Minimax regret analysis for municipal solid waste management: an interval-stochastic programming approach.

Area of Science:

• Environmental Engineering
• Operations Research
• Decision Science

Background:

• Municipal solid waste (MSW) management planning faces significant uncertainty.
• Existing methods like interval programming and minimax regret analysis have limitations in handling diverse uncertainty types.
• Robust decision-making frameworks are needed for effective MSW management.

Purpose of the Study:

• To develop and present a novel Interval Minimax Regret Programming (IMMRP) method.
• To integrate interval and random variable uncertainties into a unified optimization framework for MSW management.
• To provide a tool for analyzing economic consequences under uncertainty without probability assumptions.

Main Methods:

• Development of the Interval Minimax Regret Programming (IMMRP) model.
• Application of IMMRP to a long-term MSW management planning case study.
• Analysis of multiple scenarios considering different cost and risk levels.

Main Results:

• The IMMRP method successfully generated reasonable solutions for MSW management planning under uncertainty.
• Demonstrated complex tradeoffs between system cost, regret, and system failure risk.
• Facilitated the examination of cost differences between chosen strategies and ideal conditions.

Conclusions:

• The IMMRP method offers a robust approach for MSW management planning under uncertainty.
• The approach aids in understanding and managing economic consequences and risks.
• Results support informed decision-making for waste management policies.