Dr. Amir Hossein akbari | Engineering and Technology | Research Excellence Award
Iran University of Science and Technology | Iran
Amir Hosein Akbari is an accomplished researcher in industrial engineering with a strong record of scholarly impact his academic background is grounded in advanced industrial engineering education, complemented by progressive research experience spanning optimization, decision sciences, and intelligent systems. His professional experience includes active involvement in high-quality research collaborations and contributions to applied and theoretical studies addressing complex industrial and societal problems. His core research interests focus on supply chain management, optimization, meta-heuristic and evolutionary algorithms, scheduling, decision support systems, and artificial intelligence–driven industrial applications, with several influential works in expert systems, soft computing, and manufacturing systems. His publications have appeared in high-impact venues such as Expert Systems with Applications, Soft Computing, and Neural Computing and Applications, reflecting both methodological rigor and practical relevance. Recognition of his work is demonstrated through strong citation performance and collaborations with well-established scholars in operations research and industrial engineering. Overall, his research portfolio highlights a consistent commitment to advancing intelligent optimization methods and decision-making frameworks, contributing valuable insights to academia and industry while strengthening the scientific foundations of modern industrial engineering.
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Featured Publications
Multi-objective Boxing Match Algorithm for Multi-objective Optimization Problems
Boxing Match Algorithm: A New Meta-Heuristic Algorithm
Semi-Permutation-Based Genetic Algorithm for Order Acceptance and Scheduling in a Two-Stage Assembly Problem
Service Level and Profit Maximisation in Order Acceptance and Scheduling Problem with Weighted Tardiness
A Four-Echelon Supply Chain Considering Economic, Social, and Regional Satisfaction Goals