Advancing Decision-Making: AI-Driven Optimization Models for Complex Systems
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Abstract
Effective decision-making in complex systems requires optimization models that balance multiple competing objectives, such as cost efficiency, time constraints, and adaptability to dynamic environments. This research proposes an AI-driven optimization model utilizing the Pareto optimization algorithm to enhance decision-making accuracy and system resilience. The model was tested in a logistics scenario, demonstrating a 10% reduction in operational costs and a 36% decrease in time deviations while improving adaptability to real-time disruptions. Unlike traditional static models, the proposed framework dynamically adjusts to external factors, optimizing resource allocation and route planning in real-world conditions. The findings highlight the model’s capability to bridge the gap between theoretical AI advancements and practical applications in industries such as supply chain management, urban transportation, and disaster response logistics. While computational requirements and data availability pose challenges, future research should explore computational efficiency enhancements, broader industry applications, and sustainability integration. This study contributes to the advancement of AI-based multi-objective optimization, providing a scalable and adaptable solution for complex decision-making in dynamic environments
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How to Cite
Sihotang, H. T., Sihotang, J., Simbolon, A. P. H., Panjaitan, F. S., & Simbolon, R. S. (2024). Advancing Decision-Making: AI-Driven Optimization Models for Complex Systems. International Journal of Basic and Applied Science, 13(3), 123–136. https://doi.org/10.35335/ijobas.v13i3.581
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T. Ahmad, R. Madonski, D. Zhang, C. Huang, and A. Mujeeb, “Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm,” Renew. Sustain. Energy Rev., vol. 160, no. 5, p. 112128, 2022, doi: https://doi.org/10.1016/j.rser.2022.112128.
R. Nateghi, “Multi-dimensional infrastructure resilience modeling: an application to hurricane-prone electric power distribution systems,” Ieee Access, vol. 6, no. 6, pp. 13478–13489, 2018, doi: https://doi.org/10.1109/ACCESS.2018.2792680.
F. Ekundayo, “Leveraging AI-Driven Decision Intelligence for Complex Systems Engineering,” Int J Res Publ Rev, vol. 5, no. 11, pp. 1–10, 2024.
S. Kaggwa, T. F. Eleogu, F. Okonkwo, O. A. Farayola, P. U. Uwaoma, and A. Akinoso, “AI in decision making: transforming business strategies,” Int. J. Res. Sci. Innov., vol. 10, no. 12, pp. 423–444, 2024, doi: https://doi.org/10.51244/IJRSI.2023.1012032.
N. L. Rane, M. Paramesha, S. P. Choudhary, and J. Rane, “Artificial intelligence, machine learning, and deep learning for advanced business strategies: a review,” Partners Univers. Int. Innov. J., vol. 2, no. 3, pp. 147–171, 2024, doi: https://doi.org/10.5281/zenodo.12208298.
H. Bossel, Systems and models: complexity, dynamics, evolution, sustainability. Germany: BoD–Books on Demand, 2007.
S. E. Jørgensen, B. C. Patten, and M. Straškraba, “Ecosystems emerging: toward an ecology of complex systems in a complex future,” Ecol. Modell., vol. 62, no. 1–3, pp. 1–27, 1992, doi: https://doi.org/10.1016/0304-3800(92)90080-X.
A. A. Firoozi and A. A. Firoozi, “Intelligent Decision-Making Frameworks,” in Neuromorphic Computing: Transforming Disaster Management and Resilience in Civil Engineering, Springer, 2024, pp. 57–66. doi: https://doi.org/10.1007/978-3-031-65549-4_6.
S. Jebreili and A. Goli, “Optimization and computing using intelligent data-driven,” in Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making: Optimization Applications, 1st ed., I. Ali and U. Muhammad, Eds., CRC Press, 2024, p. 500.
Y. K. Dwivedi et al., “Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” Int. J. Inf. Manage., vol. 57, no. 3, p. 101994, 2021, doi: https://doi.org/10.1016/j.ijinfomgt.2019.08.002.
S. K. Moghaddam, R. Buyya, and K. Ramamohanarao, “Performance-aware management of cloud resources: A taxonomy and future directions,” ACM Comput. Surv., vol. 52, no. 4, pp. 1–37, 2019, doi: https://doi.org/10.1145/3337956.
L.-N. Lévy, “Advanced clustering and AI-driven decision support systems for smart energy management,” Université Paris-Saclay, 2024. [Online]. Available: https://theses.hal.science/tel-04634793/
A. Zappone, M. Di Renzo, and M. Debbah, “Wireless networks design in the era of deep learning: Model-based, AI-based, or both?,” IEEE Trans. Commun., vol. 67, no. 10, pp. 7331–7376, 2019, doi: https://doi.org/10.1109/TCOMM.2019.2924010.
S. Rahmani, H. Aghalar, S. Jebreili, and A. Goli, “Optimization and computing using intelligent data-driven approaches for decision-making,” in Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making, CRC Press, 2024, pp. 90–176. doi: https://doi.org/10.1201/9781003536796.
