Mathematical optimization of waste management systems: Methodological review and perspectives for application.

The transition to a circular economy through sustainable waste management (WM) follows different paths in each region, depending on its socioeconomic conditions and existing infrastructure. Mathematical optimization models are rigorous tools for informing local decision-making and identifying WM policy levers based on a variety of configurations. This review explores the pathways taken when designing WM optimization models (WM-OMs) that establish a network of waste valorization technologies.

A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems.

Sustainability challenges, such as solid waste management, are usually scientifically complex and data scarce, which makes them not amenable to science-based analytical forms or data-intensive learning paradigms. Deep integration between data science and sustainability science in highly complementary manners offers new opportunities for tackling these conundrums. This study develops a novel hybrid neural network (HNN) model that imposes the holistic decision-making context of solid waste management systems (SWMS) on a traditional neural network (NN) architecture.