Skip to main content
Log in

A Two-Step Infinite α-Cuts Fuzzy Linear Programming Method in Determination of Optimal Allocation Strategies in Agricultural Irrigation Systems

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

A two-step infinite α-cuts fuzzy linear programming (TSIFP) method is developed in this study. The introduction of infinite α-cuts to conventional fuzzy linear programming frameworks makes it possible to generate more reliable optimal results than conventional fuzzy linear programming, where finite α-cuts were assumed to be sufficient in representing all fuzzy information of the membership functions. In contrast to the previous studies, the proposed TSIFP can be noted as the first attempt in solving FLP without any unreasonable simplification and assumption. An agricultural irrigation system is then provided for demonstrating its applicability. The results show that reasonable solutions and allocation strategies are obtained. As a typical finite α-cuts fuzzy linear programming method, fuzzy robust linear programming (FRLP) is further considered to solve the same problem; results from this method are then compared with those from TSIFP. It is indicated that, due to the constraints being relaxed in FRLP, more water beyond the system’s capacity would be over-allocated for pursuing higher system benefits, implying the unreliability of FRLP in being extended to real-world practices. Two scenario analyses under different α-level cutting means for FRLP are also investigated.

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.

Similar content being viewed by others

References

  • Anderson EJ, Nash P (1987) Linear programming in infinite-dimensional spaces: theory and applications. Wiley, London

    Google Scholar 

  • Ben-Tal A, Nemirovski A (1998) Robust convex optimization. Math Oper Res 23:769–805

    Article  Google Scholar 

  • Ben-Tal A, Nemirovski A (1999) Robust solutions to uncertain linear programs. Oper Res Lett 25:1–13

    Article  Google Scholar 

  • Birge JR, Louveaux FV (1988) A multicut algorithm for two-stage stochastic linear programs. Eur J Oper Res 34:384–392

    Article  Google Scholar 

  • Buckley JJ (1989) A fast method of ranking alternatives using fuzzy numbers. Fuzzy Sets Syst 30:337–338

    Article  Google Scholar 

  • Chiang JS (2001) Fuzzy linear programming based on statistical confidence interval and interval-valued fuzzy set. Eur J Oper Res 129(1):65–86

    Article  Google Scholar 

  • Delgado M, Verdegay JL, Vila MA (1989) A general model for fuzzy linear programming. Fuzzy Sets and Syst 29:21–29

    Article  Google Scholar 

  • Dupacova J (1998) Reflections on robust optimization. Lect Notes Econ Math Syst 458:111–127

    Google Scholar 

  • El Ghaoui L, Seigneuret F (1998) Robust optimization methodologies for the free route concept. Proc Am Control Conf 1797–1799

  • Fang SC, Puthenpura SC (1993) Linear optimization and extensions: theory and algorithms. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Fang SC, Hu CF, Wang HF et al (1999) Linear programming with fuzzy coefficients in constraints. Comput Math Appl 37:63–76

    Article  Google Scholar 

  • Gorzalczany MB (1987) A method of inference in approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets Syst 21:1–17

    Article  Google Scholar 

  • Guo HC, Zou R, Xu YL, Huang GH et al (1999) An inexact fuzzy multi-objective programming method and its application to the watershed environmental planning—II. case study. Environ Sci 19(1):33–37

    Google Scholar 

  • He L, Huang GH, Liao RF et al (2004) Stochastic optimization programming for multi-reach river system using GA combined with stochastic simulation. Trans Nonferr Met Soc China 14(Special 1):31–36

    Google Scholar 

  • He L, Huang GH, Zeng GM et al (2008) Wavelet-based multiresolution analysis technique for data cleaning and its application to water quality management system. Expert Syst Appl 35(3):1301–1310. doi:10.1016/j.eswa.2007.08.009

    Article  Google Scholar 

  • He L, Huang GH, Zeng GM et al (2009) Identifying optimal regional solid waste management strategies through a new inexact integer programming model containing infinite objectives and constraints. Waste Manage 29(1):21–31. doi:10.1016/j.wasman.2008.02.003

    Article  Google Scholar 

  • Huang GH, Baetz BW, Patry GG (1998) Trash flow allocation: planning under uncertainty. Interfaces 28(6):36–55

    Article  Google Scholar 

  • Huang GH, Sae-Lim N, Liu L et al (2001) An interval-parameter fuzzy-stochastic programming approach for municipal solid waste management and planning. Environ Model Assess 6(4):271–283

    Article  Google Scholar 

  • Huang YF, Baetz BW, Huang GH et al (2002) Violation analysis for solid waste management systems: an interval fuzzy programming approach. J Environ Manag 65(4):431–446

    Google Scholar 

  • Huang GH, He L, Zeng GM et al (2008) Identification of the optimal urban solid waste flow schemes under impacts of energy prices. Environ Eng Sci 25(5):686–696. doi:10.1089/ees.2007.0078

