Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2014, Special Issue (2014), Article ID 961468, 9 pages.

Forecasting the Production Abilities of Recycling Systems: A DEA Based Research

Feng Yang, Fei Du, Liang Liang, and Zheng Yang

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Network production systems are widely observed in the current real life, and the performance management of network production systems has been widely studied. The previous literatures have contributed to the performance evaluation to parallel, serial, and converging systems. However, few literatures have considered the recycling systems. The current paper proposes a method on how to forecast the actual and frontier production abilities of recycling systems. Based on the forecasting results, the paper uses a DEA (data envelopment analysis) based method to appraise the operational efficiency of recycling systems. We choose the water utilization and recycling system as a background for our research. According to the proposed method, Chinese provincial water utilization and recycling systems are examined and analyzed. The results show that the reuse level of water in China is still very low.

Article information

J. Appl. Math., Volume 2014, Special Issue (2014), Article ID 961468, 9 pages.

First available in Project Euclid: 1 October 2014

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Yang, Feng; Du, Fei; Liang, Liang; Yang, Zheng. Forecasting the Production Abilities of Recycling Systems: A DEA Based Research. J. Appl. Math. 2014, Special Issue (2014), Article ID 961468, 9 pages. doi:10.1155/2014/961468.

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  • Z. R. Zhong, “Natural resources planning, management, and sustainable use in China,” Resources Policy, vol. 25, no. 4, pp. 211–220, 1999.
  • O. Varis and P. Vakkilainen, “China\textquotesingle s 8 challenges to water resources management in the first quarter of the 21st century,” Geomorphology, vol. 41, no. 2, pp. 93–104, 2001.
  • E. Thanassoulis, “Use of data envelopment analysis in the regulation of UK water utilities: water distribution,” European Journal of Operational Research, vol. 126, no. 2, pp. 436–453, 2000.
  • A. Charnes, W. W. Cooper, and E. Rhodes, “Measuring the efficiency of decision making units,” European Journal of Operational Research, vol. 2, no. 6, pp. 429–444, 1978.
  • L. Jin and W. Young, “Water use in agriculture in china: importance, challenges, and implications for policy,” Water Policy, vol. 3, no. 3, pp. 215–228, 2001.
  • X. Mo, S. Liu, Z. Lin, Y. Xu, Y. Xiang, and T. R. McVicar, “Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain,” Ecological Modelling, vol. 183, no. 2-3, pp. 301–322, 2005.
  • Y. Huang, L. Chen, B. Fu, Z. Huang, and J. Gong, “The wheat yields and water-use efficiency in the Loess Plateau: straw mulch and irrigation effects,” Agricultural Water Management, vol. 72, no. 3, pp. 209–222, 2005.
  • J.-L. Hu, S.-C. Wang, and F.-Y. Yeh, “Total-factor water efficiency of regions in China,” Resources Policy, vol. 31, no. 4, pp. 217–230, 2006.
  • F. Nasiri and G. Huang, “A fuzzy decision aid model for environmental performance assessment in waste recycling,” Environmental Modelling and Software, vol. 23, no. 6, pp. 677–689, 2008.
  • R. Färe and S. Grosskopf, “Network DEA,” Socio-Economic Planning Sciences, vol. 34, no. 1, pp. 35–49, 2000.
  • H. F. Lewis and T. R. Sexton, “Network DEA: efficiency analysis of organizations with complex internal structure,” Computers and Operations Research, vol. 31, no. 9, pp. 1365–1410, 2004.
  • R. Färe, S. Grosskopf, C. A. K. Lovell, and C. Pasurka, “Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach,” The Review of Economics and Statistics, vol. 71, pp. 90–98, 1989.
  • R. Färe, S. Grosskopf, C. A. K. Lovell, and S. Yaisawarng, “Derivation of shadow prices for undesirable outputs: a distance function approach,” The Review of Economics and Statistics, vol. 75, pp. 374–380, 1993.
  • A. Amirteimoori, S. Kordrostami, and M. Sarparast, “Modeling undesirable factors in data envelopment analysis,” Applied Mathematics and Computation, vol. 180, no. 2, pp. 444–452, 2006.
  • Z. Hua, Y. Bian, and L. Liang, “Eco-efficiency analysis of paper mills along the Huai River: an extended DEA approach,” Omega, vol. 35, no. 5, pp. 578–587, 2007.
  • L. Liang, Y. Li, and S. Li, “Increasing the discriminatory power of DEA in the presence of the undesirable outputs and large dimensionality of data sets with PCA,” Expert Systems with Applications, vol. 36, no. 3, part 2, pp. 5895–5899, 2009.
  • S. Reinhard, C. A. K. Lovell, and G. J. Thijssen, “Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA,” European Journal of Operational Research, vol. 121, no. 2, pp. 287–303, 2000.
  • L. M. Seiford and J. Zhu, “Modeling undesirable factors in efficiency evaluation,” European Journal of Operational Research, vol. 142, no. 1, pp. 16–20, 2002. \endinput