International Journal of Wireless and Microwave Technologies (IJWMT)
IJWMT Vol. 16, No. 1, 8 Feb. 2026
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A Hybrid MAS-CBR Framework with Optimization for Adaptive Supply Chain Design and Management
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Author(s)
Index Terms
Multi-Agent Systems, Case-Based Reasoning, Supply Chain Management, Blockchain, Deep Reinforcement Learning
Abstract
Global supply chains are increasingly characterized by complexity, uncertainty, and vulnerability to disruptions, creating a pressing need for intelligent, adaptive systems that support decentralized decision-making and real-time control. This paper develops a new framework that integrates Multi-Agent Systems (MAS) with Case-Based Reasoning (CBR) to address these challenges. The model leverages autonomous agents representing suppliers, manufacturers, distributors, retailers, and coordinators that negotiate through defined protocols while embedding CBR mechanisms to retrieve and adapt historical supply chain cases for enhanced responsiveness. An optimization layer, guided by both agent heuristics and case-driven initial solutions, targets key objectives such as cost minimization, lead-time reduction, and resilience improvement. Simulation experiments were conducted under both static and dynamid environments with disruptions including supplier failures and demand fluctuations. Results demonstrate that the proposed framework achieves convergence up to 34- 41% faster than heuristic-only baselines (p<0.05) and sustains solution quality with supply chain sizes increasing from 50 to 500 agents, indicating near-linear scalability. Comparative analysis further highlights adaptability in dynamic contexts and robustness under uncertainty. A case study illustrates practical deployment and validates its effectiveness. The findings provide evidence of a powerful synergy between MAS and CBR, with implications for next-generation supply chain intelligence.
Cite This Paper
Rajbala, Pawan Kumar Singh Nain, Avadhesh Kumar, "A Hybrid MAS-CBR Framework with Optimization for Adaptive Supply Chain Design and Management", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.16, No.1, pp. 63-78, 2026. DOI:10.5815/ijwmt.2026.01.05
Reference
[1]Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775-788.
[2]Kamruzzaman, M., Duan, J., Shi, D., & Benidris, M. (2021). A deep reinforcement learning-based multi-agent framework to enhance power system resilience using shunt resources. IEEE Transactions on Power Systems, 36(6), 5525-5536.
[3]Farazi, M. Z. R. (2025). Enhancing supply chain resilience with multi-agent systems and machine learning: a framework for adaptive decision-making. The American Journal of Engineering and Technology, 7(03), 6-20.
[4]Chen, X., Ong, Y. S., Tan, P. S., Zhang, N., & Li, Z. (2013, October). Agent-based modeling and simulation for supply chain risk management-a survey of the state-of-the-art. In 2013 IEEE International Conference on Systems, Man, and Cybernetics (pp. 1294-1299). IEEE.
[5]Khiloun, I. E., Belmabrouk, K., & Dekhici, L. (2024). Literature Review on Supply Chains Optimization Using Multi-Agents Communication and Collaboration. Intelligent Methods and Alternative Economic Models for Sustainability, 74-94.
[6]Schott, P., Lederer, M., Eigner, I., & Bodendorf, F. (2020). Case-based reasoning for complexity management in Industry 4.0. Journal of Manufacturing Technology Management, 31(5), 999-1021.
[7]Soori, M., Jough, F. K. G., Dastres, R., & Arezoo, B. (2024). AI-based decision support systems in Industry 4.0, A review. Journal of Economy and Technology.
[8]Li, S., Zhang, H., Yan, W., & Jiang, Z. (2021). A hybrid method of blockchain and case-based reasoning for remanufacturing process planning. Journal of Intelligent Manufacturing, 32, 1389-1399.
[9]Chavhan, S., Kumar, S., Tiwari, P., Liang, X., Lee, I. H., & Muhammad, K. (2023). Edge-enabled blockchain-based V2X scheme for secure communication within the smart city development. IEEE Internet of Things Journal, 10(24), 21282-21293.
[10]Almuiet, M. Z., & Al-zawahra, M. M. (2019). Automated knowledge acquisition framework for supply chain management based on hybridization of case based reasoning and intelligent agent. International Journal of Advanced Computer Science and Applications, 10(1).
[11]Verma, P. (2024). Transforming Supply Chains Through AI: Demand Forecasting, Inventory Management, and Dynamic Optimization. Integrated Journal of Science and Technology, 1(9).
[12]Khedr, A. M. (2024). Enhancing supply chain management with deep learning and machine learning techniques: A review. Journal of Open Innovation: Technology, Market, and Complexity, 100379.
[13]Samayamantri, L. S. (2025). Challenges and Prospects of Implementing AI-Powered Retail and Consumer Packaged Solutions in the Global Supply Chain for Enhanced Well-Being. Artificial Intelligence and Machine Learning for Sustainable Development, 172-182.
[14]Kalusivalingam, A. K., Sharma, A., Patel, N., & Singh, V. (2022). Enhancing Supply Chain Resilience through AI: Leveraging Deep Reinforcement Learning and Predictive Analytics. International Journal of AI and ML, 3(9).
