Mathematical Optimization of Waste Reduction and Recycling Programs

Abstract

In this paper we provide a broad overview of mathematical optimization methods applied to waste reduction and recycling initiatives. The progression from simple linear programming models to more complex stochastic and dynamic programming methods has greatly increased the efficiency and sustainability of waste management systems. What kind of technologies are redefining waste management practices: artificial intelligence, Internet of Things (IOT), machine learning algorithms made accessible in use cases like real-time waste monitoring, predictive modeling, and automated sorting? This integration helps transition cities to circular systems away from linear waste models supporting sustainable urban development and resource efficiency. We review different types of optimization approaches, their mathematical formulations, and practical applications for a range of waste management systems. We offer empirical support for these performance gains and conclude with future work on how to create truly adaptive, intelligent waste management systems.