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An Interval Mixed-Integer Semi-Infinite Programming Method for Municipal Solid Waste Management.

Li He1, Guo H Huang2,3, Guangming Zeng4

  • 1a Centre for Studies in Energy and Environment , University of Regina , Regina , Saskatchewan , Canada.

Journal of the Air & Waste Management Association (1995)
|November 9, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an interval mixed-integer semi-infinite programming (IMISIP) method for robust solid waste management. The novel approach effectively handles uncertainties, leading to more globally optimal waste management policies.

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Area of Science:

  • Operations Research
  • Environmental Engineering
  • Optimization

Background:

  • Solid waste management faces significant uncertainties in parameters like facility capacities and operational costs.
  • Existing interval mixed-integer linear programming (IMILP) methods may not fully capture dynamic system fluctuations.
  • Semi-infinite programming (SIP) offers a framework for problems with infinite constraints, often arising in uncertainty modeling.

Purpose of the Study:

  • To develop a novel interval mixed-integer semi-infinite programming (IMISIP) method to address solid waste management under uncertainty.
  • To enhance the capability of optimization models to handle complex inexact programming problems in waste management.
  • To provide a more accurate and globally optimal decision-making framework for waste management policies.

Main Methods:

  • The proposed IMISIP method integrates concepts from interval mixed-integer linear programming (IMILP) and semi-infinite programming (SIP).
  • Uncertainty is modeled using constants, intervals, and functional intervals to represent real-world variability.
  • The method is applied to a solid waste management system, considering scenarios with expansions of waste-to-energy (WTE) and landfilling facilities.

Main Results:

  • The IMISIP method effectively handles uncertainties in solid waste management problems.
  • Application to waste management scenarios demonstrates the method's ability to identify optimal policies under interval and functional-interval uncertainties.
  • The study considered two scenarios: expansion of WTE facilities only, and expansion of both WTE and landfilling facilities.

Conclusions:

  • The IMISIP method provides a powerful tool for optimizing solid waste management under various forms of uncertainty.
  • Solutions derived from IMISIP can be globally optimal, reflecting dynamic system input fluctuations more effectively than conventional IMILP.
  • The findings support informed decision-making for sustainable and resilient waste management strategies.