    Article  Google Scholar 

  • Inuiguchi M, Sakawa M (1998) Robust optimization under softness in a fuzzy linear programming problem. Int J Approx Reason 18:21–34

    Article  Google Scholar 

  • Inuiguchi M, Ramik J, Tanino T et al (2003) Satisficing solutions and duality in interval and fuzzy linear programming. Fuzzy Sets Syst 135(1):151–177

    Article  Google Scholar 

  • Kizer M (1991) Alfalfa irrigation. Chapter 6. In: Alfalfa production and pest management in Oklahoma. Circular E-826. Oklahoma Cooperative Extension Service

  • Lai YJ, Hwang CL (1992) Fuzzy mathematical programming: methods and applications. Springer, Heidelberg

    Google Scholar 

  • Lee YW, Bogardi I, Stansbury J (1991) Fuzzy decision making in dredged-material management. J Environ Eng ASCE 117(2):614–628

    Article  Google Scholar 

  • Li JB, Huang GH, Chakma A et al (2003) Integrated fuzzy-stochastic modeling of petroleum contamination in subsurface. Energy Sources 25(6):547–564

    Article  Google Scholar 

  • Lin XC, Lee LH (2006) A new approach to discrete stochastic optimization problems. Eur J Oper Res 172(3):761–782

    Article  Google Scholar 

  • Liu L, Huang GH, Liu Y et al (2003) A fuzzy-stochastic robust programming model for regional air quality management under uncertainty. Eng Optim 35(2):177–199

    Article  Google Scholar 

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

    Google Scholar 

  • Lu HW, Huang GH, Zeng GM et al (2008a) An inexact two-stage fuzzy-stochastic programming model for water resources management. Water Resour Manag 22(8):991–1016. doi:10.1007/s11269-007-9206-8

    Article  Google Scholar 

  • Lu HW, Huang GH, He L (2008b) A SIA-based inexact two-stage stochastic fuzzy linear programming approach for water resources management. Eng Optim. doi:10.1080/03052150802345987

  • Lu HW, Huang GH, He L (2008c) An inexact programming method for agricultural irrigation systems under parameter uncertainty. Stoch Environ Res Risk Assess. doi:10.1007/s00477-008-0256-0

  • Lu HW, Huang GH, Liu L et al (2008d) An interval-parameter fuzzy-stochastic programming approach for air quality management under uncertainty. Environ Eng Sci 25(6):895–910

    Article  Google Scholar 

  • Maqsood I (2004) Development of simulation- and optimization-based decision support methodologies for environmental systems management. Ph.D. Thesis, University of Regina, Regina, Saskatchewan, Canada

  • 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(1):208–225

    Article  Google Scholar 

  • Rommelfanger H (2004) The advantages of fuzzy optimization models in practical use. Fuzzy Optim Decis Making 3:295–309

    Article  Google Scholar 

  • Stichler C (1997) Texas alfalfa production. Texas Agricultural Extension Service B-5017. College Station, TX

  • Tanaka H, Ichihashi H, Asai K (1984) A formulation of fuzzy linear programming problems based on comparison of fuzzy numbers. Control Cybernet 13:185–194

    Google Scholar 

  • Tong SC (1994) Interval number and fuzzy number linear programmings. Fuzzy Sets Syst 66(3):301–306

    Article  Google Scholar 

  • Trostle C (2003) Texas high plains supplement to Texas alfalfa production. Texas Cooperative Extension Bulletin B-5017. Lubbock, TX

  • Vasant PM (2003) Application of fuzzy linear programming in production planning. Fuzzy Optim Decis Making 3:229–241

    Article  Google Scholar 

  • Wang DW (1997) An inexact approach for linear programming problems with fuzzy objective and resources. Fuzzy Sets Syst 89:61–68

    Article  Google Scholar 

  • Wang LJ, Meng W, Guo HC et al (2006) An interval fuzzy multiobjective watershed management model for the Lake Qionghai watershed, China. Water Resour Manag 20(5):701–721

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  Google Scholar 

  • Zimmermann HJ (1976) Description and optimization of fuzzy system. Int J Gen Syst 2:209–216

    Article  Google Scholar 

  • Zimmermann HJ (1991) Fuzzy set theory and its applications. Kluwer, Norwell

    Google Scholar 

  • Zou R, Lung WS, Guo HC et al (2000) Independent variable controlled grey fuzzy linear programming approach for waste flow allocation planning. Eng Optim 33(1):87–111

    Article  Google Scholar 

Download references

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

Lu, H.W., Huang, G.H., Lin, Y.P. et al. A Two-Step Infinite α-Cuts Fuzzy Linear Programming Method in Determination of Optimal Allocation Strategies in Agricultural Irrigation Systems. Water Resour Manage 23, 2249–2269 (2009). https://doi.org/10.1007/s11269-008-9380-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-008-9380-3

Keywords

Navigation