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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

Modeling municipal solid waste management system under uncertainty.

Yongping Li1, Guohe Huang

  • 1Research Academy of Energy and Environmental Studies, North China Electric Power University, Beijing 102206, People's Republic of China. yongping.li@iseis.org

Journal of the Air & Waste Management Association (1995)
|May 5, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic inexact waste management model to optimize waste flow and facility expansion under uncertainty. It helps assess risks and inform municipal solid waste (MSW) planning and policy.

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Last Updated: Jun 13, 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:

  • Environmental Engineering
  • Operations Research
  • Decision Science

Background:

  • Municipal solid waste (MSW) management requires robust strategies to handle uncertainties in waste generation and facility capacities.
  • Effective planning is crucial for optimizing waste-flow allocation and facility-capacity expansion.
  • Existing models may not adequately address the probabilistic nature of uncertainties in waste management systems.

Purpose of the Study:

  • To develop a dynamic inexact waste management (DIWM) model for optimal decision-making under uncertainty.
  • To integrate an inexact scenario-based probabilistic programming (ISPP) approach for robust planning.
  • To assess the risks associated with violating facility-capacity and waste-diversion constraints.

Main Methods:

  • Development of a dynamic inexact waste management (DIWM) model.
  • Application of an inexact scenario-based probabilistic programming (ISPP) approach.
  • Examination of various violation levels for system constraints and generation of risk-associated solutions.
  • Conducting sensitivity analyses to evaluate the impact of constraint violations.

Main Results:

  • The DIWM model effectively identifies optimal waste-flow allocation and facility-capacity expansion strategies under uncertainty.
  • The model quantifies the risks of violating key system constraints.
  • Solutions were generated for different risk tolerance levels, aiding decision-making.
  • Sensitivity analyses revealed the varied impacts of constraint violations on planning and costs.

Conclusions:

  • The developed DIWM model provides valuable insights for municipal solid waste (MSW) management planning.
  • It supports long-term capacity expansion and the formulation of effective waste diversion policies.
  • The approach enhances the ability to manage uncertainties and associated risks in waste management systems.