基于多智能体的数字化船厂设计-采购耦合研究

doi:10.16182/j.issn1004731x.joss.26-0021

摘要/Abstract

摘要：

针对数字化造船中设计与采购环节脱节导致的供应链协同效率低下与动态调度能力不足问题，提出一种融合多智能体仿真与Q-learning强化学习算法，构建了包含矿场、钢厂、船厂、船东的4级设计-采购耦合供应链动态模型。依据协同深度将钢厂划分为全面协同、部分协同、不协同3个等级，并为其设计了差异化的状态空间与奖励函数以模拟信息不对称下的智能定价与博弈行为。仿真结果表明：船厂需根据建造周期与成本的动态权衡智能调整协同采购策略。为实现从设计源头到采购执行的精准协同、动态调度与优化决策提供了可量化的智能仿真方法与决策依据。

关键词: 设计-采购耦合, 造船供应链管理, 多智能体系统, Q-learning, 系统仿真

Abstract:

To address the issues of low supply chain collaboration efficiency and inadequate dynamic scheduling capability caused by the disconnection between design and procurement in digital shipbuilding, a method integrating multi-agent simulation with the Q-learning reinforcement learning algorithm is proposed, and a four-tier dynamic supply chain model coupling design and procurement is developed, covering mines, steel mills, shipyards, and shipowners. Based on the depth of collaboration, steel mills are classified into three levels, including full collaboration, partial collaboration, and non-collaboration, with differentiated state spaces and reward functions designed to simulate intelligent pricing and game behaviors under information asymmetry. Simulation results show that shipyards need to intelligently adjust collaborative procurement strategies based on the dynamic trade-off between construction cycle and cost. This provides a quantifiable intelligent simulation method and decision-making basis for achieving precise collaboration, dynamic scheduling, and optimized decision-making throughout the entire process from design to procurement execution.

Key words: design-procurement coupling, shipbuilding supply chain management, multi-agent system, Q-learning, system simulation

中图分类号:

TH186

彭开泰,冯鼎一,赵文博等 . 基于多智能体的数字化船厂设计-采购耦合研究[J]. 系统仿真学报, 2026, 38(10): 2857-2871.

Peng Kaitai,Feng Dingyi,Zhao Wenbo,et al . Research on Digital Shipyard Design-procurement Coupling Based on Multi-agent Systems[J]. Journal of System Simulation, 2026, 38(10): 2857-2871.

图/表 17

图1

图2

图3

表1

图4

图5

表2

算法关键参数的候选取值范围

参数	取值范围
学习率 $α$	{0.1, 0.3, 0.5, 0.7}
折扣因子 $γ$	{0.9, 0.93, 0.95, 0.97, 0.99}
探索率 $ε$	{0.1, 0.3, 0.5, 0.7, 0.9}
趋势权重 $χ$	{0.2, 0.4, 0.6, 0.8}
偏差容忍度 $υ$	{0.1, 0.2, 0.3, 0.4, 0.5}

表2

表3

算法参数数值

参数

最优取值

学习率

α

0.5

折扣因子

γ

0.95

探索率 $ε$

训练次数 $κ$

趋势权重 $χ$

偏差容忍度 $υ$

0.5

200

0.6

0.2

表3

图6

图7

图8

表4

图9

图10

图11

图12

表5

参考文献 21

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钢厂类型	钢板	型钢	高强度钢
A类	2 000	1 200	200
B类	1 000	700	—
C类	600	400	—

评估指标	A类钢厂	B类钢厂	C类钢厂
趋势匹配率/%	52.9	52.3	52.5
趋势匹配次数	396	391	393
平均价格偏差/(元/t)	220	277	328
平均价格偏差率/%	4.88	6.15	7.27

统计参数	14000TEU集装箱船			20000DWT散货船			8000DWT散货船
统计参数	模型实验	对比实验	提升/%	模型实验	对比实验	提升/%	模型实验	对比实验	提升/%
平均成本	1.72×10⁸	1.87×10⁸	8.02	1.34×10⁸	1.61×10⁸	16.77	0.59×10⁸	0.68×10⁸	13.24
标准差	0.19	0.26	26.92	0.17	0.29	41.38	0.05	0.07	28.57
极差	0.56	0.76	26.32	0.51	0.67	23.88	0.19	0.21	9.52
变异系数	0.11	0.14	21.43	0.13	0.18	27.78	0.08	0.10	20.00