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Radial-interval linear programming for environmental management under varied protection levels.
Qian Tan1, Guo H Huang, Yanpeng Cai
1Environmental Systems Engineering Program, Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, Canada.
Journal of the Air & Waste Management Association (1995)
|September 25, 2010
Summary
This study introduces radial-interval linear programming (RILP) for robust waste management under uncertainty. RILP enhances decision-making by quantifying risks and benefits, improving upon existing methods for environmental management.
Area of Science:
- Environmental Science
- Operations Research
- Mathematical Optimization
Background:
- Waste management decisions are often complicated by significant uncertainty in input parameters.
- Existing interval-parameter linear programming methods have limitations in handling input reasonableness and output robustness.
Purpose of the Study:
- To develop a novel approach, radial-interval linear programming (RILP), to support waste management under uncertainty.
- To improve the modeling of uncertain information and the robustness of solutions in waste management optimization.
Main Methods:
- Introduced the concept of fluctuation radius to model highly uncertain interval parameters.
- Developed RILP to control the conservatism of interval solutions and quantify system risks and benefits.
- Provided a computationally tractable algorithm for solving RILP problems.
Main Results:
- Demonstrated RILP's applicability using a long-term waste management case study.
- Obtained interval solutions under varied protection levels, revealing trade-offs between protection, risk, and cost.
- Sensitivity analysis highlighted the impact of fluctuation radii on system outcomes.
Conclusions:
- RILP effectively supports waste management by reflecting the interactive relationship between system feasibility and parameter uncertainty.
- The methodology offers valuable insights for generating and screening waste allocation alternatives based on decision-maker preferences.
- RILP is broadly applicable to environmental management problems with compound uncertainties.
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