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Inexact fuzzy-stochastic constraint-softened programming - A case study for waste management.
1College of Urban and Environmental Sciences, Peking University, Beijing 100871, China. yongping.li@urban.pku.edu.cn
Waste Management (New York, N.Y.)
|January 28, 2009
Summary
This study introduces a flexible programming method for municipal solid waste (MSW) management, effectively handling uncertainties and allowing for constraint violations to optimize decisions.
Area of Science:
- Operations Research
- Environmental Management
- Decision Science
Background:
- Municipal solid waste (MSW) management faces significant uncertainty from fuzzy sets, interval values, and random variables.
- Existing models often struggle to incorporate multiple uncertainty types and allow for flexibility in constraint satisfaction.
Purpose of the Study:
- To develop an inexact fuzzy-stochastic constraint-softened programming method for robust MSW management.
- To enhance decision-making by expanding the model's decision space under uncertain conditions.
- To analyze trade-offs between economic objectives, satisfaction degrees, and constraint-violation risks.
Main Methods:
- Developed an inexact fuzzy-stochastic programming approach incorporating constraint softening.
- Introduced violation variables to allow for flexible constraint satisfaction levels.
- Applied a multi-layer scenario tree to incorporate dynamic and uncertain information for multistage decision-making.
Main Results:
- Generated a range of decision alternatives by accommodating various uncertainty levels and constraint-violation risks.
- Demonstrated the method's applicability in a case study for planning an MSW management system.
- Provided solutions linked to different satisfaction degrees and corresponding constraint-violation risks.
Conclusions:
- The developed method offers a robust framework for MSW management under complex uncertainties.
- It supports informed decisions regarding waste flow allocation and system capacity expansion in dynamic environments.
- The approach facilitates a comprehensive analysis of economic factors, satisfaction levels, and risk management.
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