摘要
基于合理抽象虚拟企业模型和任务模型,建立虚拟企业生产任务计划的数学模型,并提出了一种时间与费用双优化的调度算法.该算法分别针对虚拟企业生产调度的时间与费用2个目标提出启发式优化算法,并以调度优化的结果作为遗传算法的初始染色体,通过对遗传算法运算的重新定义来优化虚拟企业生产计划调度,充分发挥遗传算法良好的全局搜索能力和能有效避免陷入局部极小的优点,提高了算法的全局寻优能力.实验结果表明,启发式优化算法与遗传算法相结合的优化技术能够降低虚拟企业生产费用,使企业具有较好的生产敏捷性.
A mathematical model for a virtual enterprise production plan was established based on a reasonable Abstract virtual enterprise model and the task model.A scheduling algorithm which aims at the dual optimization of time and cost was proposed.Two heuristic optimization algorithms are proposed and the output is used as the initial chromosome of a genetic algorithm.The genetic algorithm is redefined to optimize the scheduling of the virtual enterprise＇s production planning.Hence the global searching and local minimum avoidance capabilities of genetic algorithms are exploited to improve the global optimization capacity of the proposed algorithm.The experimental results show that the proposed methodology to combine the heuristic optimization algorithm with genetic algorithm is capable of reducing production cost and promoting agile manufacturing.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2011年第12期1812-1818,共7页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金资助项目(60873081)
新世纪优秀人才计划(NCET-10-078)
湖南省杰出青年基金资助项目(11JJ1012)
关键词
虚拟企业
生产计划
遗传算法
动态关键路径
费用最小
virtual enterprise
production planning
genetic algorithm
dynamic critical path
minimum cost
