[1]邱栋,彭奕童,陈兆芳.考虑不确定拆卸时间的异步并行拆卸序列规划[J].福建理工大学学报,2025,23(01):57-63.[doi:10.3969/j.issn.2097-3853.2025.01.005]
QIU Dong,PENG Yitong,CHEN Zhaofang.Research on asynchronous parallel disassembly sequence planning considering uncertain disassembly time[J].Journal of Fujian University of Technology;,2025,23(01):57-63.[doi:10.3969/j.issn.2097-3853.2025.01.005]
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考虑不确定拆卸时间的异步并行拆卸序列规划
《福建理工大学学报》[ISSN:2097-3853/CN:35-1351/Z]
- 卷:
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第23卷
- 期数:
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2025年01期
- 页码:
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57-63
- 栏目:
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- 出版日期:
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2025-02-26
文章信息/Info
- Title:
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Research on asynchronous parallel disassembly sequence planning considering uncertain disassembly time
- 作者:
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邱栋; 彭奕童; 陈兆芳
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福建理工大学管理学院
- Author(s):
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QIU Dong; PENG Yitong; CHEN Zhaofang
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School of Management, Fujian University of Technology
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- 关键词:
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异步并行拆卸序列规划; 灰数; 不确定性; 改进人工蜂群算法
- Keywords:
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asynchronous parallel disassembly sequence planning; grey numbers; uncertainty; improved artificial bee colony algorithm
- 分类号:
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TP301.6;TH165
- DOI:
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10.3969/j.issn.2097-3853.2025.01.005
- 文献标志码:
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A
- 摘要:
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因拆卸时间受操作者技能熟练程度、产品自身结构以及组成材料变化等多方面影响而存在不确定性的问题,基于灰数提出不确定拆卸时间的异步并行拆卸序列规划方法。在考虑拆卸最大规定时间约束、工作站先后顺序以及并行拆卸序列执行长度等约束条件的基础上建立最大拆卸收益与最小拆卸时间为目标的数学模型,提出一种改进人工蜂群算法。用矩阵编码构造可行解并使用锦标赛选择替代轮盘赌,在侦查蜂阶段设计一种基于潜能值的更新策略,提出一种新型的协同对比方法获得非劣解。通过算法对比验证改进算法的可行性,最后由笔记本电脑的拆卸实例来验证优化模型的有效性,实验结果表明:异步并行拆卸的经济效益提升了23.1%,拆卸时间缩短了12.7%。
- Abstract:
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Due to the uncertainty of disassembly time caused by various factors such as the proficiency of the operator’s skills, the structure of the product itself, and changes in the composition materials, an asynchronous parallel disassembly sequence planning problem with uncertain disassembly time is proposed based on grey numbers. On the basis of considering constraints such as the maximum specified dismantling time, workstation sequence, and parallel dismantling sequence execution length, a mathematical model is established with the objectives of maximum dismantling benefits and minimum dismantling time, and an improved artificial bee colony algorithm is proposed. The feasible solution is constructed by matrix encoding and tournament selection is used to replace the roulette wheel. An update strategy based on potential value is designed in the scout bee phase. A new collaborative comparison method is proposed to obtain non-inferior solutions. The feasibility of the improved algorithm was verified through algorithm comparison, and the effectiveness of the optimization model was finally validated through a disassembly example of a laptop. Experimental results show that the economic benefits of asynchronous parallel disassembly increased by 23.1%, and the disassembly time was shortened by 12.7%.
参考文献/References:
[1] 吴秀丽,张兴宇. 工位数固定的U型拆卸线部分拆卸平衡问题[J]. 控制理论与应用,2024,41(6):1079-1088.[2] GUNGOR A,GUPTA S M. An evaluation methodology for disassembly processes[J]. Computers & Industrial Engineering,1997,33(1/2):329-332.[3] 徐鹏程. 机电产品异步并行拆卸序列规划与评价技术研究[D]. 杭州:浙江大学,2020. [4] 邢世雄,陈国华,孙川,等.基于改进蝙蝠算法的再制造装配体拆卸序列规划研究[J/OL].机械设计与制造,1-6[2024-11-08].https:∥doi.org/10.19356/j.cnki.1001-3997.20240516.008.[5] 郭钧,王振东,杜百岗,等. 考虑不定拆卸程度的选择性异步并行拆卸序列规划[J]. 中国机械工程,2021,32(9):1080-1090,1101.[6] 孙娴静,唐秋华,邓明星. 基于改进遗传算法的异步并行拆卸序列规划[J]. 工业工程,2022,25(4):151-157.[7] 张雷,耿笑荣,陶凯博. 考虑碳排放与收益的随机并行拆卸线平衡优化[J]. 机械工程学报,2023,59(7):330-338.[8] 贾宝惠,任帅,卢翔. 考虑协作度的双人异步并行拆卸序列规划[J]. 机械设计与制造,2024(1):359-363,369.[9] 邓明星,陈方颖,唐秋华,等. 考虑多目标件的异步并行选择性拆卸序列[J]. 计算机集成制造系统,2020,26(7):1749-1755.[10] 郭秀萍,周玉莎. 用多目标动态规划求解拆卸序列的Pareto最优前沿[J]. 系统管理学报,2023,32(6):1205-1212.[11] 尹凤福,刘广阔,王晓东,等. 基于多种群遗传算法的废旧手机拆卸序列规划[J]. 合肥工业大学学报(自然科学版),2023,46(4):438-446.[12] ZHANG X S,FU A P,ZHAN C S,et al. Selective disassembly sequence planning under uncertainty using trapezoidal fuzzy numbers:a novel hybrid metaheuristic algorithm[J]. Engineering Applications of Artificial Intelligence,2024,128:107459.[13] 郑红斌,张则强,曾艳清. 不确定工人体能消耗的多目标U型拆卸线平衡问题[J]. 计算机集成制造系统,2023,29(2):392-403.[14] HU P,CHU F,DOLGUI A,et al. Integrated multi-product reverse supply chain design and disassembly line balancing under uncertainty[J]. Omega,2024,126:103062.[15] REN Y X,GAO K Z,FU Y P,et al. Ensemble artificial bee colony algorithm with Q-learning for scheduling bi-objective disassembly line[J]. Applied Soft Computing,2024,155:111415.
更新日期/Last Update:
2025-02-25