, 2019. "Measuring the Environmental Efficiency and Technology Gap of PM 2.5 in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model," IJERPH, MDPI, vol. 16(4), pages 1-22, February.
    19. Jie Wu & Lulu Shen & Ganggang Zhang & Zhixiang Zhou & Qingyuan Zhu, 2024. "Efficiency evaluation with data uncertainty," Annals of Operations Research, Springer, vol. 339(3), pages 1379-1403, August.
    20. Panos Xidonas & Ralph Steuer & Christis Hassapis, 2020. "Robust portfolio optimization: a categorized bibliographic review," Annals of Operations Research, Springer, vol. 292(1), pages 533-552, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:292:y:2020:i:1:d:10.1007_s10479-019-03155-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.