An Integrated AHP–PROMETHEE II Framework for Refrigerated Transport Service Provider Selection
DOI:
10.33395/sinkron.v10i4.16652Abstract
Refrigerated transport is essential for preserving the quality of temperature-sensitive products and maintaining continuity across cold chain logistics. Selecting a refrigerated transport service provider is therefore a multidimensional decision involving technical reliability, operational capability, commercial conditions, sustainability, and cost. This study develops an integrated framework combining the Analytic Hierarchy Process (AHP) and the Preference Ranking Organization Method for Enrichment Evaluations II (PROMETHEE II) to evaluate six providers in Ho Chi Minh City, Vietnam. Assessments from nine experts were used to determine the weights of four criteria groups and fifteen subcriteria through AHP, while PROMETHEE II was applied to establish the provider ranking. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and group-weight sensitivity analysis were used to examine the robustness of the results. Service Quality and Cost were the most influential criteria groups, with weights of 0.3981 and 0.2802, respectively. Service cost, temperature compliance, and quotation transparency received the highest subcriterion weights. Provider S2 ranked first under PROMETHEE II, with a net outranking flow of 0.3144, and retained this position under TOPSIS and across most variations in group weights. The proposed framework enables firms to compare providers using a consistent set of criteria, identify the preferred alternative, and assess whether the decision remains stable under different ranking methods and weighting assumptions.
Downloads
References
Ali, H., Liu, M., & Shoaib, M. (2025). Investigating the sustainable-resilient pharmaceutical cold chain logistics service provider selection using a novel scenario-based trapezoidal fuzzy hybrid approach. International Journal of Fuzzy Systems. https://doi.org/10.1007/s40815-025-02130-w.
Arief, I., Fatrias, D., Jie, F., & Armijal. (2023). Innovative multi-criteria decision-making approach for supplier evaluation: Combining TLF, fuzzy BWM, and VIKOR. Jurnal Optimasi Sistem Industri, 22(2), 179–196. https://doi.org/10.25077/josi.v22.n2.p179-196.2023.
Ben Taher, M. A., Ahachad, M., Mahdaoui, M., Zeraouli, Y., & Kousksou, T. (2022). A survey of computational and experimental studies on refrigerated trucks. Journal of Energy Storage, 47, 103575. https://doi.org/10.1016/j.est.2021.103575.
Brans, J. P., Vincke, Ph., & Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European Journal of Operational Research, 24(2), 228–238. https://doi.org/10.1016/0377-2217(86)90044-5.
Calati, M., Hooman, K., & Mancin, S. (2022). Thermal storage based on phase change materials (PCMs) for refrigerated transport and distribution applications along the cold chain: A review. International Journal of Thermofluids, 16, 100224. https://doi.org/10.1016/j.ijft.2022.100224.
Chen, Q., Qian, J., Yang, H., & Wu, W. (2022). Sustainable food cold chain logistics: From microenvironmental monitoring to global impact. Comprehensive Reviews in Food Science and Food Safety, 21(5), 4189–4209. https://doi.org/10.1111/1541-4337.13014.
Cil, A. Y., Abdurahman, D., & Cil, I. (2022). Internet of Things enabled real time cold chain monitoring in a container port. Journal of Shipping and Trade, 7(9). https://doi.org/10.1186/s41072-022-00110-z.
Cui, X., Qi, B., & Hussain, M. J. (2024). Vendor sustainability performance and corporate customers’ supplier selection. Corporate Social Responsibility and Environmental Management, 31(4), 2910–2928. https://doi.org/10.1002/csr.2727.
du Plessis, M. J., Van Eeden, J., Goedhals-Gerber, L. L., & Else, J. (2023). Calculating fuel usage and emissions for refrigerated road transport using real-world data. Transportation Research Part D: Transport and Environment, 117, 103623. https://doi.org/10.1016/j.trd.2023.103623.
Ecer, F., Haseli, G., Krishankumar, R., & Hajiaghaei-Keshteli, M. (2024). Evaluation of sustainable cold chain suppliers using a combined multi-criteria group decision-making framework under fuzzy ZE-numbers. Expert Systems with Applications, 245, 123063. https://doi.org/10.1016/j.eswa.2023.123063.
Fabris, F., Artuso, P., Marinetti, S., Minetto, S., & Rossetti, A. (2022). Cooling unit impact on energy and emissions of a refrigerated light truck. Applied Thermal Engineering, 216, 119132. https://doi.org/10.1016/j.applthermaleng.2022.119132.
Forman, E., & Peniwati, K. (1998). Aggregating individual judgments and priorities with the analytic hierarchy process. European Journal of Operational Research, 108(1), 165–169. https://doi.org/10.1016/S0377-2217(97)00244-0.
Han, J.-W., Zuo, M., Zhu, W.-Y., Zuo, J.-H., Lü, E.-L., & Yang, X.-T. (2021). A comprehensive review of cold chain logistics for fresh agricultural products: Current status, challenges, and future trends. Trends in Food Science & Technology, 109, 536–551. https://doi.org/10.1016/j.tifs.2021.01.066.
