Back Propagation Neural Network- Based Route Scheduling Decision for Garbage Collection

The concerns in safe and effective collection and transportation move to the forefront of those issues in managing Smart Waste Management. Fuel economy influences the distance travelled, road quality, and speed, which depends on road conditions. Prioritization is based on the service time at collection points and the remaining waste in the bin. A Backpropagation Neural Network (BNN) and Modified Distance Based Evaluation (MDE) scheduling algorithms were proposed in this proposition to solve the problem. This solution incorporates a rolling- horizon approach and explores worstcase performance scenarios. Computational experiments were conducted using both small and large instances. The framework considers vehicle types, capacities, fuel consumption, and the constraints of multiple disposal facilities with finite capacity. These factors include the garbage load, distance, and time needed to pick up garbage overlapping other trucks on the same route.