Involvement of Various Selection Methods for Genetic Algorithms in Determining the Optimal Production Schedule Problem

Abstract

This research investigates the use of genetic algorithms (GA) for optimizing production scheduling in Medan's shoe industry. The study compares traditional manual and First Come First Serve (FCFS) methods against a GA approach, incorporating selection variations such as Boltzmann, Fitness Uniform Selection Scheme (FUSS), Exponential Rank Selection, and Roulette Wheel Selection. The optimal production order is derived from the chromosome with the highest fitness. Results indicate that GA with FUSS selection significantly reduces production time from 73,630 minutes to 45,650 minutes, achieving a 35% improvement in efficiency. This optimization is attributed to FUSS’s ability to maintain a diverse population, preventing premature convergence and ensuring a broader exploration of the solution space. Additionally, it was found that using a smaller population size relative to the number of generations yields better optimization results. The study also demonstrates that while Roulette Wheel Selection shows more variability, it ultimately achieves higher optimization over time compared to FCFS. The practical implications of these findings are substantial for the shoe industry, including faster production cycles, better resource allocation, and an enhanced ability to meet customer demands. These benefits are exemplified by the implementation of the SISPROMA application, an innovative production scheduling information system that leverages machine learning to optimize scheduling in the manufacturing industry. This study provides valuable insights into the application of genetic algorithms for production scheduling, highlighting their potential to enhance operational efficiency and reduce costs. Future research should explore additional optimization techniques and real-world applications to validate and extend these findings, ensuring broader applicability and continuous improvements in manufacturing efficiency.