Hayati, E. N., Jauhari, W. A., Damayanti, R. W., Rosyidi, C. N., & Fauadi, M. H. F. B. M. (2025). A framework for sustainable supplier selection integrating grey forecasting and F-MCDM methods: A case study. Jurnal Optimasi Sistem Industri, 24(1), 63–83. https://doi.org/10.25077/josi.v24.n1.p63-83.2025.
Hendiani, S., & Walther, G. (2025). Towards sustainable futures: Rethinking supplier selection through interval-valued intuitionistic fuzzy decision-making. International Journal of Production Economics, 285, 109620. https://doi.org/10.1016/j.ijpe.2025.109620.
Hwang, C.-L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications: A state-of-the-art survey. Berlin, Heidelberg: Springer-Verlag. doi:10.1007/978-3-642-48318-9.
Konovalenko, I., Ludwig, A., & Leopold, H. (2021). Real-time temperature prediction in a cold supply chain based on Newton’s law of cooling. Decision Support Systems, 141, 113451. https://doi.org/10.1016/j.dss.2020.113451.
Krstić, M., & Tadić, S. (2023). Hybrid multi-criteria decision-making model for optimal selection of cold chain logistics service providers. Journal of Organizations, Technology and Entrepreneurship, 1(2), 77–87. https://doi.org/10.56578/jote010201.
National Assembly of Vietnam. (2025). Nghị quyết số 202/2025/QH15 về việc sắp xếp đơn vị hành chính cấp tỉnh [Resolution No. 202/2025/QH15 on the reorganization of provincial-level administrative divisions]. https://vanban.chinhphu.vn/?pageid=27160&docid=213930.
Rahman, M. H., Rahman, M. F., & Tseng, T.-L. (2023). Estimation of fuel consumption and selection of the most carbon-efficient route for cold-chain logistics. International Journal of Systems Science: Operations & Logistics, 10(1), 2075043. https://doi.org/10.1080/23302674.2022.2075043.
Raveena, R., & Umamaheswari, S. (2025). Integrated MCDM framework for sustainable pharmacy supplier selection using pioneering criteria with fuzzy TOPSIS, SVR and GRA. Scientific Reports, 15, 19144. https://doi.org/10.1038/s41598-025-02975-z.
Saaty, T. L. (1987). The analytic hierarchy process—What it is and how it is used. Mathematical Modelling, 9(3–5), 161–176. https://doi.org/10.1016/0270-0255(87)90473-8.
Siregar, A. R., & Hendry, H. (2025). Optimizing supplier selection through hybrid BWM and AHP integration. Sinkron: Jurnal dan Penelitian Teknik Informatika, 9(4), 1978–1987. https://doi.org/10.33395/sinkron.v9i4.15261.
Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15(1), 72–101. https://doi.org/10.2307/1412159.
Sun, W., Gu, X., & Wu, D. (2022). Research on the selection of green cold chain logistics service providers based on combined weighting-cloud model. Procedia Computer Science, 214, 1409–1416. https://doi.org/10.1016/j.procs.2022.11.324.
Tavana, M., Sorooshian, S., & Mina, H. (2024). An integrated group fuzzy inference and best–worst method for supplier selection in intelligent circular supply chains. Annals of Operations Research, 342, 803–844. https://doi.org/10.1007/s10479-023-05680-0.
Thi, T. H. H., Tang, M. H., & Nguyen, Q. L. (2022). Cold chain and food loss in the Vietnamese food chain. Transportation Research Procedia, 64, 44–52. https://doi.org/10.1016/j.trpro.2022.09.006.
Tu, Y., Zhang, Z., Nie, L., & Xu, Y. (2022). Evaluation of cold chain logistics suppliers based on AHPSort II with trapezoidal fuzzy sets. In J. Xu, F. Altiparmak, M. H. A. Hassan, F. P. García Márquez, & A. Hajiyev (Eds.), Proceedings of the Sixteenth International Conference on Management Science and Engineering Management—Volume 1 (pp. 727–739). Cham, Switzerland: Springer. doi:10.1007/978-3-031-10388-9_53.
Więckowski, J., & Sałabun, W. (2023). Sensitivity analysis approaches in multi-criteria decision analysis: A systematic review. Applied Soft Computing, 148, 110915. https://doi.org/10.1016/j.asoc.2023.110915.
Wu, J., Liu, G., Marson, A., Fedele, A., Scipioni, A., & Manzardo, A. (2022). Mitigating environmental burden of the refrigerated transportation sector: Carbon footprint comparisons of commonly used refrigeration systems and alternative cold storage systems. Journal of Cleaner Production, 372, 133514. https://doi.org/10.1016/j.jclepro.2022.133514.
Zhang, N., Zhou, Y., Zhou, Q., & Wei, G. (2023). An approach of selecting cold chain logistics service provider based on SNA and FCE method. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 44(2), 1893–1905. https://doi.org/10.3233/JIFS-220780.
How to Cite
Issue
Section
License
Copyright (c) 2026 Minh Nhat Nguyen

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.






















Moraref
PKP Index
Indonesia OneSearch
OCLC Worldcat
Index Copernicus
Scilit
