摘要
为满足生产中的不同需求,以最小化完成时间、最小化工件总延期时间、最小化机器总空闲时间为目标函数,建立多目标优化模型。提出一种改进混沌烟花算法,通过逻辑自映射产生混沌序列避免算法陷入局部最优,并设计了一种双元锦标赛与动态淘汰制相结合的帕累托非劣解集的构造方法;最后用所提出的方法求解六个不同规模标准问题。实验结果表明,该算法在求解多目标作业车间问题时具有较高的求解精度和稳定性。
In order to meet the different needs of the production, this paper proposed a multi-objective optimization model with the objectives of minimizing the completion time, the jobs delay time, and the machine idle time. An improved chaotic fireworks algorithm came forward to solve this model. In this algorithm, it applied the self-logical mapping function to enhance the local search ability, and using the method of a binary tournament and the dynamical elimination process generated the set of the Pareto dominance solutions. Finally, the algorithm presented highly accuracy and robustness on solving the multi-objective Job-Shop scheduling problem.
作者
包晓晓
叶春明
计磊
黄霞
Bao Xiaoxiao;Ye Chunming;Ji Lei;Huang Xia(Business School, University of Shanghai for Science & Technology, Shanghai 200093 , China;College of Zhangjiagang, Jiangsu University of Science & Technology, Suzhou Jiangsu 215600 , China)
出处
《计算机应用研究》
CSCD
北大核心
2016年第9期2601-2605,共5页
Application Research of Computers
基金
国家自然科学基金资助项目(71271138)
上海市教育委员会科研创新项目(12ZS133)
上海市一流学科项目(S1201YLXK)
上海理工大学人文社科攀登计划资助项目(14XPB01)
关键词
作业车间调度
多目标优化
烟花算法
帕累托非劣解集
混沌搜索
Job-Shop scheduling
multi-objective optimization
firework algorithm
Pareto dominance
chaos search
