All Stories

  1. Measuring global prosperity using data envelopment analysis and OWA operator
  2. Minimizing greenhouse gas emissions using inverse DEA with an application in oil and gas
  3. Gangless cross-evaluation in DEA: an application to stock selection
  4. A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries
  5. A combined goal programming and inverse DEA method for target setting in mergers
  6. Modelling stock selection using ordered weighted averaging operator
  7. Modelling generalized firms’ restructuring using inverse DEA
  8. Minor and major consolidations in inverse DEA: Definition and determination
  9. Application of Optimistic and Pessimistic OWA and DEA Methods in Stock Selection
  10. A combined OWA–DEA method for dispatching rule selection
  11. Cross-efficiency in DEA: A maximum resonated appreciative model
  12. Maximum appreciative cross-efficiency in DEA: A new ranking method
  13. Prioritization of textile fabric defects using ordered weighted averaging operator
  14. Measuring batting parameters in cricket: A two-stage regression-OWA method
  15. An efficient DEA method for ranking woven fabric defects in textile manufacturing
  16. Choosing the best Twenty20 cricket batsmen using ordered weighted averaging
  17. A new DEA model for technology selection in the presence of ordinal data
  18. Some clarifications on the DEA clustering approach
  19. Parametric aggregation in ordered weighted averaging
  20. On the boundedness of the SORM DEA models with negative data
  21. Improving minimax disparity model to determine the OWA operator weights
  22. A comment on modified big-M method to recognize the infeasibility of linear programming models
  23. Input and output scaling in advanced manufacturing technology: theory and application
  24. Comments on finding the most efficient DMUs in DEA: An improved integrated model
  25. DEA models for ratio data: Convexity consideration
  26. Document Similarity: A New Measure Using OWA
  27. Determining More Realistic OWA Weights
  28. Erratum to “A polynomial-time algorithm for finding ε in DEA models”
  29. A note on “an improved MCDM DEA model for technology selection”
  30. Inverse forecasting: A new approach for predictive modeling
  31. Note on “A preemptive goal programming method for aggregating OWA operator weights in group decision making”
  32. Notes on properties of the OWA weights determination model
  33. Finding the most efficient DMUs in DEA: An improved integrated model
  34. An extended minimax disparity to determine the OWA operator weights
  35. An improved MCDM DEA model for technology selection
  36. Comment on “The general form of 0–1 programming problem based on DNA computing, by Yin ZhiXiang et al.”
  37. A polynomial-time algorithm for finding ε in DEA models
  38. An Assurance Interval for the Non-Archimedean Epsilon in DEA Models