Predictive and Prescriptive Logistics Optimization Using Hybrid AI, Time-Series Analytics, and Synthetic Data: A Case Study

Global logistics networks face increasing volatility driven by geopolitical tensions, climate disruptions, demand variability, and operational uncertainty. Although artificial intelligence has improved predictive capabilities in logistics, classical and standalone learning models remain limited by data sparsity, non-stationarity, and scalability constraints. This study proposes a hybrid logistics intelligence framework that integrates time-series forecasting, synthetic data generation, and AI-based optimization. The framework is designed to enhance forecasting robustness and translate predictions into actionable operational decisions. A FedEx case study demonstrates how historical shipment data, real-time telemetry, and synthetically generated disruption scenarios can be jointly leveraged to improve demand forecasting, routing efficiency, and service reliability. Performance is evaluated across real, simulated, and hybrid datasets. Results show that the proposed approach consistently outperforms traditional statistical and machine-learning methods in accuracy, robustness, and operational scalability.

Logistics Optimization, Time

1. Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M., Discrete-Event System Simulation, 5th ed., Pearson, 2014. [Google Scholar] [Crossref]

2. Hyndman, R. J., & Athanasopoulos, G., Forecasting: Principles and Practice, OTexts, 2015. [Google Scholar] [Crossref]

3. Toth, P., & Vigo, D., Vehicle Routing: Problems, Methods, and Applications, SIAM, 2014. [Google Scholar] [Crossref]

4. Nazari, M., Oroojlooy, A., Snyder, L., & Takáč, M., “Reinforcement learning for solving the vehicle routing problem,” Advances in Neural Information Processing Systems, 2018. [Google Scholar] [Crossref]

5. Ivanov, D., Dolgui, A., & Sokolov, B., “The impact of digital technology and Industry 4.0 on supply chain resilience,” International Journal of Production Research, vol. 57, no. 3, 2019. [Google Scholar] [Crossref]

6. Hosseini, S., Ivanov, D., & Dolgui, A., “Review of quantitative methods for supply chain resilience,” Transportation Research Part E, vol. 125, 2019. [Google Scholar] [Crossref]

7. Yoon, J., Jarrett, D., & van der Schaar, M., “Time-series generative adversarial networks,” Advances in Neural Information Processing Systems, 2019. [Google Scholar] [Crossref]

8. Ivanov, D., & Dolgui, A., “Viability of intertwined supply networks during the COVID-19 pandemic,” International Journal of Production Research, vol. 58, no. 10, 2020. [Google Scholar] [Crossref]

9. Salinas, D., et al., “DeepAR: Probabilistic forecasting with autoregressive recurrent networks,” International Journal of Forecasting, vol. 36, no. 3, 2020. [Google Scholar] [Crossref]

10. Lim, B., et al., “Temporal Fusion Transformers for interpretable multi-horizon time series forecasting,” International Journal of Forecasting, vol. 37, no. 4, 2021. [Google Scholar] [Crossref]

11. Zhou, H., et al., “Informer: Beyond efficient transformer for long sequence time-series forecasting,” AAAI Conference on Artificial Intelligence, 2021. [Google Scholar] [Crossref]

12. Wu, H., et al., “Autoformer: Decomposition transformers with autocorrelation for long-term series forecasting,” NeurIPS, 2021. [Google Scholar] [Crossref]

13. Benidis, K., et al., “Deep learning for time series forecasting: Tutorial and literature survey,” ACM Computing Surveys, vol. 55, no. 6, 2022. [Google Scholar] [Crossref]

14. Bogyrbayeva, A., et al., “Learning-based methods for vehicle routing problems: A survey,” Computers & Operations Research, vol. 146, 2022. [Google Scholar] [Crossref]

15. Nie, Y., et al., “A time series is worth 64 words: Long-term forecasting with transformers,” ICLR, 2023. [Google Scholar] [Crossref]

16. Chatterjee, S., et al., “GAN-based synthetic time-series generation for improved predictive modeling,” Sensors, vol. 23, no. 2, 2023. [Google Scholar] [Crossref]

17. Klopries, H., et al., “Synthetic time-series generation for industrial analytics,” Knowledge-Based Systems, vol. 282, 2024. [Google Scholar] [Crossref]

18. Long, Y., et al., “Leveraging synthetic data to tackle machine learning challenges in supply chains,” International Journal of Production Research, 2025. [Google Scholar] [Crossref]

19. Xu, K., et al., “Learning-assisted optimization for dynamic vehicle routing under uncertainty,” Computers & Operations Research, 2025. [Google Scholar] [Crossref]

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