Y. Chen, Z. Liu, Y. Zhang, Y. Wu, X. Chen, and L. Zhao, “Deep reinforcement learning-based dynamic resource management for mobile edge computing in industrial internet of things,” IEEE Trans. Ind. Informatics, vol. 17, no. 7, pp. 4925–4934, 2020, doi: https://doi.org/10.1109/TII.2020.3028963.
A. M. Khedr, “Enhancing supply chain management with deep learning and machine learning techniques: A review,” J. Open Innov. Technol. Mark. Complex., vol. 10, no. 4, p. 100379, 2024, doi: https://doi.org/10.1016/j.joitmc.2024.100379.
S. P. Ghodake, V. R. Malkar, K. Santosh, L. Jabasheela, S. Abdufattokhov, and A. Gopi, “Enhancing Supply Chain Management Efficiency: A Data-Driven Approach using Predictive Analytics and Machine Learning Algorithms.,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 4, pp. 672–682, 2024, doi: 10.14569/ijacsa.2024.0150469.
P. Esmaeilzadeh, “Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations,” Artif. Intell. Med., vol. 151, no. 5, p. 102861, 2024, doi: https://doi.org/10.1016/j.artmed.2024.102861.
D. M. Buede and W. D. Miller, The engineering design of systems: models and methods. John Wiley & Sons, 2024.
S. S. Rao, Engineering optimization: theory and practice. John Wiley & Sons, 2019.
D. A. Pierre, Optimization theory with applications. Courier Corporation, 2012.
A. Wilson and M. R. Anwar, “The Future of Adaptive Machine Learning Algorithms in High-Dimensional Data Processing,” Int. Trans. Artif. Intell., vol. 3, no. 1, pp. 97–107, 2024, doi: https://doi.org/10.33050/italic.v3i1.656.
B. Taylor, V. S. Marco, W. Wolff, Y. Elkhatib, and Z. Wang, “Adaptive deep learning model selection on embedded systems,” ACM Sigplan Not., vol. 53, no. 6, pp. 31–43, 2018, doi: https://doi.org/10.1145/3299710.3211336.
M. Soori, B. Arezoo, and R. Dastres, “Artificial intelligence, machine learning and deep learning in advanced robotics, a review,” Cogn. Robot., vol. 3, no. 1, pp. 54–70, 2023, doi: https://doi.org/10.1016/j.cogr.2023.04.001.
S. Darvishpoor, A. Darvishpour, M. Escarcega, and M. Hassanalian, “Nature-inspired algorithms from oceans to space: A comprehensive review of heuristic and meta-heuristic optimization algorithms and their potential applications in drones,” Drones, vol. 7, no. 7, p. 427, 2023, doi: https://doi.org/10.3390/drones7070427.
M. Yu, J. Xu, W. Liang, Y. Qiu, S. Bao, and L. Tang, “Improved multi-strategy adaptive Grey Wolf Optimization for practical engineering applications and high-dimensional problem solving,” Artif. Intell. Rev., vol. 57, no. 10, p. 277, 2024, doi: https://doi.org/10.1007/s10462-024-10821-3.
S. Twaha and M. A. M. Ramli, “A review of optimization approaches for hybrid distributed energy generation systems: Off-grid and grid-connected systems,” Sustain. Cities Soc., vol. 41, no. 4, pp. 320–331, 2018, doi: https://doi.org/10.1016/j.scs.2018.05.027.
A. Shoomal, M. Jahanbakht, P. J. Componation, and D. Ozay, “Enhancing supply chain resilience and efficiency through internet of things integration: Challenges and opportunities,” Internet of Things, vol. 27, no. 1, p. 101324, 2024, doi: https://doi.org/10.1016/j.iot.2024.101324.
A. Malekloo, E. Ozer, M. AlHamaydeh, and M. Girolami, “Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights,” Struct. Heal. Monit., vol. 21, no. 4, pp. 1906–1955, 2022, doi: https://doi.org/10.1177/14759217211036880.
I. H. Sarker, “AI-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems,” SN Comput. Sci., vol. 3, no. 2, p. 158, 2022, doi: https://doi.org/10.1007/s42979-022-01043-x.
C. R. Telles, “A mathematical modelling for workflows,” J. Math., vol. 2019, no. 1, p. 4784909, 2019.
J. R. Raol and R. Ayyagari, Control systems: classical, modern, and AI-based approaches. CRC Press, 2019. doi: https://doi.org/10.1201/9781351170802.
W. L. Winston, Operations research: applications and algorithm, FOURTH. Thomson Learning, Inc., 2004.
F. S. Hillier and G. J. Lieberman, Introduction to operations research. McGraw-Hill, 2015. doi: 9781265427610.
K. Amouzgar, “Multi-objective optimization using Genetic Algorithms,” 2012. [Online]. Available: https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A570751&dswid=-6663
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