Kybernetika 50 no. 5, 774-785, 2014

Improved interval DEA models with common weight

Jiasen Sun, Yajun Miao, Jie Wu, Lianbiao Cui and Runyang ZhongDOI: 10.14736/kyb-2014-5-0774

Abstract:

The traditional data envelopment analysis (DEA) model can evaluate the relative efficiencies of a set of decision making units (DMUs) with exact values. But it cannot handle imprecise data. Imprecise data, for example, can be expressed in the form of the interval data or mixtures of interval data and exact data. In order to solve this problem, this study proposes three new interval DEA models from different points of view. Two examples are presented to illustrate and validate these models.

Keywords:

data envelopment analysis (DEA), interval data, interval DEA model, common weight

Classification:

90B50

paper.pdf

References:

  1. M. Z. Angiz, L. A. Emrouznejad, A. Mustafa and A. S. Al-Eraqi: Aggregating preference ranking with fuzzy data envelopment analysis. Knowledge-Based Systems 23 (2010), 512-519.   CrossRef
  2. M. Braglia and A. Petroni: Evaluating and selecting investments in industrial robots. Int. J. Product. Res. 37 (1999), 4157-4178.   CrossRef
  3. A. Charnes, W. W. Cooper and E. Rhodes: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2 (1978), 429-444.   CrossRef
  4. H. C. Co and K. S. Chew: Performance and R\&D expenditures in American and Japanese manufacturing firms. Int. J. Product. Res. 35 (1997), 3333-3348.   CrossRef
  5. W. W. Cooper, K. S. Park and G. Yu: An illustrative application of IDEA (imprecise data envelopment analysis) to a Korean mobile telecommunication company. Oper. Res. 49 (2011), 807-820.   CrossRef
  6. D. K. Despotis and Y. G. Smirlis: Data envelopment analysis with imprecise data. Eur. J. Oper. Res. 140 (2002), 24-36.   CrossRef
  7. M. S. Haghighat and E. Khorram: The maximum and minimum number of efficient units in DEA with interval data. Appl. Math. Comput. 163 (2004), 919-930.   CrossRef
  8. G. R. Jahanshahloo, F. Hosseinzadeh Lofti and M. Moradi: Sensitivity and stability analysis in DEA with interval data. Appl. Math. Comput. 156 (2004), 463-477.   CrossRef
  9. G. R. Jahanshahloo, R. K. Matin and A. H. Vencheh: On return to scale offully effcient DMUs in data envelopment analysis under interval data. Appl. Math. Comput. 154 (2004), 31-40.   CrossRef
  10. G. R. Jahanshahloo, R. K. Matin and A. H. Vencheh: On FDH effciency analysis with interval data. Appl. Math. Comput. 159 (2004), 47-55.   CrossRef
  11. S. H. Kim, C. G. Park and K. S. Park: An application ofdata envelopment analysis in telephone offices evaluation with partial data. Comput. Oper. Res. 26 (1999), 59-72.   CrossRef
  12. M. C. Lai, H. C. Huang and W. K. Wang: Designing a knowledge-based system for benchmarking: A DEA approach. Knowledge-Based Syst. 24 (2011), 662-671.   CrossRef
  13. Y. K. Lee, K. S. Park and S. H. Kim: Identification of inefficiencies in an additive model based IDEA (imprecise data envelopment analysis). Comput. Oper. Res. 29 (2002), 1661-1676.   CrossRef
  14. S. Sun: Assessing computer numerical control machines using data envelopment analysis. Int. J. Product. Res. 40 (2002), 2011-2039.   CrossRef
  15. R. T. Wang, C. T. B. Ho and K. Oh: Measuring production and marketing efficiency using grey relation analysis and data envelopment analysis. Int. J. Product. Res. 48 (2010), 183-199.   CrossRef
  16. Y. M. Wang, R. Greatbanks and J. B. Yang: Interval efficiency assessment using data envelopment analysis. Fuzzy Sets and Systems 153 (2005), 347-370.   CrossRef