Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Analysis of port efficiency using imprecise and incomplete data

  • Original Paper
  • Published:
Operational Research Aims and scope Submit manuscript

Abstract

Port efficiency assessments based on data envelopment analysis (DEA) usually assume that the inputs-outputs are measured using known and crisp data values. However, the data accuracy, the imprecision, and the missing values are common problems in many port efficiency assessment applications, particularly when the data are drawn from various heterogeneous sources. In those cases, common practice so far was to use either approximated values or to completely exclude these ports from the analysis. This paper proposes Imprecise DEA (IDEA) to assess the efficiency of ports when imprecise and missing data appear in a port assessment problem. In this approach, the missing or imprecise data are replaced by interval or ordinal data, properly estimated using auxiliary data. In a post-DEA analysis stage an iterative procedure is developed with the purpose of estimating new interval bounds that turn non-efficient ports into efficient. This methodology is put into practice through an application that assesses the efficiency of a port sample with missing and imprecise data in the year of reference.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. http://www.closer-project.eu/.

  2. http://pprism.espo.be/Home.aspx.

  3. http://www.southeast-europe.net/en/projects/approved_projects/?id=162.

  4. https://www.imf.org/external/pubs/ft/weo/2012/01/weodata/index.aspx.

  5. http://lpi.worldbank.org/international/global/2012.

References

  • Aigner DJ, Lovell CAK, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econom 6:21–37

    Google Scholar 

  • Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30:1078–1092

    Google Scholar 

  • Barros CP (2006) A benchmark analysis of Italian seaports using data envelopment analysis. Marit Econ Logist 8:347–365

    Google Scholar 

  • Barros CP (2012) Productivity assessment of African seaports. Afr Dev Rev 24(1):67–78

    Google Scholar 

  • Barros CP, Athanassiou M (2004) Efficiency in European seaports with DEA: evidence from Greece and Portugal. Marit Econ Logist 6(2):122–140

    Google Scholar 

  • Bonilla M, Medal A, Casaus T, Sala R, Sala TCR (2002) The traffic in Spanish ports: an efficiency analysis. Int J Transp Econ 29(2):215–230

    Google Scholar 

  • Bray S, Caggiani L, Dell’Orco M, Ottomanelli M (2014) Measuring transport systems efficiency under uncertainty by fuzzy sets theory based data envelopment analysis. Procedia Soc Behav Sci 111:770–779. doi:10.1016/j.sbspro.2014.01.11

    Article  Google Scholar 

  • Brockett PL, Golany B (1996) Using rank statistics for determining programming efficiency differences in data envelopment analysis. Manag Sci 42:466–472

    Google Scholar 

  • Burns MG (2014) Port management and operations. CRC Press. ISBN 9781482206753

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444

    Google Scholar 

  • Charnes A, Rousseau J, Semple H (1996) Sensitivity and stability of efficiency classifications in data envelopment analysis. J Prod Anal 7:5–18

    Google Scholar 

  • Cheon SH, Dowall DE, Song DW (2010) Evaluating impacts of institutional reforms on port efficiency changes: ownership, corporate structure, and total factor productivity changes of world container ports. Transp Res Part E 46:546–561

    Google Scholar 

  • Cook WD, Kress M (1991) A multiple criteria decision model with ordinal preference data. Eur J Oper Res 54:191–198

    Google Scholar 

  • Cook WD, Kress M (1994) A multiple criteria composite index model for quantitative and qualitative data. Eur J Oper Res 78:367–379

    Google Scholar 

  • Cook WD, Zhu J (2006) Rank order data in DEA: a general framework. Eur J Oper Res 174:1021–1038

    Google Scholar 

  • Cook WD, Kress M, Seiford LM (1993) On the use of ordinal data in data envelopment analysis. J Oper Res Soc 44(2):133–140

    Google Scholar 

  • Cook WD, Kress M, Seiford LM (1996) Data envelopment analysis in the presence of both quantitative and qualitative factors. J Oper Res Soc 47:945–953

    Google Scholar 

  • Cooper WW, Park KS, Yu G (1999) Idea and AR-IDEA: models for dealing with imprecise data in DEA. Manag Sci 45(4):597–607

    Google Scholar 

  • Cooper WW, Seiford LM, Tone K (2000) Data envelopment analysis: a comprehensive text with models—applications, references and DEA-Solver Software. Kluwer, Boston

    Google Scholar 

  • Cooper WW, Park KS, Yu G (2001) IDEA (imprecise data envelopment analysis) with CMDs (column maximum decision making units). J Oper Res Soc 52:176–181

    Google Scholar 

  • Coto-Millan P, Banos-Pino J, Rodriguez-Alvarez A (2000) Economic efficiency in Spanish ports: some empirical evidence. Marit Policy Manag 27(2):169–174. doi:10.1080/030888300286581

