Artificial Intelligence for Sustainable Logistics and Supply Chains: Trends and Future Directions in Transportation and Distribution
Article Sidebar
Main Article Content
Abstract
This research presents a systematic literature review on the application of artificial intelligence (AI) in sustainable supply chain management, with a specific focus on transportation and distribution. The objective is to examine the evolution, classification, and emerging trends of AI technologies and their contributions to supply chain sustainability. The findings reveal a clear transition from traditional optimization and metaheuristic approaches toward intelligent systems that integrate real-time data with machine learning, reinforcement learning, and digital twin technologies to enhance operational efficiency, reduce carbon emissions, and support autonomous decision-making. The results indicate that the majority of AI applications continue to rely on optimization and metaheuristics (67.5%), while advanced technologies such as machine learning, deep learning, reinforcement learning, and digital twins have seen increasing adoption since 2021. In terms of sustainability dimensions, economic aspects receive the greatest emphasis, followed by environmental and social dimensions. The integration of economic and environmental sustainability is the most prevalent, whereas limited attention to social impacts highlights a significant research gap. Future trends suggest that AI-enabled transportation systems will increasingly focus on automation, hybrid AI approaches, low-carbon planning, real-time responsiveness, and transparent and responsible AI governance.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Ansari, N., Fattahi, P., & Shiri, M. (2025). The role of drone technology and application of IoT on vaccine supply chain during a pandemic under uncertain environment: A real case study of COVID-19 in Iran. Transportation Research Part E: Logistics and Transportation Review, 193,103831.
Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163, 120420.
Carter, C. R., & Easton, P. L. (2011). Sustainable supply chain management: Evolution and future directions. International Journal of Physical Distribution & Logistics Management, 41(1), 46–62.
Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: Moving toward new theory. International Journal of Physical Distribution & Logistics Management, 38(5), 360–387. https://doi.org/10.1108/09600030810882816
Chen, W., Zhang, X., Li, Y., & Huang, Z. (2024). Artificial intelligence in logistics optimization with sustainable practices. Sustainability, 16(21), 9145. https://doi.org/10.3390/su16219145
Chen, W., Men, Y., Fuster, N., Osorio, C., & Juan, A. A. (2024). Artificial intelligence in logistics optimization with sustainable criteria: A review. Sustainability, 16(21), 9145.
Cai, Y.-J., & Choi, T.-M. (2020). A United Nations’ Sustainable Development Goals perspective for sustainable textile and apparel supply chain management. Transportation Research Part E: Logistics and Transportation Review, 141, 102010.
Choi, T.-M. (2020). Internet-based elastic logistics platforms for fashion quick response systems in the digital era. Transportation Research Part E: Logistics and Transportation Review, 143, 102096. https://doi.org/10.1016/j.tre.2020.102096
Chopra, S., & Meindl, P. (2019). Supply chain management: Strategy, planning, and operation (7th ed.). Pearson Education.
Christopher, M. (2016). Logistics & supply chain management (5th ed.). Pearson.
Deveci, M. (2023). Effective use of artificial intelligence in healthcare supply chain resilience using a fuzzy decision-making model. Soft Computing. https://doi.org/10.1007/s00500-023-08906-2
Deloitte. (2023). Digital transformation in supply chain. Deloitte. Retrieved from https://www.deloitte.com/ch/en/services/consulting/perspectives/digital-transformation-in-supply-chain.html
Elkington, J. (1997). Cannibals with forks: The triple bottom line of 21st century business. Oxford, England: Capstone.
Franceschetti, A., Demir, E., Honhon, D., Van Woensel, T., Laporte, G., & Stobbe, M. (2017). A metaheuristic for the time-dependent pollution-routing problem. European Journal of Operational Research, 259(3), 972–991.
Giuffrida, N., Fajardo-Calderin, J., Masegosa, A. D., Werner, F., Steudter, M., & Pilla, F. (2022). Optimization and machine learning applied to last-mile logistics: A review. Sustainability, 14(9), 5329.
