期刊文献+

基于布谷鸟算法的压缩机装配调度项目优化方法 被引量:2

Compressor Assembly Project Scheduling OptimizationMethod Based on Cuckoo Algorithm
在线阅读 下载PDF
导出
摘要 针对制造型企业生产项目调度优化,提出一种基于自适应布谷鸟算法的调度策略。该策略采用全局搜索能力强的布谷鸟算法为框架模型,以任务调度顺序优先级编码,对影响算法性能的重要参数——步长因子设定动态自适应策略,提升算法的运算性能和全局收敛速度。以跨国S公司大型空气压缩机装配项目为例进行调度优化,结果表明该调度方案拥有更短的工期和平衡的资源利用率。此外,通过自动生成的人力配置甘特图,项目经理能够动态调整人员配置以减少人力资源的浪费。 For the optimization of production project scheduling in manufacturing enterprises,a scheduling strategy based on adaptive cuckoo algorithm is proposed.In the strategy,the cuckoo algorithm with strong global search ability is adopted as framework model.The priority is given to task scheduling order,and the important parameter step factor affecting the performance of the algorithm is set for the dynamic adaptive strategy which is used to improve the computing performance and global convergence speed of the algorithm.This paper takes the large-scale air compressor assembly project of multinational company as an example.The results show that the scheduling scheme can be used to shorten the construction period and balance the resource utilization.In addition,with the manpower configuration Gantt chart automatically generated,the project manager can dynamically adjust staff,thus reducing the waste of human resources.
作者 曹圣武 陈再良 CAO Shengwu;CHEN Zailiang(School of Mechanical and Electrical Engineering,Soochow University,Suzhou 215000,China)
出处 《机械制造与自动化》 2020年第4期84-87,共4页 Machine Building & Automation
基金 国家自然科学基金(51475313) 江苏省科技厅资助项目(BY2016043-02,BA2014004)。
关键词 生产项目调度 布谷鸟算法 自适应步长 最小化工期 production project scheduling cuckoo algorithm adaptive stepsize project makespan optimization
  • 相关文献

参考文献4

二级参考文献39

  • 1杨波,万仲平,尹德玉.资源约束项目排序问题的一种修正蚁群算法[J].工程数学学报,2007,24(3):437-445. 被引量:4
  • 2Zheng X L, Wang L, Wang S Y. A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem[J]. Knowledge-Based Systems, 2014, 57(1): 95-103.
  • 3Wang L, Fang C, Suganthan P N, et al. Solving system-level synthesis problem by a multi-objective estimation of distribution algorithm[J]. Expert Systems with Applications, 2014, 41(5): 2496-2513.
  • 4Chen W N, Zhang J. Ant colony optimization for software project scheduling and staffing with an event?based scheduler[J]. IEEE Trans on Software Engineering, 2013,39(1): 1-17.
  • 5Blazewicz J, Lenstra J K, Kan A H G. Scheduling subject to resource constraints: Classification and complexity[J]. Discrete Applied Mathematics, 1983,5(1): 11-24.
  • 6Hartmann S, Kolisch R. Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem[J]. European J of Operational Research, 2000, 127(2): 394-407.
  • 7Kolisch R, Hartmann S. Experimental investigation of heuristics for resource-constrained project scheduling: An update[J]. European J of Operational Research, 2006, 174(1): 23-37.
  • 8AI-Fawzan M A, Haouari M. A bi-objective model for robust resource-constrained project scheduling[J]. Int J of Production Economics, 2005, 96(2): 175-187.
  • 9Pollack-Johnson B, Liberatore M J. Incorporating quality considerations into project time/cost tradeoff analysis and decision making[J]. IEEE Trans on Engineering Management, 2006, 53(4): 534-542.
  • 10Mokhtari H, Baradaran Kazemzadeh R, Salmasnia A. Time-cost tradeoff analysis in project management: An ant system approach[J]. IEEE Trans on Engineering Management, 2011, 58(1): 36-43.

共引文献114

同被引文献26

引证文献2

二级引证文献4

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