期刊文献+

基于网格搜索的船体不规则分段动态堆放方法 被引量:2

Approach on irregular block dynamic stacking in shipbuilding based on grid technology
在线阅读 下载PDF
导出
摘要 针对船体分段建造后在堆场中的空间调度问题,提出基于网格搜索的分段动态空间调度算法。首先,利用改进的粒子群算法产生多个可行的分段堆放序列;然后,采用基于网格搜索的定位策略对堆放序列进行空间布局解码。在解码过程中,运用位图对场地及投影多边形进行信息描述,快速寻找多边形的最佳定位位置。考虑分段堆放问题的动态性的时空关联性,同时以场地平均利用率和需要挪动的场地内分段总数的综合加权作为评价函数,利用改进的粒子群算法对方案进行择优,得到近似最优解,实现了堆放方案的全局优化。通过对船厂实际生产数据的实证分析以及堆放算法间的对比分析,结果证明,所提算法在综合评价场地利用率、移动分段数和运算效率的条件下是最优的。 In order to solve the problem of spatial scheduling of irregular blocks in stacking field after they were built, a dynamic stacking algorithm based on grid searching was proposed. An improved Particle Swarm Optimization (PSO) algorithm was used to determine the optimal processing sequence, and the locations of blocks were determined in spatial layout decoding by a dynamic location strategy based on grid technology. In the decoding process, bitmap was used to describe the information of yard and polygon and find the best location for every block. The space utilization and the quantity of blocks that need moving were used as the evaluation function, which fully considered the dynamic nature and the correlation between time and space in a stacking problem. Every particle in population was a stacking sequence, and the optimal solution could be found in the process of evolution by the improved PSO. Finally, the results of an actual data of a shipyard and the comparative analysis with other stacking algorithms show that the proposed algorithm is the best when comprehensively considering the space utilization, the number of movements of other blocks and efficiency of the algorithm,
出处 《计算机应用》 CSCD 北大核心 2013年第2期333-337,共5页 journal of Computer Applications
基金 国家自然科学基金资助项目(70871057 71171100)
关键词 动态堆放 网格搜索 不规则分段 粒子群算法 位图 dynamic stacking grid search irregular blocking Particle Swarm Optimization (PSO) bitmap
  • 相关文献

参考文献13

  • 1张志英,江志斌,陈强,孙嘉钧.曲面分段加工的虚拟流水线生产模式及关键技术探讨[J].中国造船,2005,46(3):112-116. 被引量:14
  • 2LEE K J,LEE J K,CHOI S Y. A spatial scheduling system and its application to shipbuilding:DAS-CURVE[J].Expert Systems With Applications,1996,(3/4):311-324.
  • 3SUNG C S,CHOUNG Y I. Minimizing makespan on a single bumin oven in semiconductor manufacturing[J].European Journal of Operational Research,2000,(03):559-574.
  • 4PAULUS J J,HURINK J. Adjacent-resource scheduling:why spatial resources are so hard to incorporate[J].Electronic Notes in Discrete Mathematics,2006,(01):113-116.
  • 5JEFFREY D C,MICHAEL J M,GEORGE G P. Scheduling parallel assembly workstations to minimize a shared pool of labor[J].IIE Transactions,2008,(08):749-758.
  • 6RAJ P,SRIVASTAVA R K. Analytical and heuristic approaches for solving the spatial scheduling problem[A].Piscataway(NJ):IEEE,2007.1093-1097.
  • 7张志英,陈洁.空间调度问题的非线性规划分析求解方法[J].计算机集成制造系统,2010,16(6):1272-1278. 被引量:7
  • 8CHO K K,LEE D,OUM T H. An intelligent spatial planning and scheduling system[J].International Journal of Industrial Engineering:Theory Applications and Practice,2002,(02):133-140.
  • 9VARGHESE R,DUCK Y Y. Dynamic spatial block arrangement scheduling in shipbuilding industry using genetic algorithm[A].Piscataway(NJ):IEEE,2005.444-449.
  • 10SHIN J G,KWON O H,RYU C. Heuristic and metaheuristic spatial planning of assembly blocks with process schedules in an assembly shop using differential evolution[J].Production Planning and Control,2008,(06):605-615.doi:10.1080/09537280802474941.

