Leveraging the Synergy of AI and Optimization Models for Enhanced Problem Solving and Decision-Making

Authors

  • Bipradas Bairagi Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India
  • Bikash Bepari Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India
  • Balaram Dey Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India
  • Pritam Pain Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India
  • Goutam Kumar Bose Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India
  • Tarun Kanti Jana Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India

DOI:

https://doi.org/10.13052/jgeu0975-1416.1426

Keywords:

Artificial intelligence, optimization models, hybrid algorithms, decision-making, ethical considerations

Abstract

The development of the combination of the artificial intelligence (AI) and optimization models is a revolutionary way to solve problems and make decisions in a variety of areas. This paper examines the synergistic opportunity of integrating AI methods, i.e., machine learning and natural language processing, with different optimization models, i.e., linear programming and genetic algorithms. The efficiencies and high-quality solutions can be attained by using the ability of AI to process large volumes of data and optimize performance and the accuracy of the optimization models to make decisions. The paper provides the concepts of foundations, a specific hybrid form of AI-optimization algorithm, and explains its use with a numerical example. There are examples of successful integrations in supply chain management, finance, healthcare and others, with the improved decision-making, strategic benefits and economic effects. The ethical concerns and the future perspectives of the AI-enhanced optimization are also discussed, with the focus placed on the significance of the further research and development of this area.

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Author Biographies

Bipradas Bairagi, Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India

Bipradas Bairagi has Received BE, ME and Ph. D. from Jadavpur University, Kolkata, India. He is an accomplished academic and researcher in the field of industrial and production engineering, with extensive expertise in multi-criteria decision-making (MCDM), fuzzy systems, and supply chain management. With over 17 years of teaching and research experience. Dr. Bairagi has authored numerous research papers in reputed international journals, including Computers & Industrial Engineering and Journal of Manufacturing Systems, with several high-impact publications and citations to his credit. His research focuses on developing innovative fuzzy MCDM models for complex decision environments. He is also actively involved in guiding research scholars His work continues to influence modern decision-making methodologies in engineering and management domains.

Bikash Bepari, Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India

Bikash Bepari is an accomplished academic and researcher in the field of Production Engineering, with B.E., M.E., and Ph.D. degrees from Jadavpur University. His research expertise spans smart materials, compliant mechanisms, robotics, and intelligent systems, with a strong emphasis on ionic polymer–metal composite (IPMC) actuators. He has made significant contributions to the design and development of multifunctional microgrippers, dexterous robotic hands, and sensor-based systems. Dr. Bepari has authored numerous high-impact research publications in reputed international journals and conferences, with notable works in compliant gripper design, IPMC-based sensing, and robotic applications receiving considerable citations. His interdisciplinary research also extends to optimization techniques and decision support systems. In addition, he holds about a dozen patents, reflecting his strong innovation and translational research capabilities. His work bridges theory and practical applications, contributing significantly to advancements in smart actuation, micro-robotics, and intelligent engineering systems.

Balaram Dey, Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India

Balaram Dey is a Professor in the Department of Mechanical Engineering at Haldia Institute of Technology. He is an active researcher in the fields of multi-criteria decision-making (MCDM), supply chain management, and fuzzy decision analysis. Prof. Dey has contributed extensively to the development of advanced decision-making models, particularly in areas such as warehouse location selection, robot selection, and performance evaluation of industrial systems under uncertainty. His collaborative works, often with researchers like B. Bairagi and B. Sarkar, have been widely published in reputed journals such as Computers & Industrial Engineering. With over a hundred citations on several key publications, his research has significantly influenced decision science methodologies in industrial and supply chain applications. He continues to contribute to emerging areas like green manufacturing and intelligent decision systems through both journal articles and conference proceedings.

Pritam Pain, Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India

Pritam Pain is an Assistant Professor in the Department of Mechanical Engineering at Haldia Institute of Technology, India. He received his B.Tech and M.Tech degrees in Mechanical Engineering and is currently pursuing his Ph.D. in Non-Traditional Manufacturing Processes. His research interests include non-conventional and micro-machining processes (EDM, WEDM, μ-EDM), parametric and multi-objective optimization, and the application of metaheuristic and soft-computing techniques in advanced manufacturing. He has published several research articles and book chapters in reputed international journals and edited volumes.

Goutam Kumar Bose, Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India

Goutam Kumar Bose is a Professor and Head of the Department of Mechanical Engineering at Haldia Institute of Technology, India, with over 25 years of academic and industrial experience. He holds a Ph.D. in Production Engineering from Jadavpur University. His research interests include advanced and non-conventional manufacturing processes, production management, tribology, and micro-scale manufacturing. He has led and contributed to several sponsored research projects funded by CSIR and AICTE and has co-authored multiple books published by leading international publishers.

Tarun Kanti Jana, Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India

Tarun Kanti Jana is a distinguished academician and researcher, currently serving as the Principal of Haldia Institute of Technology. He obtained his Ph.D. from Jadavpur University and has a long and rich experience in teaching, research, and academic administration in Mechanical Engineering.

Dr. Jana’s research interests include agent-based systems, holonic manufacturing, digital twin technology, and advanced manufacturing systems. He has made notable contributions to the field through publications in reputed journals, including the Journal of Manufacturing Systems. His work on agent-based holonic manufacturing and smart production environments has received significant academic recognition.

He has supervised research, contributed to interdisciplinary studies, and actively participated in international conferences. His recent work emphasizes Industry 4.0, cognitive manufacturing, and intelligent decision-making systems, positioning him as a key contributor to modern manufacturing research and innovation.

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Storn, R., and Price, K. (1997). Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.

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Published

2026-08-24

How to Cite

Bairagi, B., Bepari, B., Dey, B., Pain, P., Bose, G. K., & Jana, T. K. (2026). Leveraging the Synergy of AI and Optimization Models for Enhanced Problem Solving and Decision-Making. Journal of Graphic Era University, 14(02), 461–508. https://doi.org/10.13052/jgeu0975-1416.1426

Issue

2026: Vol 14 Iss 2

Section

Articles