Rapid urbanization, changing consumption patterns, and operational constraints are increasing the complexity of municipal solid waste management. This paper proposes an end-to-end artificial-intelligence framework that links three decisions that are commonly addressed in isolation: short- and medium-term waste generation forecasting, automated material segregation, and dynamic collection route optimization. The conceptual architecture combines heterogeneous urban data, Internet-of-Things bin telemetry, weather and calendar variables, computer vision at transfer or material-recovery facilities, and a capacitated vehicle-routing engine. Long short-term memory and gradient-boosting models are proposed for temporal demand forecasting; a lightweight object detector and classifier are proposed for recognizing recyclable, organic, hazardous, and residual fractions; and a forecast-aware capacitated vehicle routing formulation is proposed for fleet scheduling. A shared data and governance layer enable uncertainty propagation, human override, drift monitoring, and feedback-based retraining. Because this is a conceptual framework paper, no unverified performance claims are reported. Instead, the paper defines testable hypotheses, mathematical objectives, public benchmark options, operational metrics, and a staged deployment protocol. The framework is intended to support municipalities in reducing overflow, unnecessary trips, sorting contamination, fuel use, and service inequality while preserving transparency, worker safety, and institutional accountability.
artificial intelligence, municipal solid waste, waste forecasting, automated segregation, computer vision, smart bins, vehicle routing, smart city, circular economy.
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