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

Authors

  • Anita Susanti Department of Transportation Engineering, Faculty of Vocational Studies, Universitas Negeri Surabaya, Indonesia
  • Belal Batiha Department of Mathematics. Faculty of Science and Information Technology, Jadara University, Irbid, Jordan
  • Tareq Hamadneh Department of Mathematics, Al Zaytoonah University of Jordan, Amman, Jordan
  • Gharib Mousa Gharib Department of Mathematics, Faculty of Science, Zarqa University, Jordan
  • Widi Aribowo Department of Electrical Engineering, Faculty of Vocational Studies, Universitas Negeri Surabaya, Indonesia
  • Haider Ali Department of Cybersecurity and Cloud computing, Technical Engineering, Uruk University, Baghdad, Iraq
  • Riyadh Kareem Jawad Department of Medical Instrumentations Techniques Engineering, Al-Rasheed University College, Baghdad, Iraq
  • Mahmood Anees Ahmed Medical Instrumentation Techniques Engineering Department, College of Medical Techniques, Al-Farahidi University, Baghdad, Iraq
  • Ibraheem Kasim Ibraheem Department of Electrical Engineering, College of Engineering, University of Baghdad, Iraq
  • Zeinab Monrazeri Department of Electrical and Electronics Engineering, Shiraz University of Technology, Iran
  • Mohammad Dehghani Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
Volume: 15 | Issue: 3 | Pages: 22915-22922 | June 2025 | https://doi.org/10.48084/etasr.10545

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.

Keywords:

metaheuristic optimization, Vehicle Routing Problem (VRP), Orangutan Optimization Algorithm (OOA), sustainable transportation systems, adaptive exploration and exploitation

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How to Cite

[1]
Susanti, A., Batiha, B., Hamadneh, T., Gharib, G.M., Aribowo, W., Ali, H., Jawad, R.K., Ahmed, M.A., Ibraheem, I.K., Monrazeri, Z. and Dehghani, M. 2025. Application of the Orangutan Optimization Algorithm for Solving Vehicle Routing Problems in Sustainable Transportation Systems. Engineering, Technology & Applied Science Research. 15, 3 (Jun. 2025), 22915–22922. DOI:https://doi.org/10.48084/etasr.10545.

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