Hiace Transportation Departure Scheduling Information System in Lhokseumawe With Genetic Algorithm - Taskia, Narita; Dinata, Rozzi Kesuma; Retno, Sujacka (International Journal of Engineering, Science and Information Technology, 2025)
This study aims to address challenges in transportation scheduling by employing a suitable algorithm to ensure the scheduling process operates efficiently and effectively. One algorithm identified as appropriate for this task is the Genetic Algorithm, which is widely recognized for its robust capabilities in optimization tasks. Known for its adaptability and robustness, the Genetic Algorithm is well-suited for scheduling applications, including academic timetabling, as it can handle complex problems involving multiple criteria and objectives. Inspired by principles of biological evolution and natural selection, this algorithm iteratively explores solutions to approach optimal outcomes, refining the schedule in each iteration until an effective solution is achieved. Based on the analysis of experimental results using real-world data and evaluation of the system's design, the study concludes that the Hiace transportation departure scheduling system was successfully developed using a web-based approach. This web-based system offers significant advantages, as it facilitates more efficient management of departure schedules and eliminates the need for manual checks. As a result, it reduces the risk of human error and allows for better resource allocation. The integration of Genetic Algorithms into the development of the Hiace transportation scheduling system demonstrates the potential of evolutionary computation in solving practical, real-life scheduling problems. The resulting system is supported by internet-based technologies, providing easy access to passengers and system administrators. Despite the positive outcomes achieved, the current implementation is not without limitations. Further refinement and continued development are essential to enhance system performance, increase reliability, and ensure it can adapt to evolving needs and operational complexities, ensuring its long-term effectiveness.