摘要
针对多项目调度资源利用率低的问题,提出任务可拆分的多模式多项目调度模型。采用多属性效用函数对工期—成本—质量—资源均衡进行目标优化,以提高资源利用率、缩短工期,实现多项目调度整体效用的最大化。利用正态云模型云滴的随机性和稳定性的特征,设计云遗传算法并生成多项目调度各个活动的优先级,最终生成活动可拆分的多模式多项目调度计划。通过算例验证了所提模型和算法的有效性。
Aiming at the low utilization rate of resources in multi-project scheduling, the multbmode and multi project scheduling model with activity splitting was proposed. Duration-cost-quality-resources were optimized comprehensively by multi-attribute utility function to enhance utilization rate, shorten duration and realize the maximization of total utility in multi-project scheduling. Randomness and stability of cloud droplet in Normal Cloud Model was used for designing cloud genetic algorithm to generate the priority of activities and scheduling plan of multi-mode multi- project with activities splitting. The effectiveness of the model and algorithm were verified by a case study.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2014年第6期1388-1397,共10页
Computer Integrated Manufacturing Systems
基金
高等学校博士学科点专项科研基金资助项目(20135522120002)
重庆市社会科学规划资助项目(2013YBGL130)
国家自然科学青年基金资助项目(71301179)~~
关键词
多项目调度
多模式
云遗传算法
任务可拆分
multi-project scheduling
multi-mode
cloud genetic algorithm
activity splitting
