Abstract

Operational improvement has become a critical requirement in today’s competitive data-driven and rapidly evolving technological landscape. Organizations across industries—from manufacturing and logistics to finance healthcare and information technology—seek optimization strategies that enhance performance reduce costs eliminate inefficiencies and improve decision-making under complex constraints. Genetic Algorithms (GAs) inspired by the process of natural selection and biological evolution have emerged as one of the most powerful metaheuristic techniques capable of solving large-scale optimization problems. GAs excel in environments where traditional optimization approaches struggle particularly in nonlinear multi-dimensional multi-objective and NP-hard situations. This research paper examines the genetic algorithm as a strategic tool for operational improvement focusing on its theoretical foundations practical applications algorithmic structure and performance advantages. It evaluates