基于MO-CGJaya/D算法的PC构件混流生产作业指派优化研究

doi:10.16381/j.cnki.issn1003-207x.2021.2301

摘要/Abstract

摘要：

针对PC构件生产调度优化研究在异型构件和非异型构件组合流水线的生产调度方面存在的不足，研究多生产线混流生产模式下的PC构件生产作业指派优化问题。首先，基于异型PC构件与非异型PC构件的生产线型号不同这一现实情况，考虑生产完工时间、生产成本、生产线利用率等5个目标，构建多生产线混流生产模式下的PC构件生产作业指派优化模型。然后，为求解此类高维多目标优化问题，提出了一种MO-CGJaya/D算法，该算法在分解策略的框架下结合混沌初始化和高斯变异操作，在增强算法跳出局部最优的能力的同时提升了求解效率，并通过DTLZ系列函数测试与NSGAⅡ算法、NSGAⅢ算法和MOEA/D算法进行对，比验证MO-CGJaya/D算法的性能。最后，以某预制构件生产企业的调研数据为案例，探讨了多生产线混流生产模式下的PC构件生产作业指派优化方案，研究结果对预制构件生产企业的生产实践具有指导性意义。

关键词: PC构件, 多生产线, 混流生产, 高维多目标优化, MO-CGJaya/D算法

Abstract:

As the core of prefabricated construction projects， PC components are generally produced in the mixed production mode. In other words， several different types of components are produced on one production line at the same time. The current research is mostly based on the mixed production mode of the single production line. However， the mixed production mode of multiple production lines is often adopted in the actual production process of enterprises. Some scholars simply equate the mixed-flow production of multiple production lines with the parallel production mode of multiple machines， which ignores the functional difference among production lines. In consideration of the shortcomings of PC component production scheduling optimization research in the production scheduling of special-shaped component and non-special-shaped component assembly line， it is proposed to study the PC component production job assignment optimization problem under the mixed-flow production mode of multiple production lines. Firstly， according to the reality about the different production line models of special-shaped PC components and non special-shaped PC components as well as five objectives， such as production completion time， production cost and production line utilization， the PC component production assignment optimization model under the mixed production mode of multiple production lines is constructed. Then， in order to solve this kind of many-objective optimization problem， a MO-CGJaya/D algorithm is proposed. The algorithm is combined with chaos initialization and Gaussian mutation operation under the framework of decomposition strategy， which enhances the ability of the algorithm to jump out of local optimization and improves the solution efficiency at the same time. Through the DTLZ series function test as well as the comparison with NSGA Ⅱ algorithm， NSGA Ⅲ algorithm and MOEA/D algorithm， the performance of MO-CGJaya/D algorithm is verified. Finally， the survey data of a prefabricated component manufacturing enterprise is taken as an example. The MO-CGJaya/D algorithm is adopted to solve the model. Through the comparison with the existing production scheduling scheme based on the single machine scheduling model， it is showed that the model proposed in this paper can effectively help prefabricated component manufacturers arrange the job assignment of PC components reasonably and play a guiding role in the production practice of prefabricated component manufacturers.

Key words: PC components, multiple production lines, mixed-flow production, many-objective optimization, MO-CGJaya/D algorithm

中图分类号:

TU756

汪和平,赵登宇,陈梦凯,李艳. 基于MO-CGJaya/D算法的PC构件混流生产作业指派优化研究[J]. 中国管理科学, 2024, 32(7): 95-105.

Heping Wang,Dengyu Zhao,Mengkai Chen,Yan Li. Research on Assignment Optimization of PC Components Mixed-flow Production Based on MO-CGJaya/D Algorithm[J]. Chinese Journal of Management Science, 2024, 32(7): 95-105.

