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A dual-randomness bi-level interval multi-objective programming model for regional water resources management.

Jun Xiao1, Yanpeng Cai1, Yanhu He1

  • 1Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China.

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|May 9, 2021
PubMed
Summary
This summary is machine-generated.

A new dual-randomness bi-level interval multi-objective programming (DR-BIMP) model enhances water resource management. This model effectively handles complex uncertainties and supports decision-makers in allocating resources across sectors.

Keywords:
Bi-level multi-objective programmingComplexities and uncertaintiesDouble-sided stochastic chance-constrained programmingInterval parameter programmingWater resources management

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Area of Science:

  • Environmental Science and Management
  • Operations Research
  • Water Resource Systems Analysis

Background:

  • Water resource management faces complexities and uncertainties from multiple decision-makers and data variability.
  • Existing bi-level multi-objective programming models often struggle to simultaneously address discrete interval parameters and dual-randomness.
  • Effective water allocation requires models that can characterize interval data and probability distributions under hierarchical decision-making.

Purpose of the Study:

  • To develop a novel Dual-Randomness Bi-level Interval Multi-objective Programming (DR-BIMP) model for water resource management.
  • To integrate bi-level multi-objective programming, double-sided stochastic chance-constrained programming, and interval parameter programming.
  • To provide a robust framework for optimizing water allocation under complex uncertainties and hierarchical decision structures.

Main Methods:

  • Development of the DR-BIMP model by incorporating bi-level multi-objective programming (BMOP), double-sided stochastic chance-constrained programming (DSCCP), and interval parameter programming (IPP).
  • Application of the DR-BIMP model to a case study of water resource allocation in the Dongjiang River basin, China, involving three water sectors and five sub-regions.
  • Comparative analysis of the DR-BIMP model against other related models to highlight its advantages in decision support.

Main Results:

  • The DR-BIMP model successfully generated determining values, interval values, and stochastic distribution information for water resource allocation.
  • Increasing the probability level enhanced system benefits (from [20,786.00, 26,425.92] × 108 CNY to [22,290.84, 27,492.57] × 108 CNY) and reduced the Gini value (from [0.365, 0.446] to [0.345, 0.405]).
  • The model demonstrated superior performance in generating diverse decision alternatives for bi-level decision-makers under various scenarios.

Conclusions:

  • The DR-BIMP model is a valuable tool for optimizing water resource allocation in complex, uncertain environments with multiple stakeholders.
  • The model effectively addresses limitations of existing methods by simultaneously handling discrete interval parameters and dual-randomness.
  • The findings support improved planning and decision-making for sustainable water resource management in river basins.