    Article  Google Scholar 

  • Cullinane KPB (2006) Estimating the relative efficiency of European container ports: a stochastic frontier analysis. Res Transp Econ 16(1):85–115. doi:10.1016/S0739-8859(06)16005-9

    Article  Google Scholar 

  • Cullinane K, Song D-W (2003) A stochastic frontier model of the productive efficiency of Korean container terminals. Appl Econ 35(3):251–267. doi:10.1080/00036840210139355

    Article  Google Scholar 

  • Cullinane K, Wang TF (2010) The efficiency analysis of container port production using DEA panel data approaches. OR Spectrum 32:717–738

    Google Scholar 

  • Cullinane K, Song D-W, Wang T (2005) The application of mathematical programming approaches to estimating container port production efficiency. J Product Anal 24:73–92

    Google Scholar 

  • Cullinane K, Wang T-F, Song D-W, Ji P (2006) The technical efficiency of container ports: comparing data envelopment analysis and stochastic frontier analysis. Transp Res Part A Policy Pract 40(4):354–374

    Google Scholar 

  • Daniel WW (1990) Spearman rank correlation coefficient. Applied nonparametric statistics (2nd edn). PWS-Kent, Boston. 358–365. ISBN 0-534-91976-6

  • de Oliveira GF, Cariou P (2011) A DEA study of the efficiency of 122 iron ore and coal ports and of 15/17 countries in 2005. Marit Policy Manag 38(1):727–743

    Google Scholar 

  • Despotis DK, Smirlis YG (2002) Data envelopment analysis with imprecise data. Eur J Oper Res 140:24–36

    Google Scholar 

  • Dyson RG, Shale EA (2010) Data envelopment analysis, operational research and uncertainty. J Oper Res Soc 61(1):25–34

    Google Scholar 

  • Gligorea R (2013) Key performance indicators measured by the Port of Rotterdam Authority. The Performance Magazine of the KPI Institute. http://www.performancemagazine.org

  • Hatami-Marbini A, Emrouznejad A, Tavana M (2011) A taxonomy and review of the fuzzy data envelopment analysis literature: two decades in the making. Eur J Oper Res 214(3):457–472. doi:10.1016/j.ejor.2011.02.001

    Article  Google Scholar 

  • He FX, Xu X, Chen R, Zhang N (2015) Sensitivity and stability analysis in DEA with bounded uncertainty. Optim Lett 10(4):737–752. doi:10.1007/s11590-015-0895-2

    Article  Google Scholar 

  • Itoh H (2002) Efficiency changes at major container ports in Japan: a window application of data envelopment analysis. Rev Urban Reg Dev Stud 14:133–152

    Google Scholar 

  • Jahanshahloo GR, Lofti F, Moradi M (2004) Sensitivity and stability analysis in DEA with interval data. Appl Math Comput 156(2):463–477

    Google Scholar 

  • Kao C (2006) Interval efficiency measures in data envelopment analysis with imprecise data. Eur J Oper Res 174(2):1087–1099

    Google Scholar 

  • Kao C, Liu ST (2000) Fuzzy efficiency measures in data envelopment analysis. Fuzzy Sets Syst 113:427–437

    Google Scholar 

  • Lin L, Tseng L (2005) Application of DEA and SFA on the measurement of operating efficiencies for 27 international container ports. Proc East Asia Soc Transp Stud 5:592–607

    Google Scholar 

  • Liu Z (1995) The comparative performance of public and private enterprises: the case of British ports. J Transp Econ Policy 29(3):263–274

    Google Scholar 

  • Meeusen W, van den Broeck J (1977) Efficiency estimation from Cobb–Douglas production functions with composed error. Int Econ Rev 18:435–444

    Google Scholar 

  • Merk O, Dang T (2012) Efficiency of world ports in container and bulk cargo (oil. coal. ores and grain). OECD Regional Development Working Papers 2012/09. OECD Publishing. 10.1787/5k92vgw39zs2-en

  • EU Mobility and Transport studies (2013) Impact assessment on: “Measures to enhance the efficiency and quality of port services in the EU”. PwC & Panteia. PWC, July 2013. https://ec.europa.eu/transport/modes/maritime/studies/maritime_en

  • Munisamy S, Jun OB (2013) Efficiency of Latin American container seaports using DEA. In: Proceedings of 3rd Asia-Pacific business research conference 25–26 Feb 2013, Kuala Lumpur. ISBN: 978-1-922069-19-1

  • Nelsen RB (2001) Kendall tau metric. In: Hazewinkel, Michiel, Encyclopedia of Mathematics, Springer, Berlin. ISBN 978-1-55608-010-4

