面向可持续生产中多任务调度的双重增强模因算法

卢弘; 王耀南; 乔非; 方遒

doi:10.16383/j.aas.c230446

面向可持续生产中多任务调度的双重增强模因算法

doi: 10.16383/j.aas.c230446

卢弘^1,,
王耀南^1,,
乔非^2,,
方遒^1,

1.
湖南大学电气与信息工程学院长沙 410082
2.
同济大学电子与信息工程学院上海 201804

基金项目: 湖南创新型省份建设科技重大专项 (2021GK1010), 国家自然科学基金 (62293510), 湖南省自然科学基金 (2023JJ30162), 岳麓山工业创新中心重大项目 (2023YCII0102), 湖南省教育厅科学研究项目优秀青年项目 (23B0029) 资助

详细信息

作者简介:
卢弘：湖南大学电气与信息工程学院博士后. 2022 年获得同济大学博士学位. 主要研究方向为生产调度与智能优化. E-mail: luhong@hnu.edu.cn

王耀南：中国工程院院士, 湖南大学电气与信息工程学院教授. 1995 年获得湖南大学博士学位. 主要研究方向为机器人学, 智能控制和图像处理. E-mail: yaonan@hnu.edu.cn

乔非：同济大学电子与信息工程学院教授. 1997 年获得同济大学博士学位. 主要研究方向为复杂制造计划与调度, 智能生产系统以及能源管理与优化. E-mail: fqiao@tongji.edu.cn

方遒：湖南大学电气与信息工程学院副教授. 2017 年获得同济大学博士学位. 主要研究方向为复杂工业过程建模与优化. 本文通信作者. E-mail: qfang@hnu.edu.cn

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出版历程
- 收稿日期: 2023-07-20
- 录用日期: 2024-01-23
- 网络出版日期: 2024-03-29
- 刊出日期: 2024-04-26

Dual-enhanced Memetic Algorithm for Multi-task Scheduling in Sustainable Production

LU Hong^1
,,
WANG Yao-Nan^1
,,
QIAO Fei^2
,,
FANG Qiu^1
,

1.
College of Electrical and Information Engineering, Hunan University, Changsha 410082
2.
College of Electronics and Information Engineering, Tongji University, Shanghai 201804

Funds: Supported by Special Funding Support for the Construction of Innovative Provinces in Hunan Province (2021GK1010), National Natural Science Foundation of China (62293510), Hunan Provincial Natural Science Foundation (2023JJ30162), Major Project of Yuelushan Industrial Innovation Center (2023YCII0102), and Hunan Provincial Department of Education Scientific Research Project (23B0029)

More Information

Author Bio:
LU Hong　Postdoctor at the College of Electrical and Information Engineering, Hunan University. He received his Ph.D. degree from Tongji University in 2022. His research interest covers production scheduling and intelligent optimization

WANG Yao-Nan　Academician at Chinese Academy of Engineering, professor at the College of Electrical and Information Engineering, Hunan University. He received his Ph.D. degree from Hunan University in 1995. His research interest covers robotics, intelligent control, and image processing

QIAO Fei　Professor at the College of Electronics and Information Engineering, Tongji University. She received her Ph.D. degree from Tongji University in 1997. Her research interest covers complex manufacturing planning and scheduling, intelligent production systems, and energy management and optimization

FANG Qiu　Associate professor at the College of Electrical and Information Engineering, Hunan University. He received his Ph.D. degree from Tongji University in 2017. His research interest covers modeling and optimization of complex industrial processes. Corresponding author of this paper

摘要

摘要: 从经济、环境和社会3个维度, 全面提升生产调度方案的可持续性具有重要意义. 针对并行机生产场景, 建立考虑机器指派、加工顺序、人员安排以及开关机控制等4种决策任务的调度模型. 为实现对复杂决策空间的高效寻优, 提出一种融合两种局部优化策略的双重增强模因算法(Dual-enhanced memetic algorithm, DMA)求解模型. 从随机更新角度, 针对不同决策任务, 构造单步变邻域搜索(One-step variable neighborhood search, 1S-VNS)策略. 从定向优化角度, 分析目标和关键任务之间的匹配关系, 提出一种可持续目标导向策略(Sustainable goals-oriented strategy, SGS). 考虑到两种优化策略的不同特点, 单步变邻域搜索策略作用于整个种群, 目标导向策略强化种群中的精英个体, 实现对输出解集的双重优化. 仿真实验结果表明, 双重优化策略能有效地增强算法性能, 并且所提算法在非支配解的多样性和收敛性上具有优越性.
- 可持续生产 /
- 多任务调度 /
- 优化策略 /
- 模因算法
Abstract: It is of great significance to comprehensively enhance the sustainability of production scheduling with economic, environmental and social demand. A scheduling model for parallel machine production is established with consideration of four decision tasks: Machine assignment, processing sequence, personnel arrangement, and on/off machine control. To solve this complex problem, a dual-enhanced memetic algorithm (DMA) that integrates two local optimization strategies is proposed. In a random manner, a one-step variable neighborhood search (1S-VNS) suitable for decision-making tasks is designed. For targeted optimization, a sustainable goals-oriented strategy (SGS) is constructed after analyzing the matching relationship between objectives and key tasks. Based on the different characteristics of the two optimization strategies, the 1S-VNS acts on the entire population, and the SGS strengthens the elite individuals, achieving dual optimization of the output solution set. The simulation experimental results show that the dual optimization strategies effectively enhance the algorithm performance, and the proposed DMA has superiority in diversity and convergence of non-dominated solutions.
- Sustainable production /
- multi-task scheduling /
- optimization strategy /
- memetic algorithm (MA)

