基于差分进化算法多目标作业车间调度研究  

Multi-Objective Job Shop Scheduling Based on Differential Evolution Algorithm

作　　者：张伟[1] ZHANG Wei(Institute of General Aviation Industry,Fujian Chuanzheng Communications College,Fuzhou 350007,Fujian China)

机构地区：[1]福建船政交通职业学院通用航空产业学院,福建福州350007

出　　处：《贵阳学院学报（自然科学版）》2021年第3期73-76,共4页Journal of Guiyang University：Natural Sciences

基　　金：福建省教育厅中青年科研项目“基于eM-Plant的生产线系统的仿真研究”(项目编号:JAT191176)。

摘　　要：为提高多目标车间的工作效率,提出基于差分进化算法多目标作业车间调度研究。首先以经济作为核心目标,通过对时间成本、机械成本、不合格成本进行约束,建立了多目标调度模型,根据模型得出的调度结果,建立个体种群,通过参数自适应实现对调度参数的优化,以差分进化的方式实现最终的最优解收敛,并将其作为调度结果输出。通过试验测试了所提方法的性能。试验结果表明,在试验环境下,所提方法可有效降低时间开销和机械开销,且不合格率基本稳定在3%左右,具有良好的调度效果,对实际的多目标作业调度工作具有一定的参考价值。In order to improve the efficiency of multi-objective job shop,a multi-objective job shop scheduling method based on differential evolution algorithm is proposed.Firstly,taking economy as the core objective,a multi-objective scheduling model is established by constraining the time cost,machinery cost and unqualified cost.According to the scheduling results obtained from the model,individual population is established,and the scheduling parameters are optimized by parameter adaptation.The final optimal solution convergence is realized by means of differential evolution,and it is output as the scheduling result.The performance of the proposed method is tested through experiments.The experimental results show that the proposed method can effectively reduce the time and mechanical costs in the experimental environment,and the unqualified rate is basically stable at about 3%.It has good scheduling effect,and has a certain reference value for the actual multi-objective job scheduling.

关 键 词：差分进化算法 多目标调度 成本约束 参数自适应 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]