基于改进遗传算法的柔性作业车间调度优化

在实际生产中，加工成本愈发成为企业关注的重要因素。本文对以最小化加工成本与完工时间为目标的柔性车间调度问题进行了研究。首先根据实际约束构建调度模型，提出改进遗传算法对模型进行求解，引入质量基因段来增强对染色体适应度值的评价，加速淘汰质量差的个体。为了优化求解质量，提出了基于整体负荷最小与局部负荷最小的种群初始化方法，并设计了精确变异机制来维持种群多样性。最后用标准算例进行测试，相比于其他改进遗传算法，求解速度得到提高，求解质量也得到了提升，验证了此改进遗传算法的有效性。

A Flexible Job Shop Scheduling Optimization Based on Improved Genetic Algorithm

In the actual production process, it has increasingly become an important factor for companies to pay attention to of the processing cost. In this paper, the flexible job-shop scheduling problem aiming at minimizing processing cost and makespan is studied. Firstly, a scheduling model based on actual constraints is constructed, and the improved genetic algorithm is purposed to solve the model. This algorithm introduces quality gene segment to enhance the evaluation of chromosome fitness values, which accelerates the elimination of poor quality chromosomes. A population initialization method based on the minimum overall load and the minimum local load is proposed for quality optimization of the chromosomes, and a precise mutation mechanism is designed to maintain the diversity of the population. Finally, the improved genetic algorithm is tested on a set of benchmark instance taken from the literature and compared with other approaches. The results demonstrate that the proposed algorithm outperforms others in terms of convergence speed and accuracy, which verify the effectiveness.