Genetic Algorithm-Based Optimization for Scheduling Problems

Abstract

Scheduling problems are critical in various domains, including manufacturing, cloud computing, and transportation. Traditional scheduling techniques often struggle to achieve optimal solutions due to computational complexity. Genetic Algorithms (GAs), inspired by natural selection, offer an effective soft computing approach for optimizing scheduling problems. This paper explores how GAs can improve scheduling efficiency, discusses real-world applications, and identifies challenges in implementing GA-based scheduling. Future research directions in hybrid AI models and adaptive GAs for dynamic scheduling environments are also highlighted.