Wenqiang Zhang ^1* Xuan Bao ² Xinchang Hao ³ Mitsuo Gen ⁴

• ¹ School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, China
• ² College of Information Science and Engineering, Henan University of Technology, Zhengzhou, Henan Province, China
• ³ School of Art and Design, Changzhou Institute of Technology, Changzhou, Jiangsu, China
• ⁴ Tokyo University of Science, Tokyo, Tokyo, Japan

The advent of Industry 4.0 and the emerging Industry 5.0 have fundamentally transformed manufacturing systems, introducing unprecedented levels of complexity in production scheduling. This complexity is further amplified by the integration of cyber-physical systems, Internet of Things, Artificial Intelligence, and humancentric approaches, necessitating more sophisticated optimization methods. This paper aims to provide a more comprehensive perspective on the application of metaheuristic algorithms in shop scheduling problems within the context of Industry 4.0 and Industry 5.0. Through a systematic review of recent literature (2015-2024), we analyze and categorize various metaheuristic approaches, including Evolutionary Algorithms (EAs), swarm intelligence, and hybrid methods, that have been applied to address complex scheduling challenges in smart manufacturing environments. We specifically examine how these algorithms handle multiple competing objectives such as makespan minimization, energy efficiency, production costs, and human-machine collaboration, which are crucial in modern industrial settings. Our survey reveals several key findings: (1) hybrid metaheuristics demonstrate superior performance in handling multi-objective optimization compared to standalone algorithms; (2) bioinspired algorithms show promising results in addressing complex scheduling and multi-objective manufacturing environments; (3) tri-objective and higher-order multiobjective optimization problems warrant further in-depth exploration; and (4) there is an emerging trend towards incorporating human factors and sustainability objectives in scheduling optimization, aligned with Industry 5.0 principles. Additionally, we identify research gaps and propose future research directions, particularly in areas such as real-time scheduling adaptation, human-centric optimization, and sustainability-aware scheduling algorithms. This comprehensive review provides insights for researchers and practitioners in the field of industrial scheduling, offering a structured understanding of current methodologies and future challenges in the evolution from Industry 4.0 to 5.0.