基于大模型智能体的多班组施工过程仿真方法

doi:10.16182/j.issn1004731x.joss.25-0990

摘要/Abstract

摘要：

传统施工仿真方法通常依赖预设规则或静态调度机制，难以通过动态适应多班组施工场景模拟复杂约束下的交互决策，无法解决因工序依赖和空间占用造成的人力闲置问题。提出了一种基于大语言模型智能体的多班组施工过程仿真方法，构建了具备施工场景理解和推理能力的分布式班组智能体与中心化项目经理智能体，设计了“单经理多班组”施工多智能体交互决策机制，模拟在工序、空间与资源约束下的自主决策与协同机制。仿真结果表明：该方法能针对不同场景生成高效的施工流程，并涌现出适于场景的宏观施工策略，能够在“零样本”条件下生成多班组调度方案，无需迭代优化和训练即可达到领域先进调度算法水平，实现了案例项目人力资源的优化配置。

关键词: 土木工程施工, 施工仿真, 大语言模型, 智能体, 多智能体系统

Abstract:

Traditional construction simulation methods typically rely on predefined rules or static scheduling mechanisms, making it difficult to simulate interactive decision-making under complex constraints by dynamically adapting multi-crew construction scenarios. As a result, workforce idleness caused by process dependencies and spatial occupation fails to be solved. A simulation method for multi-crew construction processes based on a LLM-powered agent was proposed. The distributed crew agents endowed with construction scenario understanding and reasoning capabilities were constructed, as well as a centralized project manager agent. A “single-manager and multiple-crew” decision-making mechanism for multi-agent interaction in construction was designed. Autonomous decision-making and coordination mechanism under the constraints of process, space, and resource was simulated. The simulation results demonstrate that the proposed method can generate efficient construction workflows tailored to different scenarios and can give rise to macroscopic construction strategies adapted to scenarios. It also produces a multi-crew scheduling plan under zero-shot conditions, achieving advanced scheduling algorithm performance without iterative optimization or training and optimizing allocation of human resources for the case project.

Key words: construction of civil engineering, construction simulation, LLM, agent, multi-agent system

中图分类号:

TU722

王一凡,杨彬,汪丛军 . 基于大模型智能体的多班组施工过程仿真方法[J]. 系统仿真学报, 2026, 38(2): 488-500.

Wang Yifan,Yang Bin,Wang Congjun . Simulation Method for Multi-crew Construction Processes Based on Large Language Model-powered Agent[J]. Journal of System Simulation, 2026, 38(2): 488-500.

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任务名称	数量	安全距离/m	所需材料
接头清理	14	1.2
预制件吊装	14	2.5	预制件
灌浆	14	1.1
钢筋绑扎	22	1.5	钢筋
模板搭建	22	2.0	模板

班组名称	工作效率或任务时长期望	工人数目	工作区域占地面积/m²	移动速度/(m/s)
接头清理班组	11.8 min	3	4	0.4
预制件吊装班组	11.5 min	5	5	0.3
灌浆班组	5 min	1	1.5	0.6
钢筋绑扎班组	0.021 m³/min	2	2.4	0.7
模板搭建班组	0.495 m²/min	2	3.2	0.5

班组配置	指标	随机	最近距离	优先级规则	GA	RL	大模型智能体
5-Agent	平均值	24.20	24.19	23.93	24.02	24.02	23.87
5-Agent	最小值	23.90	23.91	23.37	23.07	23.22	23.57 (-0.5)
6-Agent	平均值	18.13	16.96	16.95	16.77	16.41	16.62 (-0.21)
6-Agent	最小值	17.39	16.79	16.73	16.55	15.27	15.94 (-0.67)
7-Agent	平均值	18.09	16.63	17.47	16.80	17.47	16.17
7-Agent	最小值	17.06	16.25	16.48	16.66	16.48	14.51

班组配置	指标	随机	最近距离	优先级规则	GA	RL	大模型智能体
5-Agent	平均值	3.81	3.79	3.96	3.55	3.63	3.54
5-Agent	最小值	3.51	3.71	3.62	3.40	3.45	3.40
6-Agent	平均值	3.97	3.43	3.28	3.26	2.48	2.69 (-0.21)
6-Agent	最小值	3.54	3.01	2.94	3.07	2.28	2.11
7-Agent	平均值	5.78	5.26	5.57	5.01	4.24	4.43 (-0.19)
7-Agent	最小值	5.26	4.89	4.74	4.92	3.83	3.74

班组配置	GA	RL
5-Agent	21.17	9.65
6-Agent	12.82	10.57
7-Agent	10.64	10.11