<![CDATA[Production scheduling plays a critical role in improving efficiency, reducing makespan, and optimizing resource utilization in manufacturing systems. Conventional scheduling methods often fail to handle complex constraints, dynamic environments, and multi-objective requirements. This study examines the effectiveness of Genetic Algorithms in optimizing production scheduling across various manufacturing contexts. The research adopts a systematic literature-based analytical approach by synthesizing empirical findings from recent journal articles and conference proceedings published between 2021 and 2025. The analysis focuses on job shop, flow shop, flexible manufacturing systems, and reconfigurable manufacturing environments. The results show that Genetic Algorithms consistently outperform traditional heuristics and single-solution optimization methods in minimizing makespan, reducing lead time, improving machine utilization, and supporting adaptive scheduling under uncertainty. Hybrid models combining Genetic Algorithms with Simulated Annealing, Petri Nets, and simulation-based optimization demonstrate superior performance and robustness. The study confirms that Genetic Algorithms provide a scalable and flexible framework for production scheduling optimization in modern manufacturing systems. These findings contribute to decision support system development and provide guidance for future research on hybrid and data-driven scheduling models.]]>
Copyrights © 2025
