A new algorithm of the scheduling of a flexible manufacturing system based on genetic algorithm

Abstract

In the flexible manufacturing system, a reasonable production scheduling is crucial in shortening the processing completion time and improving the equipment utilization. Traditional manual scheduling cannot effectively solve the complex workshop scheduling problems and cannot provide a scheduling solution that meets the requirements in a short period of time, which can lead to a decrease in processing efficiency. Aiming at the complex job shop scheduling problem, the genetic algorithm is used to find the optimal scheduling solution in this study, taking the number of overdue jobs, the total overdue time, the job completion time, the comprehensive load rate and the maximum load rate of the machine tool as the performance indicators of the scheduling algorithm. The chromosomes are designed as process gene chain and equipment gene chain to improve the diversity and the robustness to scheduling problems of chromosome through crossover, variation, selection and other processes. The impact of different parameter settings on the performance indicators of each scheduling algorithm is researched by adjusting the four algorithm-related parameters, and there has been a certain improvement in the results of the scheduling problems. This study provides a reference for the design and optimization of production scheduling algorithm based on genetic algorithm.

Optimal Strategy of Unreliable Flexible Production System Using Information System

“…The integrated manufacturing system process significantly reduces assembly time for spherical bushels, enhancing control over production and assembly. Likewise, Bao et al [7] propose the use of a genetic algorithm to address complex job shop scheduling problems in the flexible production shop floor. They focus on optimizing scheduling solutions, considering key performance indicators such as overdue jobs, total overdue time, job completion time, comprehensive load rate, and maximum load rate of machine tools.…”