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Updated: May 24, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

A generalized fuzzy linear programming approach for environmental management problem under uncertainty.

Yurui Fan¹, Guohe Huang, Amornvadee Veawab

¹Faculty of Engineering, University of Regina, Regina, Saskatchewan, Canada.

Journal of the Air & Waste Management Association (1995)

|March 8, 2012

Summary

This summary is machine-generated.

A new generalized fuzzy linear programming (GFLP) method effectively addresses uncertainties in environmental planning. This approach provides decision-makers with fuzzy set solutions for sulfur dioxide (SO2) control policies, balancing stability and plausibility.

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Last Updated: May 24, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

Environmental Science
Operations Research
Decision Science

Background:

Uncertainties in environmental constraints and objectives pose challenges for traditional optimization models.
Existing methods like interval-parameter linear programming (ILP) may not fully capture the nuances of fuzzy uncertainties.
Effective environmental policy development requires robust methods to handle imprecise data.

Purpose of the Study:

To develop a generalized fuzzy linear programming (GFLP) method for optimizing under fuzzy set uncertainties.
To advance a stepwise interactive algorithm (SIA) for solving GFLP models and generating fuzzy set solutions.
To apply the GFLP method to a regional sulfur dioxide (SO2) control planning model for policy identification.

Main Methods:

Development of a generalized fuzzy linear programming (GFLP) framework.
Advancement of a stepwise interactive algorithm (SIA) to solve GFLP models.
Application of GFLP to a regional SO2 emission control planning problem.

Main Results:

The GFLP method successfully generated solutions expressed as fuzzy sets for SO2 mitigation policies.
Results quantified SO2 allocation across various control measures and sources under uncertainty.
Compared to ILP, GFLP provided richer information, including intervals and possibilities for decision variables and objective functions.

Conclusions:

The GFLP method offers a superior approach for environmental decision-making under uncertainty compared to conventional methods.
Fuzzy set solutions enable decision-makers to better assess tradeoffs between model stability and plausibility.
The developed method facilitates the identification of effective and robust SO2 emission control policies.