Deep Neural Network Based Intelligent Scheduling and Optimization Framework for Smart Logistics and Supply Chain Systems
DOI:
https://doi.org/10.15662/IJARCST.2025.0805030Keywords:
Deep Neural Networks, Intelligent Scheduling, Supply Chain Optimization, Smart Logistics, Predictive Analytics, Resource Allocation, Vehicle Routing, IoT-enabled Logistics, Real-time Decision-making, Operational EfficiencyAbstract
The rapid expansion of global supply chains and increasing demand for efficient logistics operations have created complex challenges in scheduling, routing, inventory management, and real-time decision-making. Traditional optimization methods often fail to handle the high-dimensional, dynamic, and stochastic nature of modern logistics networks. Deep Neural Networks (DNNs) provide an advanced computational approach capable of learning intricate patterns, predicting outcomes, and optimizing multi-variable systems in real time.
This research proposes a Deep Neural Network-based Intelligent Scheduling and Optimization Framework for smart logistics and supply chain systems. The framework leverages DNN models to forecast demand, optimize vehicle routing, allocate resources efficiently, and dynamically adjust schedules based on real-time data streams from IoT-enabled logistics platforms. By integrating predictive analytics with optimization algorithms, the system supports end-to-end supply chain visibility, operational efficiency, and proactive decision-making.
The research methodology includes system architecture design, simulation of logistics scenarios, training of deep neural networks with historical and real-time data, and evaluation of framework performance against conventional scheduling methods. Results demonstrate that the proposed framework improves delivery efficiency, reduces operational costs, enhances resource utilization, and mitigates delays. This approach provides a scalable, intelligent, and adaptive solution for modern smart logistics and supply chain management, enabling enterprises to optimize operations in complex, dynamic environments.
References
1. Panda, S. S. (2024). Managing BSL Implementation A TPM’s Guide to Robust Data centers. International Journal of Technology, Management and Humanities, 10(01), 33-38.
2. Ambati, K. C. (2025). Improving user experience and operational efficiency for smarter procurement management. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(3), 1282–1289.
3. Bheemisetty, N. (2024). From Fragmentation to Agility: Nautilus Architecture for Risk Management Modernization. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10673-10682.
4. Gopinathan, V. R. (2024). Meta-Learning–Driven Intrusion Detection for Zero-Day Attack Adaptation in Cloud-Native Networks. International Journal of Humanities and Information Technology, 6(01), 19-35.
5. Kondisetty, K., Mohammed, A. S., & Muthusamy, P. (2024). Omni-Channel Customer Onboarding with NLP-Powered Document Intelligence. Journal of Artificial Intelligence & Machine Learning Studies, 8, 124-157.
6. Mulla, F. (2024). Choosing the Best Architecture for Mobile Applications. International Journal Of Research In Computer Applications And Information Technology, 7, 2350–2363. https://doi.org/10.34218/IJRCAIT_07_02_173
7. Ambalakannu, M. (2024). Driving Operational Efficiency and Clinical Insights via Unified Care Management. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10693-10702.
8. Indurthy, V. S. K. (2024). Streamlining ROP Metrics and Reporting through Cloud Migration and Automation. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10703-10712.
9. Soundappan, S. J. (2024). AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14905.
10. Karnam, A. (2021). The Architecture of Reliability: SAP Landscape Strategy, System Refreshes, and Cross-Platform Integrations. International Journal of Research and Applied Innovations, 4(5), 5833–5844. https://doi.org/10.15662/IJRAI.2021.0405005
11. Thumala, S. R., & Pillai, B. S. (2024). Cloud Cost Optimization Methodologies for Cloud Migrations. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 4797-4809.
12. Sugumar, R. (2025). Open Ecosystems in Finance: Balancing Innovation, Security, and Compliance. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(1), 11548-11554.
13. Kumar, S. A., & Anand, L. (2025). A Novel EEG-Based Deep Learning Framework for Enhancing Communication in Locked-In Syndrome Using P300 Speller and Attention Mechanisms. KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 19(11), 3841-3855.
14. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
15. Kiran, A., & Kumar, S. A methodology and an empirical analysis to determine the most suitable synthetic data generator. IEEE Access 12, 12209–12228 (2024).
16. Dama, H. B. (2024). Cross-Cloud Data Consistency Models for Always-On Banking Platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8468-8476.
17. Dave, B. L. (2023). Enhancing Vendor Collaboration via an Online Automated Application Platform. International Journal of Humanities and Information Technology, 5(02), 44-52.
18. Karvannan, R. (2024). ConsultPro Cloud Modernizing HR Services with Salesforce. International Journal of Technology, Management and Humanities, 10(01), 24-32.
19. Ezhilan, R., Kumar, V., Umasankar, P., Suman, S., Murali, G., & Kowsalikanand, P. (2024, October). Optimizing Diabetic Foot Ulcer Classification with Transfer Learning: A Performance Analysis. In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 1121-1125). IEEE.
20. Ramsugeerthi, A., Neela Madheswari, A., Umamaheswari, A., & Prassana, D. (2020). Location navigation assistance for educational institutions using augmented reality. Journal of Xidian University, 14(4), 1342–1347. https://doi.org/10.37896/jxu14.4/156
21. Yashwanth, K., Adithya, N., Sivaraman, R., Janakiraman, S., & Rengarajan, A. (2021, July). Design and Development of Pipelined Computational Unit for High-Speed Processors. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.
22. Kesavan, E. (2025). Salesforce Classic as Well as Lightning Automation using Tosca Automation and Tosca AI-Powered Salesforce Engine. i-Manager's Journal on Information Technology, 14(2).
23. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
24. Rajasekaran, M., Sekar, S., Manikandaprabhu, K., Vijayakumar, R., Rajmohan, M., & Murugan, S. (2024, October). Next-Gen Coaching: IoT and Linear Regression for Adaptive Training Load Management. In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 224-229). IEEE.
25. Vigenesh, M., Upadhyay, A. K., Murali, M. J., Seth, K., & Shinde, G. R. (2024, June). Exploring the Role of Visual Information in Mixed Media Creation. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
26. Fazilath, M., & Umasankar, P. (2025, February). Comprehensive Analysis of Artificial Intelligence Applications for Early Detection of Ovarian Tumours: Current Trends and Future Directions. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-9). IEEE.
27. Archana, R., & Anand, L. (2023, September). Ensemble Deep Learning Approaches for Liver Tumor Detection and Prediction. In 2023 Third International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 325-330). IEEE.
28. Konda, S. K. (2024). Sustainable energy optimization through cloud-native building automation and predictive analytics integration. World Journal of Advanced Research and Reviews, 24(3), 3619–3628. https://doi.org/10.30574/wjarr.2024.24.3.3803
29. Uttama Reddy Sanepalli , " Adaptive Intelligence Framework for Retirement Portfolio Management: Self-Optimizing Infrastructure for Dynamic Asset Allocation and Risk Mitigation" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 6, pp.769-780, November-December-2022. Available at doi : https://doi.org/10.32628/CSEIT22557
30. Ravi Kumar Ireddy, " AI Driven Predictive Vulnerability Intelligence for Cloud-Native Ecosystems" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 2, pp.894-903, March-April-2023. Available at doi : https://doi.org/10.32628/CSEIT2342438.
31. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
32. Jagadeesh, S., & Sugumar, R. (2017). A Comparative study on Artificial Bee Colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243-248.
33. Fazilath, M., & Umasankar, P. (2025, February). Comprehensive Analysis of Artificial Intelligence Applications for Early Detection of Ovarian Tumours: Current Trends and Future Directions. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-9). IEEE.
