Application of Genetic Algorithm to Optimize Bicycle Lighting Order Scheduling Taking Company E as an Example

In recent years, as the Covid-19 pandemic has eased, people have increasingly turned to outdoor activities, with cycling becoming a popular choice among the public. However, riding bicycles at night can lead to accidents due to the difficulty of seeing obstacles in front, posing significant traffic safety concerns. To avoid such incidents, it is essential to equip bicycles with lights, leading to an increased demand for bicycle lights. To ensure customers receive their ordered light bulbs on time, this study focuses on sorting the orders for bicycle lights from the production company, aiming to minimize the total delay time. The production process follows a streamlined approach, making it crucial to adjust the order sequence to reduce processing time and minimize delays. To achieve optimization, this research employs a genetic algorithm with parallelization and weighted factors. The inclusion of weighted factors allows for better prioritization of orders to obtain the shortest delay time. The importance of each order is assessed using the Analytic Hierarchy Process (AHP). Additionally, Taguchi experiments are utilized to obtain the best parameter solution for the shortest delay time, which is then applied in the program. The results demonstrate that the improved genetic algorithm outperforms both the standard genetic algorithm and the company's previous methods, achieving a more effective reduction in the total delay time.