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Dynamic analysis for solid waste management systems: an inexact multistage integer programming approach.

Yongping Li1, Guohe Huang

  • 1College of Urban and Environmental Sciences, Peking University, Beijing, People's Republic of China. yongping.li@urban.pku.edu.cn

Journal of the Air & Waste Management Association (1995)
|March 27, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces an inexact multistage integer programming (IMIP) model for municipal solid waste (MSW) management under uncertainty. The model optimizes waste-flow allocation and facility expansion for cost reduction and reliability enhancement.

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Area of Science:

  • Operations Research
  • Environmental Engineering
  • Decision Science

Background:

  • Municipal solid waste (MSW) management faces significant uncertainties in waste generation and processing.
  • Long-term planning for waste management requires robust models that account for dynamic changes and economic factors.
  • Existing models may not adequately address the complexities of uncertain parameters and multistage decision-making.

Purpose of the Study:

  • To develop a dynamic analysis approach for municipal solid waste (MSW) management under uncertainty.
  • To incorporate interval-parameter programming and multistage stochastic programming into an integer-programming framework.
  • To support decision-making for waste-flow allocation and facility-capacity expansion over multiple time stages.

Main Methods:

  • Development of an inexact multistage integer programming (IMIP) model.
  • Integration of interval-parameter programming and multistage stochastic programming techniques.
  • Application of the IMIP model to a case study of long-term waste-management planning.

Main Results:

  • The IMIP model effectively handles uncertainties expressed as probability distributions and interval numbers.
  • The model provides dynamic insights into waste-flow allocation and facility-capacity expansion decisions.
  • Reasonable solutions were generated for binary and continuous variables, optimizing system cost and reliability.

Conclusions:

  • The developed IMIP model offers a robust framework for MSW management under uncertainty.
  • The approach facilitates informed decisions regarding system capacity and waste-flow allocation.
  • The method aids in minimizing costs and maximizing system reliability in long-term waste management planning.