二级参考文献37

  • 1高尚,杨静宇,吴小俊,刘同明.基于模拟退火算法思想的粒子群优化算法[J].计算机应用与软件,2005,22(1):103-104. 被引量:51
  • 2应长春,徐学光.论现代造船体制的特征和实践[J].中国造船,1995,36(2):1-11. 被引量:12
  • 3陈贵敏,贾建援,韩琪.粒子群优化算法的惯性权值递减策略研究[J].西安交通大学学报,2006,40(1):53-56. 被引量:317
  • 4满春涛,孙明辉,张礼勇.粒子群优化算法在多峰函数寻优上的应用[J].哈尔滨理工大学学报,2007,12(2):11-13. 被引量:6
  • 5LEE K J, LEE J K,CHOI S Y. A spatial scheduling system and its application to shipbuilding : DAS-CURVE [ J ]. Expert Systems with Applications, 1996,10 ( 3/4 ) : 311-324.
  • 6RAJ P. Solving spatial scheduling problem: an analytical approach[ C ]//Proceedings of the 37th International Conference on Computers and Industrial Engineering. Alexandria, Egypt, 2007:2002-2011.
  • 7ZHENG Junli, JIANG Zhibin. Minimizing makespan at module assembly shop in shipbuilding[ C ]//Proceedings of 2008 IEEE International Conference on Service Operations and Logistics, and Informatics. Beijing, China, 2008: 1794-1799.
  • 8MIN S G, LEE M W. A genetic algorithm application for the load balancing of ship erection process [J]. IE Interfaces, 2000,13 ( 2 ) : 225-233.
  • 9LI Bo, ZHAO Z Y. A dynamic scheduling method for spatial layout planning [ C ]// Proceedings of the Fourth International Conference on Machine Learning and Cybernetics. Guangzhou, China, 2005:3612-3617.
  • 10EBERHART R C, KENNEDY J. A new optimizer using particles swarm theory [ C ]//Proceedings 6th International Symposium on Micro Machine and Human Science. Nagoya, Japan, 1995:39-43.

共引文献33

同被引文献20

  • 1罗瑜,李涛,王丹琛,何大可.支持向量机中核函数的性能评价策略[J].计算机工程,2007,33(19):186-187. 被引量:4
  • 2Smruti Rekha Das, Kaberi Das, Debahuti Mishra, et al. An empirical comparison study on kernel based support vector machine for classification of gene expression data set [ J]. Procedia Engineering, 2012,38 : 1340-1345.
  • 3Bai Liming, Lin Hui, Sun /-Iua, et al. Remotely sensed percent tree cover mapping using support vector machine combined with autonomous endmember extraction [ J ]. Physics Procedia, 2012,33 : 1702-1709.
  • 4Shrime A E, Yousefi M. Recognition of the communication signals using particle swarm optimization and support vector machine based on the multi-resolution wavelet analysis [ J ]. Wireless Personal Communications, 2012, 63 ( 4 ) : 847- 860.
  • 5Li D S, He Q. A version of cooperative multi-swarm PSO using electoral mechanism to solve hybrid flow shop sched-uling problem[ J]. Przegland Elektrotechniczny, 2012,88 (5b) :22-26.
  • 6Xu Z Q, Li P W, Wang Y X. Text classifier based on an improved SVM decision tree[J]. Physics Procedia, 2012, 33 : 1986-1991.
  • 7Qi Z Q, Tian Y J, Shi Y. Structural twin support vector machine for classification [ J ]. Knowledge-Based Systems, 2013 ,g3:74-81.
  • 8Chu W, Gao X G, Soroosh S. Handling boundary con- straints for particle swarm optimization in high-dimensional search space[J]. Information Sciences, 2011,181 (20) : 4569-4581.
  • 9Liang J J, Qin A K, Suganthan P N, et al. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions [ J ]. IEEE Transactions on Evolution- ary Computation, 2006,10 ( 3 ) : 281-295.
  • 10司利云,林辉.支持向量回归机模型结构及性能的研究[J].计算机工程与应用,2008,44(32):53-56. 被引量:2

引证文献2

二级引证文献6

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