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参考文献 22

1	Yuan M H， Li Y D， Zhang L Z， et al. Research on intelligent workshop resource scheduling method based on improved NSGA-II algorithm［J］.Robotics and Computer- integrated Manufacturing， 2021，71（12）： 102141.
2	Wang H Y， Zhao F， Gao H M， et al. A three-stage method with efficient calculation for lot streaming flow-shop scheduling［J］. Frontiers of Information Technology & Electronic Engineering，2019，20（7）：1002-1020.
3	叶涛锋，达庆利.基于批量流的多阶段生产系统的优化问题研究［J］.中国管理科学，2011，19（2）：71-78.
	Ye T F， Da Q L. Optimizing interrupted multi-stage production with lot streaming［J］. Chinese Journal of Management Science， 2011，19（2）：71-78.
4	李劲，李洪，徐丽丽，等.基于改进遗传算法的置换装配线调度问题研究［J］.中国管理科学，2016，24（12）：63-71.
	Li J， Li H， Xu L L， et al. Research on assembly line scheduling problem based on improved genetic algorithm［J］. Chinese Journal of Management Science， 2016，24（12）：63-71.
5	黄辉，李梦想，严永.考虑序列设置时间的混合流水车间多目标调度研究［J］.运筹与管理，2020，29（12）：215-221.
	Huang H， Li M X， Yan Y. Research on multi-objective scheduling of hybrid flow production shopconsidering sequence setting time［J］. Operations Research and Management Science， 2020，29（12）：215-221.
6	Zhu H H， Zhang Y， Sun H W， et al.An improved genetic algorithm for solving the mixed-flow job-shop scheduling problem with combined processing constraints［J］.Transactions of Nanjing University of Aeronautics and Astronautics，2021，38（3）：415-426.
7	程月明，牛占文，刘超超.装配式建造模式中混凝土预制构件调度优化［J］.工业工程与管理， 2020，25（4）：50-58.
	Cheng Y M， Niu Z W， Liu C C. Scheduling optimization of prefabricated concrete members inprefabricated construction mode［J］. Industrial Engineering and Management， 2020，25（4）：50-58.
8	王茹，班丹梅，王月，等.考虑资源约束的预制混凝土构件生产调度问题研究［J］.制造业自动化，2020，42（12）：63-67.
	Wang R， Ban D M， Wang Y， et al. Research on production scheduling problem of prefabricated concrete components considering resource constraints［J］. Manufacturing Automation，2020，42（12）：63-67.
9	谢思聪，陈小波.基于多层编码遗传算法的两阶段装配式建筑预制构件生产调度优化［J］.工程管理学报，2018，32（1）：18-22.
	Xie S C， Chen X B. Two phase job-shop scheduling optimization for prefabricated construction projects based on multilayer coding genetic algorithm［J］. Journal of Engineering Management，2018，32（1）：18-22.
10	Khalili A， Chua D. Integrated prefabrication configuration and component grouping for resource optimization of precast production［J］. Journal of Construction Engineering and Management， 2014， 140（2）：04013052.
11	Wang Z J， Hu H. Improved precast production-scheduling model considering the whole supply chain［J］. Journal of Computing in Civil Engineering， 2017，31（4）：1-12.
12	Anvari B， Angeloudis P， Ochieng W. A multi-objective ga-based optimisation for holistic manufacturing， transportation and assembly of precast construction［J］.Automation in Construction， 2016， 71（2）： 226-241.
13	赵东方，张晓冬，周宏丽.面向并行制造的多生产单元协同调度研究［J］.中国管理科学，2020，28（8）：188-195.
	Zhao D F， Zhang X D， Zhou H L. Multi-manufacturing cells collaborative scheduling based on parallel manufacturing［J］. Chinese Journal of Management Science，2020，28（8）：188-195.
14	Yang Z T， Ma Z L， Wu S. Optimized flowshop scheduling of multiple production lines for precast production［J］.Automation in Construction， 2016，72（87）：321-329.
15	方秋军，陈勇，庄薇，等. 多生产线作业协调调度的PSOGA混合优化算法［J］. 现代制造工程， 2012，380（5）：27-32.
	Fang Q J， Chen Y， Zhuang W， et al. Multi-line optimal scheduling research based on PSOGA hybrid optimization algorithm［J］. Modern Manufacturing Engineering， 2012，380（5）：27-32.
16	方爱斌，朱敏涛.不同预制构件生产线生产工艺及能耗指标分析研究［J］. 混凝土，2021（1）：127-131.
	Fang A B， Zhu M T. Analysis and research on production technology and energy consumption index of different prefabricated component production lines［J］. Concrete， 2021（1）：127-131.
17	Rao R V. Jaya： A simple and new optimization algorithm for solving constrained and unconstrained optimization problems［J］.International Journal of Industrial Engineering Computations， 2016，7（1）： 19-34.
18	Afifi M， Rezk H， Ibrahim M N F， et al. Multi-objective optimization of switched reluctance machine design using jaya algorithm （MO-Jaya）［J］. Mathematics， 2021， 9（10）： 1107-1125.
19	王建华，潘宇杰，孙瑞.自适应Jaya算法求解多目标柔性车间绿色调度问题［J］.控制与决策，2021，36（7）：1714-1722.
	Wang J H， Pan Y J， Sun R. Multi-objective flexible job shop green scheduling problem with self-adaptive Jaya algorithm［J］. Control and Decision， 2021，36（7）：1714-1722.
20	吴亚丽，付玉龙，李国婷，等. 高维多目标头脑风暴优化算法［J］. 控制理论与应用， 2020，37（1）：193-204.
	Wu Y L， Fu Y L， Li G T， et al. Many-objective brain storm optimization algorithm［J］. Control Theory & Applications， 2020，37（1）：193-204.
21	Chen L， Chen W Y， Li H W， et al. Solving multi-objective optimization problem using cuckoo search algorithm based on decomposition［J］. Applied Intelligence，2020， 51（9）： 143-160.
22	滕志军，吕金玲，郭力文，等. 一种基于Tent映射的混合灰狼优化的改进算法［J］. 哈尔滨工业大学学报， 2018， 50（11）： 40-49.
	Teng Z J， Lv J L， Guo L W， et al. An improved hybrid grey wolf optimization algorithm based on Tent mapping［J］. Journal of Harbin Institute of Technology， 2018， 50（11）： 40-49.

