Population growth and rapid urbanization in African cities are leading to a significant increase in the production of municipal solid waste. This trend exacerbates the challenges associated with waste collection, transport, and management, particularly in cities where material and financial resources remain limited. In Matadi, the main seaport of the Democratic Republic of Congo, collection operations are still largely based on fixed routes that do not take into account the actual level of fill in the bins, which leads to unnecessary trips, an increase in operating costs and a decrease in the quality of service. This study proposes a computer model of an intelligent waste collection system based on the Vehicle Routing Problem (VRP) and the Unified Modeling Language (UML). The approach integrates Internet of Things (IoT) technologies, GPS geolocation, Google Maps and the Ant Colony Optimization (ACO) algorithm to improve collection route planning. The methodology includes functional analysis of the system, UML modeling (use case, class, sequence, activity, and deployment diagrams), and formalization of the VRP problem. This research provides a complete software architecture that can serve as a basis for developing an intelligent waste management system adapted to the context of African cities.
UML; Vehicle Routing Problem ; Internet of Things; Google Maps ; Ant Colony Optimization ; Smart Waste Management; Matadi.
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