多场景多目标动态变化下船舶小组立装焊重调度

doi:10.16183/j.cnki.jsjtu.2023.274

摘要/Abstract

摘要：

针对船舶小组立装焊过程中物料配送延迟、设备故障等异常状况频发导致的生产进度滞后、生产计划需重新调整问题,提出一种多场景多目标动态变化重调度算法.首先,根据生产阶段和异常扰动的不同选取目标函数,并构建包含场地约束、任务先序约束和人力资源约束的数学模型;其次,引入场地配置算法,采用改进的基于参考点的非支配排序算法求解,增加交叉检查机制,设计基于任务序列的变异算子,融合多染色体机制,与场地配置算法相结合进行求解;之后,提出了面向船舶小组立装焊重调度的染色体序列距离计算方法,描述重调度算法解与初始计划的差异大小,与R2指标结合,定义Pareto-R2双标准选择算子,兼顾算法多样性和收敛性;最后,基于工程案例设计对比实验,验证了提出的重调度算法的有效性.

关键词: 船舶小组立, 重调度, 多目标优化, 多场景

Abstract:

A multi-scenario and multi-objective dynamic change rescheduling algorithm is proposed to address the production schedule delays and the need for adjustments caused by frequent abnormal conditions, such as material delivery delays and equipment failures in the vertical assembly welding process of shipbuilding teams. First, the objective function is selected based on different production stage and abnormal disturbance, and a mathematical model is then developed, incorporating site constraints, task precedence constraints, and human resource constraints. Next, a site allocation algorithm is introduced, and an improved non-dominated sorting algorithm based on reference points is adopted to solve the problem. A cross-checking mechanism implemented, alongside a task-sequence-based mutation operator, and the multi-chromosome mechanism is integrated with the site allocation algorithm. Afterwards, a method for calculating the chromosome sequence distance in the ship group vertical assembly welding rescheduling is proposed, and the difference between the rescheduling algorithm solution and initial plan is described. An R2 indicator is combined with a Pareto-R2 double standard selection operator, which ensures both diversity and convergence of the algorithm. Finally, comparative experiments are conducted based on engineering cases to validate the effectiveness of the proposed rescheduling algorithm.

Key words: small assembly of ship, reschedule, multi-objective optimization, multi-scenario

中图分类号:

TP391
TH186

张澳圆, 胡小锋, 张亚辉. 多场景多目标动态变化下船舶小组立装焊重调度[J]. 上海交通大学学报, 2025, 59(4): 476-488.

ZHANG Aoyuan, HU Xiaofeng, ZHANG Yahui. Rescheduling of Multi-Scenario and Multi-Objective Dynamic Changes of Ship Group Construction[J]. Journal of Shanghai Jiao Tong University, 2025, 59(4): 476-488.

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0	0	2	109	0	124

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次数	41	194
比例	17%	83%

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1192

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76	0	1116

来源	本网站	其他网站

次数	45	1147
比例	4%	96%

目标函数	前期			中期			后期
目标函数	异常1	异常2	异常3	异常1	异常2	异常3	异常1	异常2	异常3
最小化最大完工时间	√	√	√	√			√
最小化开工时间偏差	√			√	√	√	√
最大化空间利用率	√	√	√
最大化工人利用率	√	√	√		√			√
最小化延期时间						√	√	√	√
最大化紧急任务如期交付			√			√			√

形状编号	形状	顶点坐标集
1	直角三角形	{(0, 0), (5, 0), (0, 3)}
2	正方形	{(0, 0, )(3, 0)(3, 3), (0, 3)}
3	直角梯形	{(0, 0), (5, 0), (3, 3)(0, 3)}
4	等腰梯形	{(0, 0), (5, 0), (3.9, 3), (1.1, 3)}
5	五边形	{(0, 0), (5, 0), (3.9, 3), (1.1, 3) (0, 1.5)}
6	四边形	{(0, 0), (3.0), (3.4, 1), (0, 3)}
7	一般三角形	{(0, 0), (5, 0), (4.2, 3)}
8	矩形1	{(0, 0), (14, 0), (14, 3), (0, 3)}
9	矩形2	{(0, 0), (14, 0), (14, 5), (0, 5)}
10	平行四边形1	{(0, 0), (12.3, 0), (14, 3), (1.7, 3)}
11	平行四边形2	{(0, 0), (13.1, 0), (14, 5), (0.9, 5)}
12	六边形	{(0, 0), (10.5, 0), (14, 2), (14, 5), (3.5, 5), (0, 3)}
13	横躺等腰梯形	{(0, 0), (14.4, 0), (15, 3), (1.4, 7.9)}
14	不规则五边形	{(0, 0), (14.4, 0), (15, 3), (9.6, 4.9), (0.9, 4.9)}

部件类型	部件编号	部件形状
肋板	1, 2, 3, 4, 5, 6, 7	五边形
	8, 9, 10, 11, 12, 13, 14	等腰梯形
	15, 16, 17, 18	直角梯形
	19, 20, 21, 22	直角三角形
纵桁	23	横躺等腰梯形
	24	不规则五边形
	25	六边形
	26	平行四边形2
小组部件	27	横躺等腰梯形
	28	不规则五边形
	29	六边形
	30	平行四边形2

算法	最大完工时间	开工时间偏差	空间利用率	工人利用率
改进前的NSGA-III	94	398	0.41	0.65
	97	287	0.39	0.58
	99	490	0.43	0.59
	94	398	0.41	0.65
本文算法	93	256	0.43	0.68

算法	开工时间偏差	工人利用率	延期时间	紧急任务提前交付
改进前的NSGA-III	39	0.60	95	5
	63	0.63	93	9
	72	0.62	92	6
	65	0.56	94	11
	56	0.55	96	11
	69	0.56	97	11
本文算法	57	0.65	91	11