[1]吴昌钱,黄 锐,罗志伟.基于量子蚁群算法的智能制造调度问题研究[J].南京师大学报(自然科学版),2023,46(04):74-79.[doi:10.3969/j.issn.1001-4616.2023.04.011]
　Wu Changqian,Huang Rui,Luo Zhiwei.Research on Intelligent Manufacturing Scheduling Problem Based on Quantum Ant Colony Algorithm[J].Journal of Nanjing Normal University(Natural Science Edition),2023,46(04):74-79.[doi:10.3969/j.issn.1001-4616.2023.04.011]
点击复制

基于量子蚁群算法的智能制造调度问题研究()
分享到：

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第46卷
期数:
2023年04期
页码:
74-79
栏目:
计算机科学与技术
出版日期:
2023-12-15

文章信息/Info

Title:
Research on Intelligent Manufacturing Scheduling Problem Based on Quantum Ant Colony Algorithm
文章编号:
1001-4616(2023)04-0074-06
作者:
吴昌钱1; 黄 锐2; 罗志伟3
(1.闽南科技学院计算机信息学院,福建 泉州 366200)
(2.北京理工大学计算机学院,北京 100081)
(3.厦门大学机电学院,福建 厦门 361000)
Author(s):
Wu Changqian1; Huang Rui2; Luo Zhiwei3
(1.College of Computer and Information Engineering,Minnan Science and Technology University,Quanzhou 362000,China)
(2.School of Computer Science & Technology,Beijing Institute of Technology,Beijing 100081,China)
(3.College of Mechanical and Electr
关键词:
车间调度; 智能制造; 量子计算; 蚁群算法; 全局搜索
Keywords:
workshop scheduling;  smart manufacturing;  quantum computing;  ant colony algorithm;  global search
分类号:
TP301
DOI:
10.3969/j.issn.1001-4616.2023.04.011
文献标志码:
A
摘要:
近年来,工业互联网技术逐渐得到普及,复杂构件生产车间的制造环境逐渐复杂化,提出一种基于量子蚁群算法的智能制造调度方案(QACA-AMJSP). 首先,结合智能制造车间的特点,构建了相应的车间调度数学模型. 然后,将量子计算与模拟自然界蚁群行为的蚁群算法相结合求解智能制造车间调度问题,利用量子比特表示信息素并以量子旋转门更新,保留了量子计算的高效性,提高了蚁群全局寻优能力,避免了蚂蚁易陷局部最优解问题. 实验结果表明,相比粒子群优化算法和遗传算法,量子蚁群算法对解决智能制造车间调度问题具有较高的搜索效率和较快的收敛速度.
Abstract:
In recent years,the industrial Internet technology has been gradually popularized,and the manufacturing environment of complex component production workshop has gradually become complicated. This paper proposes an intelligent manufacturing scheduling scheme based on quantum ant colony algorithm(QACA-AMJSP). Firstly,according to the characteristics of aviation manufacturing workshop,the corresponding workshop scheduling mathematical model is constructed. Then,quantum computing and ant colony algorithm,which simulates the behavior of ant colony in nature,are combined to solve the scheduling problem of aviation manufacturing workshop. Quantum bits are used to represent pheromones and are updated by quantum revolving doors,which keeps the efficiency of quantum computing,improves the global optimization ability of ant colony,and avoids the problem that ants are easily trapped in local optimal solutions. The experimental results show that,compared with particle swarm optimization algorithm and genetic algorithm,quantum ant colony algorithm has higher search efficiency and faster convergence speed for solving the aviation manufacturing workshop scheduling problem.

参考文献/References:

[1]杨小东,康雁,柳青,等. 求解作业车间调度问题的禁忌分布估计算法[J]. 计算机工程与应用,2016(7):1-8.
[2]崔琪,吴秀丽,余建军. 变邻域改进遗传算法求解混合流水车间调度问题[J]. 计算机集成制造系统,2017,23(9):1917-1927.
[3]刘洪铭,曾鸿雁,周伟,等. 基于改进粒子群算法作业车间调度问题的优化[J]. 山东大学学报(工学版),2019,49(1):75-82.
[4]刘胜辉,李小阳,张淑丽. 求解车间调度问题的双禁忌表禁忌搜索算法[J]. 哈尔滨理工大学学报,2016,21(6):50-54.
[5]黄海松,刘凯,初光勇. 改进模拟退火算法在柔性调度中的应用[J]. 组合机床与自动化加工技术,2018,2:148-156.
[6]施文章,韩伟,戴睿闻. 模拟退火下布谷鸟算法求解车间作业调度问题[J]. 计算机工程与应用,2017,53(17):249-259.
[7]朱小伶. 2020年量子计算技术发展综述[J]. 无人系统技术,2021,4(2):26-32.
[8]中国科学技术大学. “九章”问世,实现“量子计算优越性”里程碑[J]. 国际人才交流,2021(1):58-59.
[9]杜欣泽. 量子计算掌握未来的算力引擎[J]. 互联网经济,2020(12):36-37.
[10]DORIGO M,MANIEZZO V,COLORNI A. Ant system:optimization by a colony of cooperating agents[J]. IEEE transactions on system,man and cybernetics,1996,26(1):29-41.
[11]DORIGO M,GAMBARDELLA L M. Ant colony system:a cooperative learning approach to the traveling salesman problem[J]. IEEE transactions on evolutionary computation,1997,1(1):53-66.
[12]COLORNI A,DORIGO M,MANIEZZO V. Ant system for job-shop scheduling[J]. Belgian journal of operations research,statis-tics and computer science,1994,34(1):39-53.
[13]王凌. 车间调度及其遗传算法[M]. 北京:清华大学出版社,2003:59-67.
[14]王玉芳,缪昇,马铭阳,等. 改进混合遗传算法的作业车间调度研究[J]. 现代制造工程,2021(5):32-38.
[15]陈金广,马玲叶,马丽丽. 求解作业车间调度问题的改进遗传算法[J]. 计算机系统应用,2021,30(5):190-195.
[16]居凤霞. 粒子群优化算法的改进及应用[D]. 广州:华南理工大学,2014.
[17]杨文理,李长云. 改进粒子群算法在柔性作业车间调度中的应用[J]. 科学技术创新,2021(12):5-6.
[18]顾幸生,丁豪杰. 面向柔性作业车间调度问题的改进博弈粒子群算法[J]. 同济大学学报(自然科学版),2020,48(12):1782-1789.

备注/Memo

备注/Memo:
收稿日期:2022-06-21.
基金项目:国家自然科学基金面上项目(61871204)、福建省自然科学基金面上项目(2019J01863)、福建省教育科学“十三五”规划项目(FJJKCG20-014)、福建省本科教育教学改革研究项目(FBJG20200327)、新工科重点建设项目(MKXGK-2021-02).
通讯作者:吴昌钱,博士,副教授,研究方向:人工智能与大数据,物联网技术. E-mnail:wuchangqian@mku.edu.cn
更新日期/Last Update: 2023-12-15