A comprehensive review of optimization techniques in industrial applications: Trends, classifications, and future directions

Mukti Asih, Hayati and Mohamad, Effendi and Irianto and Ma’arif, Alfian (2025) A comprehensive review of optimization techniques in industrial applications: Trends, classifications, and future directions. Buletin Ilmiah Sarjana Teknik Elektro, 7 (3). pp. 382-396. ISSN 2685-9572

Text 01071210920251828412138.pdf Available under License Creative Commons Attribution Share Alike. Download (602kB)

Abstract

In recent years, optimization techniques have played a central role in enhancing operational efficiency and decision-making across diverse industrial sectors, including manufacturing, logistics, and transportation, energy, healthcare, and agriculture. These sectors face complex, large-scale, and often nonlinear challenges that demand both precision and adaptability. The research contribution of this review is to provide a structured classification of optimization methods—namely exact algorithms, heuristics, metaheuristics, and AI-integrated hybrid models—and to critically evaluate their practical applications, limitations, and emerging trends across industries. This study adopts a review approach to identify and compare those techniques in solving various optimization problems. Through a detailed analysis of over 30 recent publications for last four years, the review highlights how these techniques are being applied in real-world industrial environments, including cold chain logistics, smart energy systems, precision agriculture, and healthcare scheduling. The results indicate a growing reliance on hybrid and AI-enhanced models due to their superior scalability, adaptability, and potential alignment with Industry 4.0 and Sustainable Development Goals (SDGs). However, challenges remain in areas such as computational efficiency, model interpretability, and real-time data integration. In conclusion, this study provides valuable insights for both researchers and practitioners seeking to apply optimization techniques more effectively in industrial systems, while also identifying critical research gaps for future exploration by addressing the growing complexity and sustainability demands of modern industry.

Item Type:	Article
Uncontrolled Keywords:	Optimization Techniques, Industrial Applications, Metaheuristics, Hybrid AI-integration, Exact Algorithm
Divisions:	Faculty Of Industrial And Manufacturing Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	30 Dec 2025 06:30
Last Modified:	30 Dec 2025 06:30
URI:	http://eprints.utem.edu.my/id/eprint/29348
Statistic Details:	View Download Statistic

Actions (login required)

View Item