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图 1 任务对应的个体编码说明图

Fig. 1 An example graph of individual coding corresponding for tasks

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图 2 社会维度目标导向的优化策略作用效果说明图

Fig. 2 Explanation of the effectiveness of optimization strategy guided by social dimension goal

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图 3 经济维度目标导向的优化策略作用效果说明图

Fig. 3 Explanation of the effectiveness of optimization strategy guided by economic dimension goal

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图 4 MMA与MOA、MMA_1、MMA_2、MMA_3性能指标的均值和95%置信区间

Fig. 4 Mean and 95% confidence interval of performance indicators of MMA, MOA, MMA_1, MMA_2 and MMA_3

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图 5 DMA与MMA、MMA&SGS性能指标的均值和95%置信区间

Fig. 5 Mean and 95% confidence interval of performance indicators of DMA, MAA and MMA&SGS

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图 6 DMA与V-NSGA-II、IABC、MA性能指标的均值和95%置信区间

Fig. 6 Mean and 95% confidence interval of performance indicators of DMA, V-NSGA-II, IABC and MA

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图 7 DMA与V-NSGA-II、IABC、MA获得的Pareto前沿

Fig. 7 Pareto frontiers obtained by DMA, V-NSGA-II, IABC and MA

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表 1 MMA与MOA、MMA_1、MMA_2、MMA_3的性能指标结果

Table 1 Results for MMA, MOA, MMA_1, MMA_2 and MMA_3

案例	$ IGD$					$R_{{\mathrm{nd}}} $
案例	MOA	MMA_1	MMA_2	MMA_3	MMA	MOA	MMA_1	MMA_2	MMA_3	MMA
7&4&2	0.79	0.66	0.63	0.39	0.24	0.10	0.39	0.55	0.70	0.87
7&5&3	0.79	0.65	0.58	0.86	0.33	0.13	0.39	0.47	0.42	0.72
8&4&2	0.97	0.53	0.52	0.50	0.36	0.00	0.41	0.43	0.46	0.71
8&5&3	0.74	0.63	0.53	0.57	0.47	0.00	0.14	0.40	0.35	0.61
9&4&2	0.87	0.71	0.69	0.63	0.39	0.13	0.08	0.19	0.31	0.71
9&5&3	0.64	0.57	0.63	0.59	0.35	0.00	0.19	0.11	0.32	0.75
10&4&2	0.97	0.69	0.66	0.78	0.41	0.00	0.13	0.33	0.23	0.70
10&5&3	0.70	0.67	0.55	0.59	0.44	0.00	0.07	0.38	0.29	0.67
20&10&6	0.69	0.64	0.67	0.69	0.43	0.07	0.23	0.14	0.09	0.66
20&10&8	0.67	0.82	0.80	0.73	0.36	0.15	0.02	0.11	0.13	0.70
20&12&8	0.68	0.77	0.47	0.75	0.49	0.50	0.10	0.63	0.23	0.62
20&12&10	0.48	0.90	0.53	0.72	0.40	0.68	0.08	0.58	0.12	0.68
40&10&6	0.85	0.94	0.64	0.90	0.32	0.10	0.00	0.29	0.00	0.71
40&10&8	0.92	0.91	0.62	0.65	0.31	0.05	0.08	0.28	0.12	0.78
40&12&8	0.86	0.69	0.74	0.85	0.39	0.09	0.17	0.11	0.10	0.69
40&12&10	0.77	0.78	0.71	0.81	0.43	0.14	0.13	0.17	0.12	0.67

