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FLFP: A fuzzy linear fractional programming approach with double-sided fuzziness for optimal irrigation water allocation

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  • Zhang, Chenglong
  • Guo, Ping

Abstract

In this study, a fuzzy linear fractional programming (FLFP) approach with double-sided fuzziness is developed for optimal irrigation water allocation under uncertainty. The FLFP model can be derived from incorporating double-sided fuzzy chance-constrained programming (DFCCP) into linear fractional programming (LFP) optimization framework. The developed model can deal with uncertainty presented as fuzziness in both right-hand and left-hand sides of constraints. Moreover, it has advantages in: (1) addressing two objectives directly without considering subjective factors, (2) effectively reflecting economic water productivity between total system economic benefit and total irrigation water use, (3) introducing the concept of confidence levels of fuzzy constraints-satisfaction under both the minimum and maximum reliabilities to generate more flexible solutions and (4) facilitating in-depth analysis of interrelationships among economic water productivity, system benefits and varying confidence levels. The model is applied to a case study of irrigation water allocation in the middle reaches of Heihe River Basin, northwest China. The optimal irrigation water allocation solutions from the FLFP model can be obtained. These results can provide decision-support when deciding on selecting reasonable irrigation water resources management and agricultural production.

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  • Zhang, Chenglong & Guo, Ping, 2018. "FLFP: A fuzzy linear fractional programming approach with double-sided fuzziness for optimal irrigation water allocation," Agricultural Water Management, Elsevier, vol. 199(C), pages 105-119.
    • Handle: RePEc:eee:agiwat:v:199:y:2018:i:c:p:105-119
    DOI: 10.1016/j.agwat.2017.12.013
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    1. Chenglong Zhang & Qiong Yue & Ping Guo, 2019. "A Nonlinear Inexact Two-Stage Management Model for Agricultural Water Allocation under Uncertainty Based on the Heihe River Water Diversion Plan," IJERPH, MDPI, vol. 16(11), pages 1-18, May.
    2. Yue, Qiong & Zhang, Fan & Zhang, Chenglong & Zhu, Hua & Tang, Yikuan & Guo, Ping, 2020. "A full fuzzy-interval credibility-constrained nonlinear programming approach for irrigation water allocation under uncertainty," Agricultural Water Management, Elsevier, vol. 230(C).
    3. Zhang, Chenglong & Engel, Bernard A. & Guo, Ping, 2018. "An Interval-based Fuzzy Chance-constrained Irrigation Water Allocation model with double-sided fuzziness," Agricultural Water Management, Elsevier, vol. 210(C), pages 22-31.
    4. Zhang, Chenglong & Yang, Gaiqiang & Wang, Chaozi & Huo, Zailin, 2023. "Linking agricultural water-food-environment nexus with crop area planning: A fuzzy credibility-based multi-objective linear fractional programming approach," Agricultural Water Management, Elsevier, vol. 277(C).
    5. Sun, J. & Li, Y.P. & Suo, C. & Liu, J., 2020. "Development of an uncertain water-food-energy nexus model for pursuing sustainable agricultural and electric productions," Agricultural Water Management, Elsevier, vol. 241(C).
    6. Zhang, Chenglong & Guo, Ping & Huo, Zailin, 2021. "Irrigation water resources management under uncertainty: An interval nonlinear double-sided fuzzy chance-constrained programming approach," Agricultural Water Management, Elsevier, vol. 245(C).
    7. Li, Xuemin & Zhang, Chenglong & Huo, Zailin & Adeloye, Adebayo J., 2020. "A sustainable irrigation water management framework coupling water-salt processes simulation and uncertain optimization in an arid area," Agricultural Water Management, Elsevier, vol. 231(C).

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