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浙江大学学报(工学版)  2022, Vol. 56 Issue (11): 2135-2144    DOI: 10.3785/j.issn.1008-973X.2022.11.004
机械与能源工程     
基于候鸟算法的批量流混合装配流水车间调度
鲁建厦(),金敬豪,赵文彬*(),陈青丰,江伟光
浙江工业大学 机械工程学院,浙江 杭州 310023
Lot streaming hybrid assembly flow shop scheduling on migratory bird algorithm
Jian-sha LU(),Jing-hao JIN,Wen-bin ZHAO*(),Qing-feng CHEN,Wei-guang JIANG
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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摘要:

针对模具数量限制下装配车间生产计划的优化问题,在考虑不等量可变的批量划分策略情况下,研究批量流混合装配流水车间调度问题,并提出一种有效候鸟优化算法. 在算法中,针对多种产品各生产阶段装配约束设计批量划分与排列顺序的2段编码机制;根据编码特征设计多种邻域结构,包含一种同时优化批量划分与排列顺序的邻域结构,并提出邻域结构自适应调节策略来提升领域结构搜索性能;设计竞争机制来提升算法优化效率. 开展不同规模算例的仿真实验,结果验证不等量可变分批策略更有效,优于等量策略5%~6%. 与其他多种算法进行比较,不等量策略可为车间提供更合理的生产计划,验证有效候鸟优化算法的有效性和鲁棒性.

关键词: 批量流混合装配流水车间多模具约束候鸟优化算法领域自适应调节    
Abstract:

An effective migratory bird optimization algorithm was proposed to aim at the optimized problem of productive planning of assemble workshop under the limitation of the number of molds, and the variable batch division strategy with unequal quantity was considered. The lot streaming hybrid assembly flow shop scheduling problem was studied. In the algorithm, a two-stage coding mechanism of batch partitioning and ordering was designed according to the assembly constraints on various products of each production stage. The multiple neighborhood structures were designed according to the coding characteristics, including a neighborhood structure that simultaneously optimized the batch division and ordering. An adaptive adjustment strategy of neighborhood structure was proposed to improve the search performance of domain structure. The competition mechanism was designed to improve the efficiency of algorithm optimization. The simulation results of different scale examples showed that the unequal variable batch strategy was more effective than the equal batch strategy by 5%~6%. The unequal strategy provided a reasonable production plan for the workshop. The effectiveness and robustness of the effective migratory bird optimization algorithm compared with other algorithms were verified.

Key words: lot streaming    hybrid assembly flow shop    multi-mold constraint    migratory bird optimization algorithm    domain adaptive adjustment
收稿日期: 2021-12-14 出版日期: 2022-12-02
CLC:  TH 166  
基金资助: 浙江省重点研发计划项目(2018C0100);浙江省自然科学基金资助项目(LY19G020010)
通讯作者: 赵文彬     E-mail: ljs@zjut.edu.cn;wenbin86@zjut.edu.cn
作者简介: 鲁建厦(1963—),男,教授,博导,从事智能物流、物流装备和精益生产研究. orcid.org/0000-0001-5793-3328.E-mail: ljs@zjut.edu.cn
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引用本文:

鲁建厦,金敬豪,赵文彬,陈青丰,江伟光. 基于候鸟算法的批量流混合装配流水车间调度[J]. 浙江大学学报(工学版), 2022, 56(11): 2135-2144.

Jian-sha LU,Jing-hao JIN,Wen-bin ZHAO,Qing-feng CHEN,Wei-guang JIANG. Lot streaming hybrid assembly flow shop scheduling on migratory bird algorithm. Journal of ZheJiang University (Engineering Science), 2022, 56(11): 2135-2144.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.11.004        https://www.zjujournals.com/eng/CN/Y2022/V56/I11/2135

图 1  家电类产品工序简化图
图 2  装配流水车间简化模型图
图 3  产品3阶段工序图
图 4  批量划分编码生成
图 5  初始排列编码生成
图 6  2段编码的邻域结构变换
图 7  EMBO算法流程图
图 8  多种产品的工序图
水平值 参数
Nf ng C0
1 25 3 10
2 51 5 20
3 81 8 30
4 101 10 50
表 1  EMBO算法参数水平表
实验序号 参数 ARV
Nf ng C0
1 25 3 10 11 809
2 25 5 20 11 084
3 25 8 30 11 644
4 25 10 50 11 312
5 51 3 20 10 723
6 51 5 10 10 735
7 51 8 50 10 719
8 51 10 30 10 738
9 81 3 30 11 212
10 81 5 50 11 081
11 81 8 10 11 115
12 81 10 20 10 964
13 101 3 50 11 624
14 101 5 30 10 807
15 101 8 20 11 285
16 101 10 10 11 534
Level1 11 462 11 342 11 298
Level2 10 729 10 927 11 014
Level3 11 093 11 191 11 100
Level4 11 313 11 137 11 184
Delta 734 415 284
排秩 1 2 3
表 2  正交实验结果
图 9  EMBO算法中参数对算法性能影响图
算例 CPLEX EMBO PRD
1 6 600 6 600 0.0
2 8 100 8 160 7.3
表 3  EMBO算法与CPLEX的求解对比
图 10  不同分批策略2种算例结果图
算例 EMBO IGA IPSO IMBO
序号 P K Avg Std. RSD Avg Std RSD Avg Std RSD Avg Std RSD
1 3 55 8 261 116 1.40% 9 108 251 2.76% 9 497 328 3.45% 8 666 235 2.71%
2 60 8 131 123 1.51% 8 864 263 2.97% 9 270 262 2.83% 8 455 238 2.81%
3 65 8 059 139 1.72% 8 776 321 3.66% 9 207 229 2.49% 8 318 204 2.45%
4 5 67 10 772 133 1.23% 12 003 331 2.76% 12 772 322 2.52% 10 840 230 2.12%
5 72 10 624 127 1.20% 11 914 357 3.00% 12 454 255 2.05% 10 741 243 2.26%
6 81 10 570 119 1.13% 11 493 364 3.17% 12 389 188 1.52% 10 632 213 2.00%
7 7 73 15 186 122 0.80% 16 359 577 3.53% 18 022 370 2.05% 15 373 140 0.91%
8 80 15 032 169 1.12% 16 074 416 2.59% 17 676 196 1.11% 15 186 229 1.51%
9 91 14 653 200 1.36% 15 626 308 1.97% 17 520 259 1.48% 14 744 228 1.55%
10 9 78 18 396 159 0.86% 20 288 357 1.76% 21 855 350 1.60% 19 240 142 0.74%
11 86 18 206 184 1.01% 20 072 371 1.85% 21 543 374 1.74% 18 821 211 1.12%
12 100 18 119 223 1.23% 19 921 362 1.82% 21 531 272 1.26% 18 506 240 1.30%
表 4  4种算法的统计结果
算例 EMBO-IMBO
置信上限 置信下限
1 ?555 ?256
2 ?450 ?197
3 ?387 ?132
4 ?217 81
5 ?252 19
6 ?198 74
7 ?309 ?67
8 ?300 ?7
9 ?213 31
10 ?917 ?771
11 ?762 ?468
12 ?578 ?197
表 5  置信度为0.95的EMBO与IMBO算法的最优解比较
图 11  4种算法收敛效果图
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