DOI QR코드

DOI QR Code

유입량의 변동성을 고려한 Fuzzy DEA 기반의 댐 군 연계운영 가중치 대안 평가

An Evaluation of Multi-Reservoir Operation Weighting Coefficients Using Fuzzy DEA taking into account Inflow Variability

  • 김용기 (고려대학교 정보경영공학전문대학원) ;
  • 김재희 (전북대학교 경영학부) ;
  • 김승권 (고려대학교 기술경영전문대학원)
  • Kim, Yong-Ki (Graduate School of Information Management and Security, Korea University) ;
  • Kim, Jae-Hee (Division of Business Administration, Chonbuk National University) ;
  • Kim, Sheung-Kown (School of Industrial Management Engineering, Korea University)
  • 투고 : 2011.02.01
  • 심사 : 2011.04.26
  • 발행 : 2011.09.01

초록

The multi-reservoir operation problem for efficient utilization of water resources involves conflicting objectives, and the problem can be solved by varying weight coefficient on objective functions. Accordingly, decision makers need to choose appropriate weight coefficients balancing the trade-offs among multiple objectives. Although the appropriateness of the weight coefficients may depend on the total amount of water inflow, reservoir operating policy may not be changed to a certain degree for different hydrological conditions on inflow. Therefore, we propose to use fuzzy Data Envelopment Analysis (DEA) to rank the weight coefficients in consideration of the inflow variation. In this approach, we generate a set of Paretooptimal solutions by applying different weight coefficients on Coordinated Multi-reservoir Operating Model. Then, we rank the Pareto-optimal solutions or the corresponding weight coefficients by using Fuzzy DEA model. With the proposed approach, we can suggest the best weight coefficients that can produce the appropriate Pareto-optimal solution considering the uncertainty of inflow, whereas the general DEA model cannot pinpoint the best weight coefficients.

키워드

참고문헌

  1. Charnes, A., Cooper, W. W., and Rhodes, E. (1978), Measuring the efficiency of decision making units, European Journal of Operational Research, 2, 429-444.
  2. Guo, P. and Tanaka, H. (2001), Fuzzy DEA : perceptual evaluation method, Fuzzy Sets and Systems, 119, 149-160. https://doi.org/10.1016/S0165-0114(99)00106-2
  3. Deb, K. (2001), Multi-objective optimization using evolutionary algorithms, John Wiley and Sons, Chichester.
  4. Eschenbach, E. A., Magee, T., Zagona, E., Goranflo, M., and Shane, R. (2001), Goal Programming Decision Support System for Multiobjective Operation of Reservoir Systems, Journal of Water Resources Planning and Management, 127 (2), 108-120. https://doi.org/10.1061/(ASCE)0733-9496(2001)127:2(108)
  5. Kim, M. G., Kim, J. H., and Kim, S. K. (2008), Determination of weight coefficients of multiple objective reservoir operation problem considering inflow variation, Journal of the Korea Water Resources Association, 41(1), 1-15. https://doi.org/10.3741/JKWRA.2008.41.1.001
  6. Kim, S. K. and Park, Y. J. (1998), A Mathematical Model for Coordinated Multiple Reservoir Operation, Journal of the Korea Water Resources Association, 31(6), 779-793.
  7. Kim, S. K., Lee, Y. D., Kim, J. H., and Ko, I. H. (2005), A multiple objective mathematical model for daily coordinated multi-reservoir operation, Water Science and Technology : water supply, International Water Association, 5(3-4), 81-88.
  8. Kim, J. H. and Kim, S. K. (2006), A CHIM based interactive tchebycheff procedure for multiple objective decision making, Computers and Operations Research, 33(6), 1557-1574. https://doi.org/10.1016/j.cor.2004.11.007
  9. Labadie, J. W. (2004), Optimal operation of multireservoir systems : state-of-art review, Journal of Water Resources Planning and Management, 130(2), 93-111. https://doi.org/10.1061/(ASCE)0733-9496(2004)130:2(93)
  10. Lee, Y. D. (2008), Stochastic Linear Programming Models for Coordinated Multi- Reservoir Operation, Ph. D. Dissertation, Korea University.
  11. Leon, T., Liern, V., Ruiz, J. L., and Sirvent, I. (2003), A fuzzy mathematical programming approach to the assessment of efficiency with DEA models, Fuzzy Sets and Systems, 139(2), 407-419. https://doi.org/10.1016/S0165-0114(02)00608-5
  12. Na, M. S., Kim J. H., and Kim S. K. (2010), Development of Operating Guidelines of a Multi-reservoir System Using an Artificial Neural Network Model, IE Interfaces, 23(4), 310-317.
  13. Saati, S., Menariani, A., and Jahanshahloo, G. R. (2002), Efficiency analysis and ranking of DMUs with fuzzy data, Fuzzy Optimization and Decision Making, 1(3), 255-267. https://doi.org/10.1023/A:1019648512614
  14. Sengupta, J. K. (1992), A fuzzy systems approach in data envelopment analysis, Computers and Mathematics with Applications, 24(8-9), 259-266. https://doi.org/10.1016/0898-1221(92)90203-T
  15. Yeh, W. W.-G. (1985), Reservoir management and operations models : A state of art review, Water Resources Research, 21(12), 1797-1818. https://doi.org/10.1029/WR021i012p01797
  16. Wang Y. M., Luo Y., and Liang L. (2009), Fuzzy data envelopment analysis based upon fuzzy arithmetic with an application to performance assessment of manufacturing enterprises, Expert systems with applications, 36, 5205-5211. https://doi.org/10.1016/j.eswa.2008.06.102
  17. Wen. M., You. C., and Kang, R. (2010), A new ranking method to fuzzy data envelopment analysis, Computers and Mathematics with Applications, 59, 3398-3404. https://doi.org/10.1016/j.camwa.2010.02.034
  18. William, W. C., Lawrence, M. S., and Kaoru, T. (2007), Data Envelopment Analysis : A Comprehensive Text with Models, Applications, References and DEA-Solver Software, Second Edition, Springer, New York.