An application of genetic algorithm into order scheduling of a textile company

Scheduling not only poses a significant complexity problem in manufacturing inside each of enterprise but extending to the competitive landscape among enterprises as well. The footwear industry, particularly in the field of accessories for shoe production, is no exception to this challenge. This research paper addresses scheduling issues that arising in the dyeing plant, for the product of shoelaces with various restrictions such as color, machines resource and the demand uncertainty. By emerged the Genetic Algorithm (GA) as modeling evolutionary system, incorporating the two-point crossover, swap mutation operators, and k-tournament selection, which are the operators that mainly used in the job shop and flow shop environment, to the scheduling of machine in parallel environment. The purpose of such the application is to find the efficient allocation of jobs, focus on addressing order lateness by minimizing the total completion time of all batches. The optimization results demonstrate the schedule of a one-day scenario production, providing practical insights into the benefits of GA when comparing to the MILP and current scheduling method of the company. Overall, the research contributes valuable findings to the field of textile dyeing scheduling, offering a robust solution to enhance production efficiency, resource utilization, and order sequencing in the footwear auxiliary enterprise.