Application of the Orangutan Optimization Algorithm for Solving Vehicle Routing Problems in Sustainable Transportation Systems

Abstract

Metaheuristic optimization algorithms are powerful tools for solving complex transportation problems. This study investigates the application of the Orangutan Optimization Algorithm (OOA) to a Vehicle Routing Problem (VRP), aiming to minimize travel distances while adhering to time constraints and vehicle capacity limits. Compared to 12 state-of-the-art algorithms, OOA demonstrated superior performance in convergence speed, solution quality, computational efficiency, and robustness. Its dynamic balance between exploration and exploitation allows it to consistently outperform other methods, achieving the best solutions in the least computational time. The study highlights the effectiveness of OOA in solving real-world transportation optimization challenges and sets the stage for future research into hybrid algorithms and integration with emerging technologies such as machine learning and IoT to further advance transportation systems.