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FSILP: fuzzy-stochastic-interval linear programming for supporting municipal solid waste management.

Pu Li¹, Bing Chen

¹Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada.

Journal of Environmental Management

|January 15, 2011

Summary

This summary is machine-generated.

A new fuzzy-stochastic-interval linear programming (FSILP) method efficiently handles uncertainties in municipal solid waste management. This approach improves upon existing methods, offering better solutions and reducing computation time for waste management systems.

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Area of Science:

Operations Research
Environmental Engineering
Applied Mathematics

Background:

Municipal solid waste management (MSW) faces complex uncertainties involving fuzzy, stochastic, and interval data.
Conventional linear programming methods struggle with integrating these diverse uncertainty types efficiently.

Purpose of the Study:

To develop an integrated fuzzy-stochastic-interval linear programming (FSILP) method for robust MSW management.
To improve the efficiency and effectiveness of decision-making under uncertainty in waste management.

Main Methods:

Integration of Nguyen's method with conventional linear programming to address fuzzy and fuzzy-stochastic linear programming problems.
Quantification of attainment values and superiority/inferiority measures for fuzzy and fuzzy-stochastic variables.
Application of the developed FSILP model to a real-world municipal solid waste management case study.

Main Results:

The FSILP method effectively tackles uncertainties from probability density functions, fuzzy membership functions, and discrete intervals.
The developed approach outperforms conventional interval fuzzy programming and two-stage stochastic programming.
The model generated reasonable solutions for the MSW management case study, quantifying cost-uncertainty relationships.

Conclusions:

The FSILP method provides an efficient and effective tool for MSW management under complex uncertainties.
The approach facilitates informed decision-making by analyzing tradeoffs between waste management costs and system failure risks.
This method offers advantages in terms of fewer constraints and reduced consumption time compared to existing techniques.