全部 标题 作者
关键词 摘要


基于遗传算法的试验计划总完工时间极小化模型设计与实现
Design and Implementation of the Total Completion Time Minimization Model of Test Plan Based on Genetic Algorithm

DOI: 10.12677/HJDM.2016.63014, PP. 116-124

Keywords: 遗传算法,总完工时间,极小化,试验检测,试验计划调度
Genetic Algorithm
, The Total Completion Time, Minimization, Test and Detection

Full-Text   Cite this paper   Add to My Lib

Abstract:

伴随着物联网技术和大数据分析技术的兴起,越来越多的企业由传统制造业向智能化转型,以实现产业升级,而总完工时间极小化,尤其是复杂产品的总完工时间极小化,是制造企业生产计划编制中的重要环节。若能实现总完工时间极小化模型的准确建立,既能改善计划的准确性也可以大幅度提高检测业务的工作效率。本文提出的试验检测计划的总完工时间极小化方法,经算法实现后得到的试验检测方案能够有效的提高产品的试验检测效率,缩短产品的试验检测周期。经实例验证,有良好的应用效果。
With the rise of the Internet of things and big data analysis technology, more and more enterprises transform from the traditional manufacturing industry to intellectualization to achieve industrial upgrading. For complex products, total completion time minimization is an important part of production planning in manufacturing enterprises. If we can establish the total completion time minimization model accurately, both of the program accuracy and working efficiency can be greatly improved. The proposed method of test and detection plan to minimize the total completion time can improve the test and detection efficiency and shorten the test cycle. Verified by examples, it has good application effect.

References

[1]  许云江. 高压电气试验设备现状及技术优化[J]. 科技创业家, 2013(22): 110.
[2]  赵东野. 高压电气试验设备与技术问题研究[J]. 科技致富向导, 2014(12): 215.
[3]  魏巍, 谭建荣, 冯毅雄, 张蕊. 柔性工作车间调度问题的多目标优化方法研究[J]. 计算机集成制造系统, 2009, 15(8): 1592-1599.
[4]  Allahverdi, A. and Aydilek, H. (2013) Algorithms for No-Wait Flowshops with Total Completion Time Subject to Makespan. International Journal of Advanced Manufacturing Technology, 68, 2237-2251. http://dx.doi.org/10.1007/s00170-013-4836-x
[5]  田乐, 赵传立. 极小化总完工时间的同时加工排序[J]. 数学的实践与认识, 2009, 39(20): 100-105.
[6]  许瑞, 陈华平, 邵浩, 王栓狮. 极小化总完工时间批调度问题的两种蚁群算法[J]. 计算机集成制造系统, 2010, 16(6): 1255-1264.
[7]  Damodaran, P., Vélez-Gallego, M.C. and Maya, J. (2011) A GRASP appr#oach for Makespan Minimization on Parallel Batch Processing Machines. Journal of Intelligent Manufacturing, 22, 767-777. http://dx.doi.org/10.1007/s10845-009-0272-z
[8]  Damodaran, P. and Vélez-Gallego, M.C. (2012) A Simulated Annealing Algorithm to Minimize Makespan of Parallel Batch Processing Machines with Unequal Job Ready Times. Expert Systems with Applications, 39, 1451-1458. http://dx.doi.org/10.1016/j.eswa.2011.08.029
[9]  Kim, M.-Y. and Lee, Y.H. (2012) MIP Models and Hybrid Algorithm for Mini-mizing the Makespan of Parallel Machines Scheduling Problem with a Single Server. Computers & Operations Research, 39, 2457-2468. http://dx.doi.org/10.1016/j.cor.2011.12.011
[10]  Allahverdi, A. and Aydilek, H. (2014) Total Completion Time with Makespan Constraint in No-Wait Flowshops with Setup Times. European Journal of Operational Research, 238, 724-734. http://dx.doi.org/10.1016/j.ejor.2014.04.031
[11]  Al-Anzi, F.S. and Allahverdi, A. (2013) An Artificial Immune System Heuristic for Two-Stage Multi-Machineas- sembly Scheduling Problem to Minimize Total Completion Time. Journal of Manufacturing Systems, 32, 825-830. http://dx.doi.org/10.1016/j.jmsy.2013.06.001
[12]  Xiong, F.L., Xing, K.Y. and Wang, F. (2015) Scheduling a Hybrid Assem-bly-Differentiation Flowshop Tominimizetotal Flow Time. European Journal of Operational Research, 240, 338-354. http://dx.doi.org/10.1016/j.ejor.2014.07.004
[13]  Muthuswamy, S., Vélez-Gallego, M.C., Maya, J. and Rojas-Santiago, M. (2012) Minimizing Makespan in a Two- Machine No-Wait Flow Shop with Batch Processing Machines. The International Journal of Ad-vanced Manufacturing Technology, 63, 281-290. http://dx.doi.org/10.1007/s00170-012-3906-9
[14]  Rahmati, S.H.A. and Zandieh, M. (2012) A New Biogeography-Based Optimization (BBO) Algorithm for the Flexible Job Shop Scheduling Problem. The International Journal of Advanced Manufacturing Technology, 58, 1115-1129. http://dx.doi.org/10.1007/s00170-011-3437-9
[15]  Gómez-Gasquet, P., Andres, C. and Lario, F.C. (2012) An Agent-Based Genetic Algorithm for Hybrid Flow Shops with Sequence Dependent Setup Times to Minimise Makespan. Expert Systems with Applications, 39, 8095-8107. http://dx.doi.org/10.1016/j.eswa.2012.01.158
[16]  Dorndorf, U., Pesch, E. and Phan-Huy, T. (2000) Constraint Propagation Tech-niques for Disjunctive Scheduling Problems. Artificial Intelligence, 122, 189-240. http://dx.doi.org/10.1016/S0004-3702(00)00040-0
[17]  Hajri, S., Liouane, N., Hammadi, S. and Borne, P. (2000) A Controlled Genetic Algorithm by Fuzzy Logic and Belief Functions for Job-Shop Scheduling. IEEE Transactions on Systems, Man, and Cyber-netics, 30, 812-818. http://dx.doi.org/10.1109/3477.875454
[18]  王伟玲, 李俊芳, 王晶. 求解多目标作业车间调度问题的双种群遗传算法[J]. 计算机集成制造系统, 2011, 17(04): 808-815.
[19]  赵诗奎, 方水良, 顾新建. 柔性车间调度的新型初始机制遗传算法[J]. 浙江大学学报(工学版), 2013, 47(6): 1022- 1030.
[20]  彭运芳, 高雅, 夏蓓鑫. 不确定条件下基于遗传算法的作业车间调度问题研究[J]. 上海大学学报(自然科学版), 2016: 1-12.
[21]  张腾飞, 马跃, 胡毅, 安涛, 王帅, 郭安. 基于遗传算法的多目标车间调度问题的研究[J]. 组合机床与自动化加工技术, 2016(05): 43-45.
[22]  裴振江, 姚斯立, 王建生, 李鹏. 西安高压电器研究所试验能力和设备配置[J]. 电力设备, 2005, 6(4): 108-110.
[23]  王凌. 车间调度及其遗传算法[M]. 北京: 清华大学出版社, 2003.

Full-Text

comments powered by Disqus