Solving the waste bin location problem with uncertain waste generation rate: A bi-objective robust optimization...

Area of Science:

• Operations Research
• Environmental Engineering
• Urban Planning

Background:

• Efficient municipal solid waste (MSW) systems are vital for urban sustainability and liveability.
• Planning MSW systems faces challenges due to uncertainties in key parameters like waste generation rates.
• The initial contact points in an MSW system, collection points, require careful network design.

Purpose of the Study:

• To develop a robust optimization model for designing municipal solid waste (MSW) collection point networks.
• To simultaneously minimize network costs and waste collection frequency.
• To address uncertainties in waste generation rates for improved MSW system planning.

Main Methods:

• A robust optimization model was developed for MSW collection point network design.
• The model incorporates a bi-objective function to balance network costs and collection frequency.
• Solutions were compared against a deterministic model using realistic scenarios with varying waste generation rates.

Main Results:

• The robust optimization model provides competitive solutions compared to deterministic approaches across various scenarios.
• The model successfully minimizes both collection point network costs and required collection frequency.
• The developed model allows for exploration of trade-offs between cost minimization and collection frequency.

Conclusions:

• Robust optimization is effective for designing MSW collection networks under uncertainty.
• The model enhances the planning of sustainable and liveable urban environments.
• Decision-makers can utilize this model to optimize MSW collection infrastructure and operations.