  • Notteboom T, Coeck C, van den Broeck J (2000a) Measuring and explaining the relative efficiency of container terminals by means of Bayesian Stochastic Frontier models. Int J Marit Econ 2:83–106

    Google Scholar 

  • Notteboom T, Coeck C, Van Den Broeck J (2000b) Measuring and explaining the relative efficiency of container terminals by means of Bayesian stochastic frontier models. Int J Marit Econ 2(2):83–106. doi:10.1057/ijme.2000.9

    Article  Google Scholar 

  • Pagano AM, Wang GWY, Sánchez OV, Ungo R (2013) Impact of privatization on port efficiency and effectiveness: results from Panama and US Ports. Marit Policy Manag 40(2):100–115. doi:10.1080/03088839.2012.756589

    Article  Google Scholar 

  • Park KS (2007a) Efficiency bounds and efficiency classifications in DEA with imprecise data. J Oper Res Soc 58:533–540

    Google Scholar 

  • Park KS (2007b) Efficiency bounds and efficiency classifications in DEA with imprecise data. J Oper Res Soc 58:533–540

    Google Scholar 

  • Park KS (2014) Imprecise data envelopment analysis: concepts, methods, and interpretations. Stud Fuzziness Soft Comput 309:47–59. doi:10.1007/978-3-642-41372-8_2

    Article  Google Scholar 

  • Roll Y, Hayuth Y (1993) Port performance comparison applying data envelopment analysis (DEA). Marit Policy Manage 20(2):153–161

    Google Scholar 

  • Schoyen H, Odeck J (2013) The technical efficiency of Norwegian container ports: a comparison to some Nordic and UK container ports using data envelopment analysis (DEA). Marit Econ Logist 15:197–221

    Google Scholar 

  • Sengupta JK (1992) A fuzzy systems approach in data envelopment analysis. Comput Math Appl 24(8–9):259–266

    Google Scholar 

  • Smirlis Y, Zeimpekis V, Kaimakamis G (2012) Data envelopment analysis models to support the selection of vehicle routing software for city logistics operations. Oper Res Int J 12(3):399–420. doi:10.1007/s12351-010-0100-4

    Article  Google Scholar 

  • Thanassoulis E, Portela MC, Allen R (2004) Incoporating value judgment in DEA. In: Cooper WW, Seiford LM, Zhu J (eds) Handbook on data envelopment analysis. Kluwer Academic Publishers, Boston, pp 99–138

    Google Scholar 

  • Tongzon J (2001) Efficiency measurement of selected Australian and other international ports using data envelopment analysis. Transp Res Part A Policy Pract 35:107–122. doi:10.1016/S0965-8564(99)00049-X

    Article  Google Scholar 

  • Trujillo L, Tovar B (2007a) The European port industry: an analysis of its economic efficiency. Marit Econ Logist 9(2):148–171

    Google Scholar 

  • Trujillo L, Tovar B (2007b) The European port industry: an analysis of its efficiency. Marit Econ Logist 9(2):148–171

    Google Scholar 

  • UNCTAD report (2016) Linking performance indicators to strategic objectives. Port Performance: UNCTAD Port Management Series—vol 4. UNCTAD/DTL/KDB/2016/1

  • Wang Y-M, Greatbanks R, Jian-Bo Yang (2005) Interval efficiency assessment using data envelopment analysis. Fuzzy Sets Syst 153:347–370

    Google Scholar 

  • Wilmsmeier G, Tovar B, Sanchez R (2013) The evolution of container terminal productivity and efficiency under changing economic environments. Res Transp Bus Manag 8:50–66

    Google Scholar 

  • Zahran SZ, Alam JB, Al-Zahrani AH, Smirlis Y, Papadimitriou S, Tsioumas V (2015) Analysis of port authority efficiency using data envelopment analysis. Marit Econ Logist. doi:10.1057/mel.2015.33

    Article  Google Scholar 

  • Zhu J (2003a) Imprecise data envelopment analysis (IDEA): a review and improvement with an application. Eur J Oper Res 144(3):513–529

    Google Scholar 

  • Zhu J (2003b) Efficiency evaluation with strong ordinal input and output measures. Eur J Oper Res 146:477–485

    Google Scholar 

Download references

Acknowledgements

This paper was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Under Grant No. (9-135-35/ HiCi). The authors, therefore, acknowledge with thanks DSR for technical and financial support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yiannis Smirlis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zahran, S.Z., Alam, J.B., Al-Zahrani, A.H. et al. Analysis of port efficiency using imprecise and incomplete data. Oper Res Int J 20, 219–246 (2020). https://doi.org/10.1007/s12351-017-0322-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12351-017-0322-9

Keywords

Mathematical Subject Classification