A Multi-Objective Hierarchical Model for Irrigation Scheduling in the Complex Canal System

被引:11
|
作者
Guo, Shanshan [1 ]
Zhang, Fan [1 ]
Zhang, Chenglong [1 ]
An, Chunjiang [2 ]
Wang, Sufen [1 ]
Guo, Ping [1 ]
机构
[1] China Agr Univ, Ctr Agr Water Res China, Beijing 100083, Peoples R China
[2] Concordia Univ, Dept Bldg Civil & Environm Engn, Montreal, PQ H3G 1M8, Canada
来源
SUSTAINABILITY | 2019年 / 11卷 / 01期
基金
中国国家自然科学基金;
关键词
irrigation scheduling; multilevel multi-objective programming; decomposition-coordination theory; TOPSIS; genetic algorithm; WATER-USE EFFICIENCY; GENETIC ALGORITHMS; OPTIMAL ALLOCATION; OPTIMAL OPERATION; DECISION-MAKING; CLIMATE-CHANGE; OPTIMIZATION; RESOURCES; PRODUCTIVITY; IMPACTS;
D O I
10.3390/su11010024
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Due to population growth, environmental pollution and climate change, the lack of water resources has become a critical factor which threatens sustainable agricultural development. Reasonable irrigation scheduling strategies can reduce the waste of water and enhance agricultural water-use efficiency. In the present study, the decomposition-coordination theory was adopted to analyze the hierarchical canal system. A novel nonlinear multi-level multi-objective optimization model for complex canal systems was established, taking account of the multiple demands from decision makers and realistic factors of canal operation. An interactive method of the technique for order preference using similarity algorithm and genetic algorithm was proposed to solve the developed model. The developed model was successfully applied for the operational strategy making of a canal system located in the arid area of northwest China. The results indicated that the optimization model could help shorten the operational duration by two days, achieve about 26% reduction of irrigation water consumption, and improve the efficiency of water delivery from 0.566 to 0.687. That will be very favorable for the promotion of the agricultural water productivity, the relief of water shortage crisis and the sustainable development of agriculture. The outcomes can provide a wide range of support for decision making and make irrigation decision-making more scientific and systematic.
引用
收藏
页数:15
相关论文
共 50 条
  • [21] Evolutionary Multi-objective Optimization for Evolving Hierarchical Fuzzy System
    Jarraya, Yosra
    Bouaziz, Souhir
    Alimi, Adel M.
    Abraham, Ajith
    2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2015, : 3163 - 3170
  • [22] A multi-scenario and multi-objective scheduling optimization model for liquefied light hydrocarbon pipeline system
    Qiu, Rui
    Zhang, Haoran
    Gao, Xiaoyong
    Zhou, Xingyuan
    Guo, Zhichao
    Liao, Qi
    Liang, Yongtu
    CHEMICAL ENGINEERING RESEARCH & DESIGN, 2019, 141 : 566 - 579
  • [23] Multi-Objective Optimization Model for Inspection Scheduling of Sewer Pipelines
    Elmasry, Mohamed
    Zayed, Tarek
    Hawari, Alaa
    JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, 2019, 145 (02)
  • [24] An uncertain multi-objective programming model for machine scheduling problem
    Yufu Ning
    Xiumei Chen
    Zhiyong Wang
    Xiangying Li
    International Journal of Machine Learning and Cybernetics, 2017, 8 : 1493 - 1500
  • [25] Multi-objective Optimal Scheduling of Electric Vehicles in Distribution System
    Singh, Jyotsna
    Tiwari, Rajive
    2018 20TH NATIONAL POWER SYSTEMS CONFERENCE (NPSC), 2018,
  • [26] A multi-objective and integrated model for supply chain scheduling optimization in a multi-site manufacturing system
    Beheshtinia, Mohammad Ali
    Ghasemi, Amir
    ENGINEERING OPTIMIZATION, 2018, 50 (09) : 1415 - 1433
  • [27] Multi-objective scheduling of electric vehicles in smart distribution system
    Siano, P. (psiano@unisa.it), 1600, Elsevier Ltd (79):
  • [28] Multi-objective scheduling of electric vehicles in smart distribution system
    Zakariazadeh, Alireza
    Jadid, Shahram
    Siano, Pierluigi
    ENERGY CONVERSION AND MANAGEMENT, 2014, 79 : 43 - 53
  • [29] Multi-objective Scheduling for Divisible Load in Heterogeneous Distributed System
    Xuan, Hejun
    Wang, Yuping
    Hao, Shanshan
    Wang, Xiaoli
    2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2016, : 3378 - 3384
  • [30] An uncertain multi-objective programming model for machine scheduling problem
    Ning, Yufu
    Chen, Xiumei
    Wang, Zhiyong
    Li, Xiangying
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2017, 8 (05) : 1493 - 1500