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Updated: Jun 18, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

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

Published on: December 9, 2012

An interval-parameter stochastic robust optimization model for supporting municipal solid waste management under

Y Xu1, G H Huang, X S Qin

  • 1Sino-Canada Center of Energy and Environmental Research, North China Electric Power University, Beijing 102206, China.

Waste Management (New York, N.Y.)
|November 11, 2009
PubMed
Summary
This summary is machine-generated.

A new model for municipal solid waste management integrates interval and stochastic theories to balance costs, variability, and risks. This approach aids in selecting optimal waste management strategies under uncertainty.

Related Experiment Videos

Last Updated: Jun 18, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

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

Published on: December 9, 2012

Area of Science:

  • Operations Research
  • Environmental Management
  • Applied Mathematics

Background:

  • Municipal solid waste management faces significant uncertainties.
  • Existing optimization methods like stochastic robust optimization (SRO) and interval linear programming (ILP) have limitations in handling complex trade-offs.

Purpose of the Study:

  • To develop a novel stochastic robust interval linear programming (IPRO) model for municipal solid waste management.
  • To enable simultaneous evaluation of trade-offs between expected costs, cost variability, and constraint violation risks.
  • To incorporate complex uncertainties using both interval and stochastic theories.

Main Methods:

  • Development of the IPRO model, integrating interval and stochastic optimization principles.
  • Application of the IPRO model to a long-term municipal solid waste management problem.
  • Analysis of trade-offs among cost, variability, and risk.

Main Results:

  • The IPRO model generates interval solutions, offering flexibility in waste management alternatives.
  • Decision-variable values can be adjusted within their intervals to generate various management options.
  • The model effectively identifies policies considering environmental, economic, feasibility, and reliability constraints.

Conclusions:

  • The IPRO model provides a robust framework for municipal solid waste management under uncertainty.
  • It enhances decision-making by allowing a comprehensive assessment of various factors and risks.
  • The model supports the identification of optimal and reliable waste management policies.