Optimizing Distribution of Print Media Using Metaheuristic Algorithms

Efficient distribution of newspapers and magazines is crucial to ensure punctual and cost-effective delivery. Presently, the distribution of print media lacks organization. In this study, we introduce an innovative approach to enhance this process by leveraging advanced metaheuristic algorithms. Beyond conventional methodologies, we explore the potential of Genetic Algorithms, Hill Climbing Algorithms, and Simulated Annealing, providing a comparative analysis of their efficacy. Our research demonstrates that these advanced techniques can significantly improve traditional strategies, offering valuable insights into the transformation of distribution methods. When comparing various optimization techniques, our findings indicate that simulated annealing stands out as the most suitable method for addressing our problem statement.