|Table of Contents|

Research on Intelligent Manufacturing Scheduling Problem Based on Quantum Ant Colony Algorithm(PDF)

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

Issue:
2023年04期
Page:
74-79
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Research on Intelligent Manufacturing Scheduling Problem Based on Quantum Ant Colony Algorithm
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
Keywords:
workshop scheduling;  smart manufacturing;  quantum computing;  ant colony algorithm;  global search
PACS:
TP301
DOI:
10.3969/j.issn.1001-4616.2023.04.011
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.

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.

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Last Update: 2023-12-15