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A Generalized Interval Fuzzy Chance-Constrained Programming Method for Domestic Wastewater Management Under Uncertainty – A Case Study of Kunming, China

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  • C. Dai
  • Y. Cai
  • Y. Liu
  • W. Wang
  • H. Guo

Abstract

In this study, interval mathematical programming (IMP), m λ -measure, and fuzzy chance-constrained programming are incorporated into a general optimization framework, leading to a generalized interval fuzzy chance-constrained programming (GIFCP) method. GIFCP can be used to address not only interval uncertainties in the objective function, variables and left-hand side parameters but also fuzzy uncertainties on the right-hand side. Also, it can reflect the aspiration preference of optimistic and pessimistic decision makers due to the integration of m λ -measure. The developed method is applied to the long-term planning of a domestic wastewater management system in the city of Kunming, China, with consideration of the eco-environmental protection of downstream water body. The solution results of the GIFCP method can generate a series of optimal wastewater allocation patterns and WTPs capacity expansion schemes under different risk levels, provide in-depth insights into the effects of uncertainties, and consider the proper balance between system cost and risk of constraint violation. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • C. Dai & Y. Cai & Y. Liu & W. Wang & H. Guo, 2015. "A Generalized Interval Fuzzy Chance-Constrained Programming Method for Domestic Wastewater Management Under Uncertainty – A Case Study of Kunming, China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3015-3036, July.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:9:p:3015-3036
    DOI: 10.1007/s11269-014-0902-x
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    2. Dai, C. & Cai, Y.P. & Ren, W. & Xie, Y.F. & Guo, H.C., 2016. "Identification of optimal placements of best management practices through an interval-fuzzy possibilistic programming model," Agricultural Water Management, Elsevier, vol. 165(C), pages 108-121.
    3. Lu, W.T. & Dai, C. & Fu, Z.H. & Liang, Z.Y. & Guo, H.C., 2018. "An interval-fuzzy possibilistic programming model to optimize China energy management system with CO2 emission constraint," Energy, Elsevier, vol. 142(C), pages 1023-1039.

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