Designing a sustainable municipal solid waste management system

Municipal solid waste (MSW) management presents a formidable challenge in the face of rapid urbanization, escalating waste volumes, and the growing urgency for sustainable development. This thesis introduces a Python-based decision-support framework designed to optimize MSW management by balancing economic costs with environmental impacts, specifically greenhouse gas emissions. The framework integrates three multi-objective optimization techniques-the Weighted Sum Method, the Epsilon-Constraint Method, and Goal Programming-within a unified mixedinteger linear programming (MILP) model. Each approach is implemented and compared using a consistent dataset and visualized through a user-friendly Tkinter graphical interface. Through systematic analysis, the study highlights the trade-offs inherent in each method: while the Weighted Sum Method offers computational simplicity, the Epsilon-Constraint Method provides a detailed trade-off curve, and Goal Programming ensures realistic target setting with immediate feasibility feedback. The findings not only demonstrate the potential for significant cost savings and emission reductions but also offer practical insights for municipal decision-makers tasked with sustainable waste management. This research lays the groundwork for further advancements in integrating multi-objective optimization techniques into sustainable urban waste management practices.