Skip to main content
Log in

Two-stage fuzzy chance-constrained programming: application to water resources management under dual uncertainties

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

In this study, a two-stage fuzzy chance-constrained programming (TFCCP) approach is developed for water resources management under dual uncertainties. The concept of distribution with fuzzy probability (DFP) is presented as an extended form for expressing uncertainties. It is expressed as dual uncertainties with both stochastic and fuzzy characteristics. As an improvement upon the conventional inexact linear programming for handling uncertainties in the objective function and constraints, TFCCP has advantages in uncertainty reflection and policy analysis, especially when the input parameters are provided as fuzzy sets, probability distributions and DFPs. TFCCP integrates the two-stage stochastic programming (TSP) and fuzzy chance-constrained programming within a general optimization framework. TFCCP incorporates the pre-regulated water resources management policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised amounts are not delivered. TFCCP is applied to a water resources management system with three users. Solutions from TFCCP provide desired water allocation patterns, which maximize both the system’s benefits and feasibility. The results indicate that reasonable solutions were generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of stream flows, α-cut levels and fuzzy dominance indices.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Ahmed S, Tawarmalani M, Sahinidis NV (2004) A finite branch-and-bound algorithm for two-stage stochastic integer programs. Math Program 100(2):355–377

    Article  Google Scholar 

  • Ben Abdelaziz F, Enneifar L, Martel JM (2004) A multiobjective fuzzy stochastic program for water resources optimization: the case of lake management. INFOR 42(3):201–215

    Google Scholar 

  • Beraldi P, Grandinetti L, Musmanno R, Triki C (2000) Parallel algorithms to solve two-stage stochastic linear programs with robustness constrains. Parallel Comput 26:1889–1908

    Article  Google Scholar 

  • Chakraborty D (2002) Redefining chance-constrained programming in fuzzy environment. Fuzzy Sets Syst 125:327–333

    Article  Google Scholar 

  • Charnes A, Cooper WW (1983) Response to decision problems under risk and chance constrained programming: dilemmas in the transitions. Manage Sci 29:750–753

    Article  Google Scholar 

  • Charnes A, Cooper WW, Kirby P (1972) Chance constrained programming: an extension of statistical method. In: Optimizing methods in statistics. Academic Press, New York, pp 391–402

  • Cooper WW, Deng H, Huang Z, Li SX (2004) Chance constrained programming approaches to congestion in stochastic data envelopment analysis. Eur J Oper Res 155:487–501

    Article  Google Scholar 

  • Dai L, Chen CH, Birge JR (2000) Convergence properties of two-stage stochastic programming. J Optim Theory Appl 106:489–509

    Article  Google Scholar 

  • Dubois D, Prade H (1983) Ranking fuzzy numbers in the setting of possibility theory. Inf Sci 30:183–224

    Article  Google Scholar 

  • Guo P, Huang GH, He L (2007) ISMISIP: An inexact stochastic mixed integer linear semi-infinite programming approach for solid waste management and planning under uncertainty. Stoch Env Res Risk Assess doi:10.1007/s00477-007-0185-3

  • Hoppe H, Weilandt M, Orth H (2004) A combined water management approach based on river water quality standards. J Env Inf 3(2):67–76

    Article  Google Scholar 

  • Huang GH (1998) A hybrid inexact-stochastic water management model. Eur J Oper Res 107:137–158

    Article  Google Scholar 

  • Huang GH, Chang NB (2003) The perspectives of environmental informatics and systems analysis. J Env Inf 1:1–6

    Article  Google Scholar 

  • Huang GH, Loucks DP (2000) An inexact two-stage stochastic programming model for water resources management under uncertainty. Civil Eng Env Syst 17:95–118

    Article  Google Scholar 

  • Iskander MG (2002) Comparison of fuzzy numbers using possibility programming: comments and new concepts. Comput Math Appl 43:833–840

    Article  Google Scholar 

  • Iskander MG (2005) A suggested approach for possibility and necessity dominance indices in stochastic fuzzy linear programming. Appl Math Lett 18:395–399

    Google Scholar 

  • Jung BS, Karnev BW Lambert MF (2006) Benchmark tests of evolutionary algorithms: mathematic evaluation and application to water distribution systems. J Env Inf 7(1):24–35

    Article  Google Scholar 

  • Li JB (2003) Integration of stochastic programming and factorial design for optimal reservoir operation. J Env Inf 1(2):12–17

    Article  Google Scholar 

  • Li YP, Huang GH, Nie SL (2006a) An interval-parameter multi-stage stochastic programming model for water resources management under uncertainty. Adv Water Resources 29:776–789

    Article  Google Scholar 

  • Li YP, Huang GH, Nie SL, Huang YF (2006b) IFTSIP: interval fuzzy two-stage stochastic mixed-integer linear programming: a case study for environmental management and planning. Civil Eng Env Syst 23(2):73–99

    Article  Google Scholar 

  • Loucks DP, Stedinger JR, Haith DA (1981) Water resource systems planning and analysis. PrenticeHall, Englewood Cliffs

    Google Scholar 

  • Luo B, Maqsood I., Yin YY, Huang GH, Cohen SJ (2003) Adaption to climate change through water trading under uncertainty-An inexact two-stage nonlinear programming approach. J Env Inf 2:58–68

    Article  Google Scholar 

  • Maqsood I, Huang GH, Yeomans JS (2005) An interval-parameter fuzzy two-stage stochastic program for water resources management under uncertainty. Eur J Oper Res 167:208–225

    Article  Google Scholar 

  • Rangarajan S, Simonovic SP (1999) The value of considering autocorrelation between inflows in the stochastic planning of water resource systems. Water Resources Manage 13(6):427–442

    Article  Google Scholar 

  • Sethi LN, Panda SN, Nayak MK (2006) Optimal crop planning and water resources allocation in a coastal groundwater basin, Orissa, India. Agric Water Manage 83(3):209–220

    Article  Google Scholar 

  • Wang D, Adams BJ (1986) Optimization of real-time reservoir operations with Markov decision processes. Water Resources Res 22:345–352

    Article  Google Scholar 

  • Wu SM, Huang GH, Guo HC (1997) An interactive inexact-fuzzy approach for multiobjective planning of water resource systems. Water Sci Technol 36(5):235–242

    Article  Google Scholar 

  • Yin YY, Huang GH, Hipel KW (1999) Fuzzy relation analysis for multicriteria water resources management. J Water Resour Plan Manage ASCE 125(1):41–47

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the Major State Basic Research Development Program (2005CB724200 and 2006CB403307) and the Natural Science and Engineering Research Council of Canada.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to G. H. Huang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Guo, P., Huang, G.H. Two-stage fuzzy chance-constrained programming: application to water resources management under dual uncertainties. Stoch Environ Res Risk Assess 23, 349–359 (2009). https://doi.org/10.1007/s00477-008-0221-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-008-0221-y

Keywords

Navigation