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Violation analysis for solid waste management systems: an interval fuzzy programming approach.

Y F Huang1, B W Baetz, G H Huang

  • 1Canada-China Center of Energy and Environment Research, Hunan University, Changsha 410082, China.

Journal of Environmental Management
|October 9, 2002
PubMed
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This study presents a novel violation analysis approach for regional solid waste management planning under uncertainty. The method allows for some constraint violations, expanding decision options for facility expansion and waste flow allocation.

Area of Science:

  • Environmental Engineering
  • Operations Research
  • Sustainable Waste Management

Background:

  • Regional solid waste management systems face significant uncertainty.
  • Traditional planning models may be too rigid to accommodate real-world variability.
  • Robust decision-making requires flexibility in constraint satisfaction.

Purpose of the Study:

  • To introduce a violation analysis approach for planning regional solid waste management systems.
  • To develop an interval-parameter fuzzy integer programming (IPFIP) model that permits tolerable constraint violations.
  • To expand the decision space for waste management planning under uncertainty.

Main Methods:

  • Developed an interval-parameter fuzzy integer programming (IPFIP) model.

Related Experiment Videos

  • Incorporated violation variables to relax critical system constraints.
  • Applied the methodology to a case study of regional waste management planning.

    • Main Results:

    • The violation analysis approach generated reasonable solutions for waste management planning.
    • Significant information was obtained for facility expansion and waste flow allocation decisions.
    • The approach provided multiple decision alternatives under various system conditions.

    Conclusions:

    • The developed methodology effectively enhances decision-making in uncertain waste management systems.
    • It allows for in-depth analysis of trade-offs between environmental and economic objectives.
    • The approach improves system reliability by offering a range of adaptable solutions.