Govindan, K., Cheng, T. C. E., Mishra, N., & Shukla, N. (2018). Big data analytics and application for logistics and supply chain management. Transportation Research Part E: Logistics and Transportation Review, 114, 343–349.
Govindan, K., Mina, H., & Alavi, B. (2020). A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transportation Research Part E: Logistics and Transportation Review, 138, 101967.
International Energy Agency. (2023).Transport sector CO₂ emissions. เข้าถึงได้จาก https://www.iea.org
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915.
Ivanov, D. (2023). The Industry 5.0 framework: Viability-based integration of the resilience, sustainability, and human-centricity perspectives. International Journal of Production Research, 61(5), 1683–1695.
Jahani, H., Chaleshtori, A. E., Khaksar, S. M. S., Aghaie, A., & Sheu, J.-B. (2022). COVID-19 vaccine distribution planning using a congested queuing system: A real case from Australia. Transportation Research Part E: Logistics and Transportation Review, 163, 102749.
Liu, Y., Tao, F., & Zhu, R. (2025). Vehicle-routing problem for low-carbon cold chain logistics based on the idea of cost–benefit. Complex & Intelligent Systems, 11, 157
Liu, D., Yan, P., Pu, Z., Wang, Y., & Kaisar, E. I. (2021). Hybrid artificial immune algorithm for optimizing a van-robot e-grocery delivery system. Transportation Research Part E: Logistics and Transportation Review, 154, 102466.
Laurent, A.-B., Vallerand, S., van der Meer, Y., & D'Amours, S. (2020). CarbonRoadMap: A multicriteria decision tool for multimodal transportation. International Journal of Sustainable Transportation, 14(3), 205-214
Maleki, A., Hemmati, V., Reza Abazari, S., Aghsami, A., & Rabbani, M. (2024). Optimal distribution and waste management of COVID-19 vaccines from vaccination centers’ satisfaction perspective: A fuzzy time window-based VRP. Transportation Research Part E: Logistics and Transportation Review, 183, 103454.
Maiyar, L. M., & Thakkar, J. J. (2019). Environmentally conscious logistics planning for food grain industry considering wastages employing multi-objective hybrid particle swarm optimization. Transportation Research Part E: Logistics and Transportation Review, 127, 220–248.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097.
Montreuil, B. (2011). The Physical Internet. In T. Borangiù, A. Thomas, & D. Trentesaux (Eds.), Service orientation in holonic and multi-agent manufacturing control (pp. 11–13). Springer.
Montreuil, B. (2011). Toward a Physical Internet: meeting the global logistics sustainability grand challenge. Logistics Research, 3(2–3), 71–87.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Qu, C., & Kim, E. (2024). Reviewing the roles of AI-integrated technologies in sustainable supply chain management: Research propositions and a framework for future directions. Sustainability, 16(14), 6186.
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Upper Saddle River, NJ: Pearson.
Rodríguez-Espíndola, O., Ahmadi, H., Gastélum-Chavira, D., Ahumada-Valenzuela, O., Chowdhury, S., Dey, P. K., & Albores, P. (2023). Humanitarian logistics optimization models: An investigation of decision-maker involvement and directions to promote implementation. Socio-Economic Planning Sciences, 89, 101669.
Smith, A. D., & Moreno, G. (2025). Omnichannel transportation, warehousing and manufacturing quality characteristics in a post-COVID environment: Case and empirical study. International Journal of Logistics Systems and Management, 51(1), 79–103. https://doi.org/10.1504/IJLSM.2025.146060
Tang, L., Li, Y., Zhang, S., Wang, Z., & Coelho, L. C. (2025). Mobile COVID-19 vaccination scheduling with capacity selection. Transportation Research Part E: Logistics and Transportation Review, 193, 103826.
Taj, S., Imran, A. S., Kastrati, Z., Daudpota, S. M., Memon, R. A., & Ahmed, J. (2023). IoT-based supply chain management: A systematic literature review. Internet of Things, 24, 100982.
Teixeira, A. R., Ferreira, J. V., & Ramos, A. L. (2025). Intelligent supply chain management: A systematic literature review on artificial intelligence contributions. Information, 16(5), 399.
