Hybrid Optimization for Secure and Timely Medical Logistics: A Case Study in Burkina Faso
DOI:
https://doi.org/10.7494/dmms.2025.19.7507Keywords:
Clarke–Wright savings, Ant Colony Optimization, OR-Tools, Vehicle Routing Problem, medical logistics, risk-adjusted distance, Burkina FasoAbstract
Transportation logistics plays a critical role in ensuring equitable access to medical supplies in low-infrastructure and insecure environments. This paper proposes a three-stage hybrid framework to solve a Vehicle Routing Problem with Safety and Time Windows (VRP-ST) in high-risk contexts. The methodology combines a Clarke–Wright savings heuristic for rapid initialization, an Ant Colony Optimization (ACO) metaheuristic to minimize a risk-adjusted distance objective, and Google OR-Tools to strictly enforce hard constraints, including vehicle capacities and delivery time windows. The approach is evaluated on a nationwide case study involving 44 healthcare centers in Burkina Faso. The results show a 19% reduction in total risk-adjusted distance (from 10,425 to 8,280 units) relative to the initial heuristic solution, together with improved fleet utilization. Beyond this case study, the framework provides a robust and fully feasible decision-support tool for medical logistics planning in crisis-affected regions. A sensitivity analysis under ±10% demand and risk-penalty perturbations confirms the stability of the solutions.
References
Alves F., Pacheco F., Rocha A.M.A.C., Pereira A.I. & Leitão P. (2021). Solving a logistics system for vehicle routing problem using an open-source tool. In: Gervasi O., Murgante B., Garau Ch., Misra S., Garau Ch., Blečić I., Taniar D., Apduhan B.O., Rocha A.M.A., Tarantino E. & Torre C.M. (Eds.), Computational Science and Its Applications – ICCSA 2021. Part V. Cham: Springer, pp. 397–412.
Bigdeli H., Mirdamadi S.M.S., Deaconu A. & Tayyebi J. (2023). Robust Vehicle Routing Problem in the presence of a proactive attacker. arXiv preprint, arXiv:2310.05254. DOI: https://doi.org/10.48550/arXiv.2310.05254.
Borowski M., Gora P., Karnas K., Błajda M., Król K., Matyjasek A., Burczyk D., Szewczyk M. & Kutwin M. (2020). New hybrid quantum annealing algorithms for solving vehicle routing problem. In: Krzhizhanovskaya V.V., Závodszky G., Lees M.H., Dongarra J.J., Sloot P.M.A., Brissos S. & Teixeira J. (Eds.), Computational Science – ICCS 2020. Part VI. Cham: Springer, pp. 546–561. DOI: https://doi.org/10.1007/978-3-030-50433-5_42.
Bouraima M., Badi I., Stević Ž., Kiptum C.K., Pamucar D., & Marinkovic D. (2024). A novel vehicle routing algorithm for route optimization using best-worst method and ranking alternatives for similarity to ideal solution. Engineering Review, 44(4), pp. 57–76. DOI: https://doi.org/10.30765/er.2597.
Cipta H. & Hasibuan M.F. (2023). Optimal Vehicle Routing Problem (VRP) for the distribution of medical devices by applying the Clarke–Wright algorithm. International Journal of Science and Environment, 3(1), pp. 7–12. DOI: https://doi.org/10.51601/ijse.v3i1.56.
Clarke G. & Wright J.W. (1964). Scheduling of vehicles from a central depot to a number of delivery points. Operations Research, 12(4), pp. 568–581. DOI: https://doi.org/10.1287/opre.12.4.568.
Diao X. (2024). Route optimization for hazardous materials transportation vehicles based on BFA-ACO under carbon tax policy. Journal of Computational Methods in Sciences and Engineering, 24(3), pp. 1943–1954. DOI: https://doi.org/10.3233/JCM-230015.
Directorate General of Studies and Sectoral Statistics/Ministry of Health of Burkina Faso (2024). Data on Healthcare Centers in Burkina Faso [25.09.2024].
Fröhlich G.E.A., Gansterer M. & Doerner K.F. (2023). Safe and secure vehicle routing: A survey on minimization of risk exposure. International Transactions in Operational Research, 30(6), pp. 3087–3121. DOI: https://doi.org/10.1111/itor.13130.
Laporte G. (2009). Fifty years of vehicle routing. Transportation Science, 43(4), pp. 408–416. DOI: https://doi.org/10.1287/trsc.1090.0301.
Li B., Wu G., He Y., Fan M. & Pedrycz W. (2022). An overview and experimental study of learning-based optimization algorithms for vehicle routing problem. arXiv preprint, arXiv:2107.07076. DOI: https://doi.org/10.48550/arXiv.2107.07076.
Liu F., Lu C., Gui L., Zhang Q., Tong X. & Yuan M. (2023). Heuristics for vehicle routing problem: A survey and recent advances. arXiv preprint, arXiv:2303.04147. DOI: https://doi.org/10.48550/arXiv.2303.04147.
Nunna T. (2017). Performance Analysis on Hybrid and Exact Methods for Solving Clustered VRP: A Comparative Study on VRP Algorithms [Master’s thesis], Blekinge Institute of Technology. URL: https://www.diva-portal.org/smash/get/diva2:1088923/FULLTEXT02.pdf [18.08.2025].
Omidvar A., Ozguven E.E., Vanli O.A. & Tavakkoli-Moghaddam R. (2017). A two-phase safe vehicle routing and scheduling problem: formulations and solution algorithms. arXiv preprint, arXiv:1710.07147. DOI: https://doi.org/10.48550/arXiv.1710.07147.
Prayoga V.C. & Mardiana N. (2023). Implementasi nearest neighbour dan tabu search dalam optimasi rute pendistribusian produk. Prosiding Seminar Sosial Politik, Bisnis, Akuntansi dan Teknik, 5, p. 212. DOI: https://doi.org/10.32897/sobat.2023.5.0.3099.
Salsabila A.S. (2023). Optimization of vehicle routing problem using guided local search and simulated annealing. Jurnal Terapan Teknik Industri, 4(2), pp. 155–166. DOI: https://doi.org/10.37373/jenius.v4i2.582.
Stević Ž. & Gavranović M. (2024). Choosing the optimal route for a delivery vehicle in X Express company using Clarke and Wright algorithm. In: Kautish S., Chatterjee P., Pamucar D., Pradeep N. & Singh D. (Eds.), Computational Intelligence for Modern Business Systems: Emerging Applications and Strategies. Singapore: Springer Nature, pp. 407–418. DOI: https://doi.org/10.1007/978-981-99-5354-7_21.
Toth P. & Vigo D. (2014). Vehicle Routing: Problems, Methods, and Applications. Second Edition. Philadelphia: Society for Industrial and Applied Mathematics.
Vidal T. (2020). Hybrid genetic search for the CVRP: Open-source implementation and perspectives. Computers & Operations Research, 124, 105051. DOI: https://doi.org/10.1016/j.cor.2020.105051.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Decision Making in Manufacturing and Services
This work is licensed under a Creative Commons Attribution 4.0 International License.
