Abstract

In the field of operational research optimizing complex systems and processes remains a critical challenge. This paper explores the application of Genetic Algorithms (GAs) as a robust tool for operational improvement. Genetic Algorithms inspired by the principles of natural evolution offer a heuristic approach to solving optimization problems by mimicking the process of natural selection. This study presents a comprehensive framework for integrating GAs into operational improvement strategies emphasizing their ability to handle multi-objective optimization adaptability to dynamic environments and effectiveness in navigating large search spaces. Through a series of case studies and simulations the paper demonstrates how GAs can be utilized to enhance operational efficiency reduce costs and improve decision-making across various industries. The results highlight the GAs potential as a versatile and powerful tool for addressing complex operational challenges providing valuable insights fo