A mixed integer linear programming model for long-term planning of municipal solid waste management systems: Against...

Area of Science:

• Environmental Engineering
• Operations Research
• Sustainable Waste Management

Background:

• Long-term municipal solid waste management planning is complex, involving numerous decision layers.
• Existing models struggle with mass balance programming due to varying waste component responses to different treatment processes.
• Previous mixed integer linear programming models often restricted possibilities in mass balances to address these complexities.

Purpose of the Study:

• To develop a novel mixed integer linear programming model for comprehensive municipal solid waste management planning.
• To overcome the mass balance obstacle in modeling complex waste management systems.
• To integrate waste collection, process selection, allocation, transportation, location, and capacity assessment within a single framework.

Main Methods:

• Formulation of a novel mixed integer linear programming model named ALOMWASTE.
• Development of a model structure capable of considering diverse process, capacity, and location possibilities simultaneously.
• Application and validation of the model through a case study.

Main Results:

• The ALOMWASTE model successfully addresses the mass balance obstacle, allowing for unrestricted calculations.
• Demonstrated feasibility for simultaneous solution of all essential decision layers in waste management.
• The model facilitates multi-objective optimization of financial-environmental-social costs and aids in uncertainty analysis for tools like life cycle assessment.

Conclusions:

• The ALOMWASTE model offers a significant advancement in long-term municipal solid waste management planning.
• It provides a unified approach to address complex decision-making layers without compromising mass balance accuracy.
• The model's capabilities extend to multi-objective optimization and uncertainty analysis, enhancing decision-making tools.