多目标柔性作业车间调度的多交叉策略元胞进化算法

发布时间：2018-03-10 19:42

本文选题：柔性车间调度　切入点：元胞结构　出处：《科学技术与工程》2017年07期 　论文类型：期刊论文

【摘要】：针对经典Job-shop调度问题的局限性,构建了以加工成本、瓶颈机器负荷、机器总负荷及制造工期为目标函数的柔性作业车间调度多目标优化模型,提出了基于多交叉策略的元胞多目标遗传算法。在分析优化模型的基础上,使用双层编码方式,并采用多个交叉算子协同进化,提出一种多交叉策略的进化算子。针对元胞多目标遗传算法的特点,提出一种改进的精英策略,保证更多的精英个体参与进化,从而提升算法收敛速度。通过2个基准实例求解对比分析,表明所提方法的有效性。将新算法应用于实际生产企业的车间调度问题中,得到了一组Pareto解集,并采用层次分析法得到一种满意度最大的方案。数据结果表明,该算法在解决多目标FJSP的工程有效性。
[Abstract]:Aiming at the limitation of classical Job-shop scheduling problem, a multi-objective optimization model of flexible job shop scheduling is constructed, which takes machining cost, bottleneck machine load, total machine load and manufacturing time as objective functions. A cellular multi-objective genetic algorithm based on multi-crossover strategy is proposed. Based on the analysis of the optimization model, a double-layer coding method is used and multiple crossover operators are used to co-evolve. An evolutionary operator with multiple crossover strategies is proposed. According to the characteristics of cellular multi-objective genetic algorithm, an improved elite strategy is proposed to ensure that more elite individuals participate in evolution. In order to improve the convergence speed of the algorithm, the comparison and analysis of two benchmark examples show the effectiveness of the proposed method. A set of Pareto solutions is obtained by applying the new algorithm to the job shop scheduling problem of actual production enterprises. The analytic hierarchy process (AHP) is used to obtain a scheme with the highest degree of satisfaction. The results show that the algorithm is effective in solving multi-objective FJSP.
【作者单位】：桂林电子科技大学教学实践部;
【基金】：国家自然科学基金(61261017) 广西无线宽带通信与信号处理重点实验室项目(GXKL061501) 广西教育厅广西高校科学技术研究项目(KY2015ZL040)资助
【分类号】：TP18;TB497

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