A Comprehensive Review of Optimization Techniques, Stochastic Models, and Intelligent Systems in Industrial Engineering
Keywords:
Optimization, Stochastic Models, Intelligent Systems, Industrial Engineering, Simulation, Artificial IntelligenceAbstract
Industrial engineering has undergone a significant transformation with the integration of optimization techniques, stochastic modeling, and intelligent systems, enabling more effective decision-making in complex and uncertain industrial environments. This review presents a comprehensive analysis of classical and modern optimization methods, stochastic models, and intelligent systems used in industrial applications. It highlights recent advancements such as simulation-based optimization, artificial intelligence (AI), machine learning, and hybrid approaches that enhance the adaptability, efficiency, and robustness of industrial systems. The study also examines practical applications in production planning, supply chain management, manufacturing, and logistics. Furthermore, key challenges—including computational complexity, data availability, and methodological integration—are identified, alongside emerging research directions focused on autonomous decision-making, real-time optimization, digital twins, and sustainable industrial systems. This review provides a roadmap for researchers and practitioners seeking to leverage advanced methodologies to design intelligent, resilient, and sustainable industrial operations.
How to cite this article:
Prakhya S, A Comprehensive Review of Optimization Techniques, Stochastic Models, and Intelligent Systems in Industrial Engineering. J Adv Res Prod Ind Engg 2026; 13(1): 8-13.
References
Desai, S. S., & Lim, G. J. (2013). Hazardous material routing using stochastic dynamic networks. Industrial and Systems Engineering Review. Maxwell, M. S., et al. (2014). Simulation optimization framework for dynamic systems. Computers & Industrial Engineering.
