A two-stage mixed-integer fuzzy programming with interval-valued membership functions approach for flood-diversion...

Area of Science:

• Environmental Science
• Water Resource Management
• Operations Research

Background:

• Flood disasters are increasing in severity, posing significant global risks.
• Effective flood diversion planning is crucial for mitigating natural catastrophe impacts.
• Existing methods struggle to fully address complex system uncertainties.

Purpose of the Study:

• To develop an advanced optimization approach for flood diversion planning under uncertainty.
• To integrate fuzzy logic, stochastic programming, and integer programming for robust decision-making.
• To provide a framework that incorporates pre-existing water diversion policies.

Main Methods:

• A two-stage mixed-integer fuzzy programming with interval-valued membership functions (TMFP-IMF) approach was developed.
• The method integrates fuzzy flexible programming, two-stage stochastic programming, and integer programming.
• Interval-valued fuzzy membership functions were introduced to handle system uncertainties.

Main Results:

• The TMFP-IMF approach successfully generated reasonable solutions for both binary and continuous variables.
• Diverse flood-diversion and capacity-expansion schemes were identified across four distinct scenarios.
• The approach demonstrated applicability in a hypothetical flood-diversion planning case study.

Conclusions:

• The TMFP-IMF approach offers a powerful tool for flood diversion planning under uncertainty.
• It enables decision-makers to select optimal strategies based on risk tolerance and objectives.
• This method enhances the ability to manage complex water resource systems facing flood risks.