DTLZ系列函数	k	M	MO-CJaya/D	NSGAⅡ	NSGAⅢ	MOEA/D
DTLZ1	5	3	2.4065 e-04	2.0595 e-02	1.9084 e-02	1.9610 e-02
		5	9.0403 e-04	6.4012 e-02	5.3074 e-02	1.1173 e-01
		8	2.9253 e-03	1.8612 e-01	1.0803 e-01	1.5170 e-01
DTLZ2	10	3	3.0647 e-04	5.9347 e-02	5.1141 e-02	6.7963 e-02
		5	2.2027 e-03	1.9933 e-01	1.8589 e-01	3.1804 e-01
		8	5.2946 e-03	4.3573 e-01	3.3053 e-01	5.4157 e-01
DTLZ3	10	3	6.7158 e-04	2.7669 e-02	2.3366 e-02	2.8563 e-02
		5	1.1326 e-02	1.9629 e-01	1.9543 e-01	2.5186 e-01
		8	3.6434 e-02	5.7729 e-01	5.0716 e-01	5.5711 e-01
DTLZ4	10	3	3.9241 e-04	1.5375 e-01	2.8566 e-02	2.8548 e-02
		5	2.4816 e-03	2.3174 e-01	1.7526 e-01	2.1677 e-01
		8	5.5815 e-03	3.7757 e-01	3.6237 e-01	5.4785 e-01

生产线	生产顺序
1号生产线	7→8→36→9→35→34→44→43→37→38→39
2号生产线	15→21→20→32→16→33→42→40→41→45→47→48→46
3号生产线	27→24→22→4→1→2→23→3→26→5→25→6
4号生产线	30→13→19→12→14→17→18→28→10→11→31→29

Pareto解集	完工时间(小时)	生产成本(元)	惩罚成本(元)	无效化时间(小时)	生产线利用率（%）
1	219.8	40218.3	6138.5	146.7	99.77
2	219.9	41704.5	5841	124.5	88.92
3	220	36016	5817	191	99.63
4	220.1	42434.4	5849	125.3	88.93
5	221.4	42374.4	5045	125.6	99.09
6	244.2	38933.4	3980	216.6	89.74

生产线	生产顺序
1号生产线	47→13→44→40→37→48→38→29
2号生产线	41→45→46→43→39→42→36
3号生产线	30→28→1→31→24→33→8→9→7→2→4→3→5→6
4号生产线	17→19→34→22→18→14→16→20→11→32→21→15→27→12→25→26→10→23→35