[15]Balasubramanian, S. (2024). Ai-driven geospatial decision support systems for sustainable development and natural resource management. Journal ID, 2770, 4350.
[16]Oriekhoe, O. I., Oyeyemi, O. P., Bello, B. G., Omotoye, G. B., Daraojimba, A. I., & Adefemi, A. (2024). Blockchain in supply chain management: A review of efficiency, transparency, and innovation. International Journal of Science and Research Archive, 11(1), 173-181.
[17]Ahmad, A. Y. B., Verma, N., Sarhan, N., Awwad, E. M., Arora, A., & Nyangaresi, V. O. (2024). An IoT and blockchain-based secure and transparent supply chain management framework in smart cities using optimal queue model. IEEE Access.
[18]Agrawal, T. K., Angelis, J., Khilji, W. A., Kalaiarasan, R., & Wiktorsson, M. (2023). Demonstration of a blockchain-based framework using smart contracts for supply chain collaboration. International journal of production research, 61(5), 1497-1516.
[19]Wu, W., Cheung, C., Lo, S. Y., Zhong, R. Y., & Huang, G. Q. (2020). An iot-enabled real-time logistics system for a third-party company: A case study. Procedia Manufacturing, 49, 16-23.
[20]Dolgui, A., & Ivanov, D. (2022). 5G in digital supply chain and operations management: fostering flexibility, end-to-end connectivity and real-time visibility through internet-of-everything. International Journal of Production Research, 60(2), 442-451.
[21]Frederico, G. F. (2021). Towards a supply chain 4.0 on the post-COVID-19 pandemic: a conceptual and strategic discussion for more resilient supply chains. Rajagiri Management Journal, 15(2), 94-104.
[22]Belhadi, A., Kamble, S., Fosso Wamba, S., & Queiroz, M. M. (2022). Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework. International Journal of Production Research, 60(14), 4487-4507.
[23]Alsmairat, M. A. K., & Hammad, M. S. A. (2023, December). Mitigating supply chain risks through AI integration: the mediation role of data-driven decision culture. In 4th International Conference on Distributed Sensing and Intelligent Systems (ICDSIS 2023) Vol. 2023, pp. 276-285. IET.
[24]Aljohani, A. (2023). Predictive analytics and machine learning for real-time supply chain risk mitigation and agility. Sustainability, 15(20), 15088.
[25]Belhadi, A., Kamble, S., Fosso Wamba, S., & Queiroz, M. M. (2022). Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework. International Journal of Production Research, 60(14), 4487-4507.
[26]Rahmanzadeh, S., Pishvaee, M. S., & Govindan, K. (2023). Emergence of open supply chain management: the role of open innovation in the future smart industry using digital twin network. Annals of Operations Research, 329(1), 979-1007.
[27]Elufioye, O. A., Ike, C. U., Odeyemi, O., Usman, F. O., & Mhlongo, N. Z. (2024). Ai-Driven predictive analytics in agricultural supply chains: a review: assessing the benefits and challenges of ai in forecasting demand and optimizing supply in agriculture. Computer Science & IT Research Journal, 5(2), 473-497.
[28]Younis, H., Sundarakani, B., & Alsharairi, M. (2022). Applications of artificial intelligence and machine learning within supply chains: systematic review and future research directions. Journal of Modelling in Management, 17(3), 916-940.
[29]Vijaykumar, V., Mercy, P., Lucia Agnes Beena, T., Leena, H. M., & Savarimuthu, C. (2024). Convergence of IoT, Artificial Intelligence and Blockchain Approaches for Supply Chain Management. In Blockchain, IoT, and AI Technologies for Supply Chain Management: Apply Emerging Technologies to Address and Improve Supply Chain Management (pp. 45-89). Berkeley, CA: Apress.
[30]Mohseni, S., & Moghaddas-Tafreshi, S. M. (2018). Development of a multi-agent system for optimal sizing of a commercial complex microgrid. arXiv preprint arXiv:1811.12553. https://arxiv.org/abs/1811.12553
[31]Kumar, M. R., Ghosh, S., & Das, S. (2016). A hybrid optimization-based approach for parameter estimation and investigation of fractional dynamics in ultracapacitors. Circuits, Systems, and Signal Processing, 35(6), 1949–1971. https://doi.org/10.1007/s00034-016-0254-2link.springer.com.
[32]Noori, B. (2015). Developing a CBR system for marketing mix planning and weighting method selection using fuzzy AHP. Applied Artificial Intelligence, 29(1), 1–32. https://doi.org/10.1080/08839514.2014.962282tandfonline.com.
[33]Dhatterwal, J. S., Dixit, S., & Srinivasan, S. (2019). The role of MAS-based CBRS using DM techniques for the supplier selection. International Journal of Computer Science and Engineering (IJCSE), 7(5), 1–6. DOI: https://doi.org/10.26438/ijcse/v7i5.16581665