Multi-objective vehicle routing problem with time windows via genetic algorithm - Ketaren, Dahlia Rizky; Parapat Gultom; Mahyuddin K.M. Nasution (Universitas Islam Negeri Raden Intan Lampung, 2025)
Efficient waste transportation requires route planning that considers time constraints, vehicle capacity, and road conditions. This study develops a Multi-Objective Vehicle Routing Problem with Time Windows model to optimize waste collection routes in Batu Bara Regency. The model simultaneously optimizes three objective functions: minimizing total travel distance, travel time, and risk based on road conditions. The solution is obtained using a Genetic Algorithm, with field data serving as model input. Simulation results show that the proposed model produces more efficient and realistic routes compared to conventional methods. The model effectively accommodates vehicle capacity constraints and customer service time windows. With the Genetic Algorithm, the solutions are not only operationally effective but also adaptable to the complex road network in the study area. These results can inform local government and waste management agencies in developing more adaptive and data-driven routing strategies, leading to cost savings, improved service efficiency, and reduced environmental impact. Moreover, the model can be extended or customized for other public logistics problems, such as food distribution or emergency response in semi-urban areas with similar infrastructure constraints. Future research can enhance this model by incorporating dynamic traffic data, multi-depot scenarios, or integrating sustainability metrics such as fuel consumption and emissions.