期刊文献+

基于粒子群的多目标多执行模式项目调度 被引量:2

Multi-objective and Multi-mode Project Scheduling Problem Based on Particle Swarm Optimization
下载PDF
导出
摘要 聚焦多目标多执行模式特点下的项目调度问题,通过建立工期、费用、资源和质量多目标函数,构建综合优化模型,同时运用粒子群算法解决工程项目多目标多执行模式优化问题.最后,通过一个应用实例计算,表明粒子群算法可以准确快速地解决该模型下的工程项目多目标优化问题,达到了项目调度中面对不同模式进行抉择,并且缩短工期、减少成本、均衡资源以及提升质量的综合的理想效果. Multi-objective and multi-mode project scheduling characteristics were mainly focused. A comprehensive optimization model was built by establishing time,expenses,resources and quality objective functions, and a particle swarm optimization was made to solve the multi-objective and multi-mode project scheduling problems. Through an application example, it is proved that the particle swarm algorithm can solve the multi-objective optimization problems by this model accurately and rapidly, and gain the ideal effect on different mode selection, construction period cut,cost reduction,quality improvement and resources balance in the project scheduling.
作者 周蓉 叶春明
出处 《上海理工大学学报》 CAS 北大核心 2013年第1期27-32,81,共7页 Journal of University of Shanghai For Science and Technology
基金 国家自然科学基金资助项目(71271138) 教育部人文社会科学规划基金资助项目(10YJA630187) 高等学校博士点基金资助项目(20093120110008) 上海研究生创新基金资助项目(JWCXSL1102) 上海市教育委员会科研创新资助项目(12ZS133)
关键词 项目调度 多目标 多执行模式 粒子群算法 project scheduling multi-objective multi, mode particle swarm optimization
  • 相关文献

参考文献14

二级参考文献76

共引文献660

同被引文献29

  • 1王存睿,段晓东,刘向东,周福才.改进的基本粒子群优化算法[J].计算机工程,2004,30(21):35-37. 被引量:43
  • 2Sarker R,Omar M.Hybrid evolutionary algorithm for job scheduling under machine maintenance[J].Applied Soft Computing Journal,2013,13(3):1440-1447.
  • 3Khalid H,Beatrix B.Integration of products expiry dates in optimal scheduling of storage/retrieval operations for a flow-rack AS/RS[J].International Journal of Industrial and Systems Engineering,2013,15(2):216-233.
  • 4Kennedy J,Eberhart R.Particle swarm optimization[C]//Proceedings of IEEE International Conference on Neural Networks.Portland:IEEE,2003.
  • 5Eberhart R,Kennedy J.A new optimizer using particle swarm theory[C]//Proceedings of the Sixth International Symposium on Micro Machine and Human Science.Nagoya:IEEE,1995.
  • 6Beekman M,Ratnieks F L W.Long-rang foraging by the honey-bee[J].Functional Ecology,2000,14(4):490-496.
  • 7Ghodrati A,Lotfi S.A hybrid CS/PSO algorithm for global optimization[J].Intelligent Information and Database Systems,2012,7198(2):89-98.
  • 8Liu Z H,Wang X L.A PSO-based algorithm for load balancing in virtual machines of cloud computing environment[J].Advances in Swarm Intelligence,2012,7331(4):142-147.
  • 9El-Sherbiny M M,Alhamali R M.A hybrid particle swarm algorithm with artificial immune learning for solving the fixed charge transportation problem[J].Computers & Industrial Engineering,2013,64(2):610-620.
  • 10Hamta N,FatemiGhomi S M T,Jolai F,et al.A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times,sequence-dependent setup times and learning effect[J].International Journal of Production Economics,2013,141(1):99-111.

引证文献2

二级引证文献14

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部