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表 2 DMA与MMA、MMA&SGS的性能指标结果

Table 2 Results for DMA, MMA and MMA&SGS

案例	$ IGD$			$R_{{\mathrm{nd}}} $
案例	MMA	MMA&SGS	DMA	MMA	MMA&SGS	DMA
7&4&2	0.00	0.00	0.00	1.00	1.00	1.00
7&5&3	0.35	0.49	0.38	0.87	0.70	0.86
8&4&2	0.45	0.51	0.39	0.71	0.48	0.76
8&5&3	0.29	0.44	0.34	0.74	0.49	0.72
9&4&2	0.61	0.57	0.41	0.62	0.52	0.73
9&5&3	0.52	0.80	0.39	0.65	0.36	0.77
10&4&2	0.80	0.80	0.36	0.47	0.46	0.77
10&5&3	0.57	0.85	0.43	0.42	0.30	0.71
20&10&6	0.71	0.57	0.41	0.20	0.69	0.74
20&10&8	0.65	0.51	0.43	0.12	0.70	0.73
20&12&8	0.71	0.44	0.47	0.22	0.74	0.72
20&12&10	0.85	0.39	0.42	0.22	0.79	0.75
40&10&6	0.80	0.59	0.39	0.11	0.28	0.79
40&10&8	0.71	0.58	0.37	0.16	0.33	0.72
40&12&8	0.73	0.83	0.42	0.19	0.30	0.71
40&12&10	0.74	0.65	0.40	0.22	0.26	0.73

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表 3 DMA与V-NSGA-II、IABC、MA的性能指标结果

Table 3 Results for DMA, V-NSGA-II, IABC and MA

案例	$IGD$				$R_{{\mathrm{nd}}}$
案例	V-NSGA-II	IABC	MA	DMA	V-NSGA-II	IABC	MA	DMA
7&4&2	0.85	0.67	0.48	0.15	0.00	0.15	0.70	0.87
7&5&3	0.74	0.85	0.66	0.24	0.00	0.08	0.22	0.82
8&4&2	0.65	0.76	0.41	0.32	0.20	0.08	0.43	0.76
8&5&3	0.78	0.80	0.32	0.25	0.32	0.24	0.44	0.87
9&4&2	0.41	0.63	0.56	0.36	0.39	0.34	0.37	0.76
9&5&3	0.86	0.61	0.57	0.13	0.15	0.20	0.35	0.86
10&4&2	0.52	0.31	0.49	0.22	0.21	0.31	0.27	0.77
10&5&3	0.63	0.56	0.52	0.20	0.12	0.22	0.45	0.78
20&10&6	0.85	0.88	0.69	0.21	0.07	0.05	0.15	0.83
20&10&8	0.83	0.91	0.72	0.18	0.02	0.00	0.11	0.86
20&12&8	0.79	0.84	0.74	0.31	0.05	0.00	0.23	0.71
20&12&10	0.82	0.93	0.69	0.30	0.03	0.00	0.35	0.74
40&10&6	0.78	0.81	0.55	0.11	0.00	0.00	0.15	0.85
40&10&8	0.62	0.89	0.61	0.29	0.14	0.00	0.14	0.73
40&12&8	0.59	0.87	0.53	0.23	0.11	0.00	0.15	0.77
40&12&10	0.62	0.83	0.50	0.31	0.09	0.00	0.18	0.71

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A1 各个工件的基本加工数据

A1 Basic machining data for each job

工件	机器	工人	加工时间$t$	加工单位能耗$p^{\rm{prc}}$
$J1$	$M1$	$W1$	3	8
$J1$	$M1$	$W2$	6	6
$J1$	$M2$	$W1$	4	6
$J1$	$M2$	$W2$	2	4
$J1$	$M3$	$W1$	6	5
$J1$	$M3$	$W2$	6	6
$J2$	$M1$	$W1$	4	7
$J2$	$M1$	$W2$	2	5
$J2$	$M2$	$W1$	3	5
$J2$	$M2$	$W2$	4	8
$J2$	$M3$	$W1$	4	3
$J2$	$M3$	$W2$	3	8
$J3$	$M1$	$W1$	5	8
$J3$	$M1$	$W2$	5	7
$J3$	$M2$	$W1$	3	3
$J3$	$M2$	$W2$	2	4
$J3$	$M3$	$W1$	3	5
$J3$	$M3$	$W2$	3	7
$J4$	$M1$	$W1$	4	3
$J4$	$M1$	$W2$	4	7
$J4$	$M2$	$W1$	2	3
$J4$	$M2$	$W2$	3	6
$J4$	$M3$	$W1$	6	5
$J4$	$M3$	$W2$	2	7
$J5$	$M1$	$W1$	6	7
$J5$	$M1$	$W2$	5	8
$J5$	$M2$	$W1$	4	8
$J5$	$M2$	$W2$	4	5
$J5$	$M3$	$W1$	3	7
$J5$	$M3$	$W2$	4	4

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A2 机器单位空闲能耗以及开关机能耗

A2 Unit idle energy consumption and on/off energy consumption of machines

机器	空闲单位能耗$p^{\rm{idle}}$	开关机时间$t_{\rm{on/off}}$	开关机能耗$H_{\rm{turn}}$
$M1$	1	2	2
$M2$	3	3	9
$M3$	3	3	6

下载: 导出CSV

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