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Interval-parameter semi-infinite fuzzy-stochastic mixed-integer programming approach for environmental management

P Guo¹, G H Huang

¹College of Water Conservancy and Civil Engineering, China Agricultural University, Beijing 100083, China. guoping@iseis.org

Waste Management (New York, N.Y.)

|October 27, 2009

Summary

This summary is machine-generated.

This study introduces a new optimization method for waste management planning, handling multiple uncertainties. It supports decisions on facility expansion and waste flow, aiding long-term system sustainability.

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Area of Science:

Operations Research
Environmental Engineering
Decision Science

Background:

Waste management systems face complex uncertainties impacting long-term planning.
Existing optimization methods have limitations in handling diverse and combined uncertainties.

Purpose of the Study:

To develop an interval-parameter semi-infinite fuzzy-chance-constrained mixed-integer linear programming (ISIFCIP) approach.
To support long-term planning for waste management systems under multiple uncertainties.
To provide decision support for facility expansion and waste flow allocation.

Main Methods:

Developed an ISIFCIP approach integrating dual uncertainties of functional intervals and distribution uncertainties.
Incorporated fuzzy-interval admissible probability for constraint violation.
Applied the method to the City of Regina, Canada, analyzing two waste management scenarios.

Main Results:

Scenario 1 (existing policy) projected a 14% residential waste diversion rate.
Scenario 2 (waste minimization) projected 35% diversion in 15 years and 50% in 25 years, requiring landfill, CF, and MRF expansion.
Generated decision support for solid waste managers regarding cost-risk tradeoffs and facility planning.

Conclusions:

The ISIFCIP approach effectively tackles multiple, combined uncertainties in waste management planning.
The method facilitates dynamic analysis for multi-facility, multi-period, and multi-option decisions.
Provides valuable insights for optimizing waste management strategies and resource allocation.