International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Solid waste management remains a critical challenge in African cities, where rapid urbanization, poor infrastructure, and limited resources often lead to inefficiencies and environmental hazards. In Enugu State, Nigeria, waste collection is managed by the Enugu State Waste Management Authority (ESWAMA) through a network of bin drop centers serviced by trucks from central depots. The current practice relies on static scheduling, which results in overflowing bins, excessive operational costs, and delays due to traffic congestion and long routing distances. This paper proposes a Model Predictive Control (MPC) framework for smart waste collection routing tailored to the operational context of Enugu State. The system models waste collection as a dynamic vehicle routing problem and leverages predictive control to forecast bin fill levels, adapt to real-time traffic conditions, and optimize routing decisions. Simulations conducted across urban, semi-urban, and rural scenarios demonstrate that the MPC-based approach reduces total distance traveled by up to 23%, decreases collection times by over 20%, and lowers overall operational costs and emissions compared with static scheduling. The results highlight the potential of MPC to transform municipal waste management into an adaptive, efficient, and sustainable system for African cities. Future work will integrate Internet of Things (IoT)-enabled smart bins and machine learning-based forecasting to further enhance prediction accuracy and scalability.

Downloads: | DOI: 10.17148/IJARCCE.2026.15201