A fractional-factorial probabilistic-possibilistic optimization framework for planning water resources management...

Area of Science:

• Environmental Science
• Operations Research
• Water Resource Management

Background:

• Water resource systems face complex uncertainties from both probabilistic (random) and possibilistic (fuzzy) factors.
• Existing optimization methods may not adequately address the nuanced interactions and decision-maker attitudes inherent in fuzzy parameters.

Purpose of the Study:

• To develop and present a novel multi-level factorial-vertex fuzzy-stochastic programming (MFFP) approach.
• To optimize water resource systems by effectively managing probabilistic and possibilistic uncertainties.
• To explore parameter interactions and their impact on system performance.

Main Methods:

• Development of the multi-level factorial-vertex fuzzy-stochastic programming (MFFP) framework.
• Incorporation of fuzzy parameters at various alpha-cut levels to represent diverse decision-maker preferences.
• Application of multi-level factorial analysis to investigate interactions among fuzzy parameters.
• Demonstration using a water resources management problem with fuzzy and random characteristics.

Main Results:

• The MFFP approach yields effective solutions for optimal water resource allocation under combined fuzziness and randomness.
• Decision-makers can identify optimal water allocation schemes that maximize total net benefits.
• The factorial experiment reveals significant interactions and curvature effects of fuzzy parameters on net benefits.
• A comparison highlights the advantages of MFFP over traditional vertex methods.

Conclusions:

• The MFFP methodology provides a robust framework for optimizing water resource systems with complex uncertainties.
• Understanding parameter interactions is crucial for uncovering hidden information affecting system performance and maximizing benefits.
• The approach supports informed decision-making by generating a variety of policy alternatives under different management scenarios.