Water resources allocation considering water supply and demand uncertainties using newsvendor model-based framework

Area of Science:

• Environmental Science
• Water Resource Management
• Operations Research

Background:

• Regional water allocation faces challenges due to uncertain supply and demand.
• Existing models may not adequately address cost minimization and shortage reduction.

Purpose of the Study:

• To develop a novel newsvendor model-based framework for optimizing regional water resource allocation.
• To minimize total water allocation costs, including supply, unmet demand, and excess demand penalties.

Main Methods:

• Utilized a newsvendor model incorporating uncertainties in water supply and demand via cumulative distribution functions.
• Applied the model to monthly water allocation for domestic, industrial, and agricultural sectors in Huizhou City, China.
• Modeled water demand with uniform distribution and reservoir inflow with P-III distribution.

Main Results:

• The newsvendor model framework improved water demand satisfied ratios and reduced overall costs compared to actual allocation schemes.
• The proposed framework demonstrated superior performance over traditional deterministic models, showing fewer shortages and higher satisfaction rates.
• The model achieved lower surplus water release and a higher water demand satisfied ratio, indicating robust efficiency.

Conclusions:

• The novel framework effectively integrates market (opportunity loss) and government (water price, penalty loss) properties for optimal water allocation.
• This approach enhances water resource management by reducing shortages, lowering costs, and increasing water use efficiency.
• The framework offers a robust and adaptable solution for water allocation under strict management systems.