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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) JOIV : International Journal on Informatics Visualization Jurnal Rekayasa Material, Manufaktur & Energi Jurnal Sistem Teknik Industri INCODING: Journal of Informatics and Computer Science Engineering IRA Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Teknik Mesin & Industri (JITMI)
Silviana, Nukhe Andri
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Other

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Journal : JOIV : International Journal on Informatics Visualization

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Involvement of Various Selection Methods for Genetic Algorithms in Determining the Optimal Production Schedule Problem Muliono, Rizki; Silviana, Nukhe Andri; Novita, Nanda
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2632

Abstract

This research investigates using genetic algorithms (GA) to optimize 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 solution for space exploration. 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 achieves higher optimization over time than 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 implementing 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 applying 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.
Co-Authors Abdul Rahim Matondang Anangjasa, Trinaldo Sihotang Habib Satria Haniza Harahap, Azdi Rihadi Juliza Hidayati Khairuddin - Munte, Sirmas Nainggolan, Lorena Nanda Novita, Nanda ninny siregar, ninny Pasaribu, Rismauli Novianty Rizki Muliono, Rizki Sari, Hesti Indah Susilawati . Sutrisno, Nos Sutrisno, Sutrisno Syafii . Syah, Rahmad B.Y