Multi-Capacitated Waste Collection Routing Problem with Container Fill Levels

Efficient and sustainable waste collection is a critical challenge in modern urban management. This study proposes a new Mixed Integer Linear Programming (MILP) model to address the Waste Collection Routing Problem by incorporating both the weight and volume capacities of vehicles along with container fill levels. The model optimizes vehicle routes by dynamically excluding containers that do not meet a predefined occupancy threshold, thereby reducing unnecessary trips and operational costs. The proposed model is implemented using the GUROBI solver in Python and tested on multiple hypothetical scenarios with varying customer and vehicle configurations. Results show that the total collection cost (measured as travel distance) increased from 285 units for 10 containers with 2 vehicles to 511 units for 30 containers with 4 vehicles, revealing a clear relationship between container quantity and route complexity. These findings demonstrate the model’s effectiveness in minimizing total distance while adapting to real-time container conditions, contributing to smart and responsive waste management strategies.