Efficient Model for Waste Load and Route Optimization

Abstract

Urbanization frequently gives rise to substantial environmental issues, namely in waste management and water quality maintenance. Gross Pollutant Traps (GPTs) are essential in urban stormwater management as they effectively capture substantial pollutants before they enter the central water bodies. Nevertheless, the irregular buildup of trash caused by fluctuating rainfall intensity hinders the effective transfer of garbage from GPTs to their ultimate disposal locations. This research presents a holistic approach to enhancing the efficiency of waste transportation by improving route and load planning. The model utilizes machine learning techniques to forecast the quantity of waste collected by GPTs. We have created an optimization algorithm that uses the forecast outcome from a prior research dataset. This algorithm is designed to efficiently plan the routes and loads for trucks responsible for transporting waste to its final disposal location. The optimization process considered the estimated amounts of garbage, the capacities of the vehicles, and the locations of the disposal sites to reduce transportation expenses and save time. The system adaptively optimized routes using real-time data on the vehicle's origin and destination, ensuring effective allocation of resources and prompt garbage removal. Installing this approach resulted in a substantial decrease in transportation expenses and enhanced compliance with waste pickup timetables. The integration of predictive modeling and route optimization is enhancing urban trash management. Accurate garbage quantity forecasts and optimized transportation logistics can enable municipalities to deploy resources more effectively, decrease operational costs, and improve environmental protection. We chose a subset of 7 days, equivalent to one week, from the projected dataset for our experiment. Subsequently, we conducted numerous trials involving various waste disposal frequencies. The findings suggest that waste disposal every four (4) days is the most advantageous approach. Still, it performs similarly to waste disposal every three (3) days and has negligible environmental consequences. Hence, we select to execute the optimal solution for three (3) days, as it provides exceptional performance when considering the influence of natural pollution.