United Nations Conference on Trade and Development: UNCTAD. (2023). Review of maritime transport 2023. United Nations. เข้าถึงได้จาก https://unctad.org/publication/review-maritime-transport-2023
United Nations Economic and Social Commission for Asia and the Pacific (ESCAP). (2024). Review of Developments in Transport in Asia and the Pacific: Transition Towards Sustainable Transport Solutions. United Nations. เข้าถึงได้จาก https://www.unescap.org/kp/2024/review-developments-transport-asia-and-pacific-2024-transition-towards-sustainable?
United Nations. (2025). The Sustainable Development Goals Report 2025. United Nations Publications. เข้าถึงได้จาก https://unstats.un.org/sdgs/report/2025/The-Sustainable-Development-Goals-Report-2025.pdf
United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP). (2024). The Review of Developments in Transport in Asia and the Pacific 2024: Transition Towards Sustainable Transport Solutions. United Nations. เข้าถึงได้จาก https://repository.unescap.org/items/4e8a167f-a79e-41f4-8a75-99767136a494
Yan, Y., Chow, A. H. F., Ho, C. P., Kuo, Y.-H., Wu, Q., & Ying, C. (2022). Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities. Transportation Research Part E: Logistics and Transportation Review, 162, 102712.
Yılmaz, Ö. F., Guan, Y., & Yılmaz, B. G. (2025). Designing a resilient humanitarian supply chain by considering viability under uncertainty: A machine learning embedded approach. Transportation Research Part E: Logistics and Transportation Review. เข้าถึงได้จาก
https://www.sciencedirect.com/science/article/abs/pii/S1366554524005349
Wang, Y., Peng, S., Zhou, X., Mahmoudi, M., & Zhen, L. (2020). Green logistics location-routing problem with eco-packages. Transportation Research Part E: Logistics and Transportation Review, 143, 102118.
Wamba, S. F., Dubey, R., Gunasekaran, A., & Akter, S. (2020). The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism. International Journal of Production Economics, 222, 107498.
World Bank. (2023). Supply chain resilience report: Navigating global disruptions. เข้าถึงได้จาก
World Economic Forum. (2024). Future of supply chains: AI-enabled sustainable logistics.
เข้าถึงได้จาก https://www.weforum.org
Wu, M., Tsai, N. E., & Yuen, K. F. (2025). Maritime AI socialisation: Exploring the impact of digital enablers on human–AI collaboration and service and process innovation. Transportation Research Part E: Logistics and Transportation Review, 197, 104053.
Xu, H., Liu, J., Xu, X., Chen, J., & Yue, X. (2024). The impact of AI technology adoption on operational decision-making in competitive heterogeneous ports. Transportation Research Part E: Logistics and Transportation Review, 183, 103428.
Xu, S., Ou, X., Govindan, K., Chen, M., & Yang, W. (2025). An adaptive genetic hyper-heuristic algorithm for a two-echelon vehicle routing problem with dual-customer satisfaction in community group-buying. Transportation Research Part E: Logistics and Transportation Review, 194, 103874.
Zhang, R., Zhang, Z., & Chen, Y. (2018). Green VRP with multi-objective optimization. Computers & Operations Research, 95, 1–12.
Zhang, Y., Mou, Z., Gao, F., Jiang, J., Ding, R., & Han, Z. (2020). UAV-enabled secure communications
by multi-agent deep reinforcement learning. IEEE Transactions on Vehicular Technology, 69(10), 11599–11611.
Zhang, Q., Li, X., Wang, J., & Chen, H. (2024). AI-enabled transport optimization for carbon-
neutral logistics. Transportation Research Part E, 188, 103532.
Zhang, Y., Tan, X., Gan, M., Liu, X., & Atasoy, B. (2025). Operational synchromodal transport
planning methodologies: Review and roadmap. Transportation Research Part E: Logistics and Transportation Review, 194, 103915
Zhang, X., Hao, Y., Zhang, L., & Yuan, X. (2025). Application of improved genetic algorithm to vehicle routing problem considering the environmental self-regulation of the freight companies. Expert Systems with Applications, 274, 127010.
