Route Optimizer: Best Tool for Waste Disposal Management

Abstract

Efficient waste disposal management is a critical challenge for modern urban systems, requiring solutions that minimize operational costs, reduce environmental impact, and adapt to dynamic traffic conditions. This study proposes a Route Optimizer framework using a machine learning–enhanced reinforcement learning (RL) routing model integrated with classical graph algorithms such as Dijkstra and A*. The methodology involves constructing a weighted graph of urban road networks, real time data from IoT bin sensors, preprocessing geospatial such as GIS mapping, and traffic-aware routing which enables dynamic scheduling and on demand pickups, reducing unnecessary stops, fuel consumption and then having an RL agent that will undergo training on learning optimal routing policies through iterative prototyping and simulation. The optimization algorithm combines real-time traffic predictions (via supervised ML models) with RL-based decision-making, in order to adapt to congestion, vehicle capacity, and time-window constraints.