基于蒙特卡洛树搜索的自组织制造车间任务调度优化

王立平; 赵振; 刘长春; 唐敦兵

doi:10.11959/j.issn.2096-3750.2025.00468

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基于蒙特卡洛树搜索的自组织制造车间任务调度优化

理论与技术 | 更新时间：2026-01-04

- 基于蒙特卡洛树搜索的自组织制造车间任务调度优化
- Optimization of task scheduling in self organizing manufacturing workshop based on Monte Carlo tree search
- 物联网学报 2025年9卷第4期页码：91-104
- 作者机构：
  
  南京航空航天大学机电学院，江苏南京 210016
- 作者简介：
  
  [ "王立平（1990‒ ），男，博士，南京航空航天大学机电学院助理研究员，主要研究方向为自组织智能制造系统及其关键使能技术。" ]
  [ "赵振（1998‒ ），男，南京航空航天大学机电学院博士生，主要研究方向为工业大模型驱动的多智能体智能制造系统。" ]
  [ "刘长春（1995‒ ），男，博士，南京航空航天大学机电学院助理研究员，主要研究方向为基于深度强化学习的设备状态分析与人机协作。" ]
  [ "唐敦兵（1972‒ ），男，博士，南京航空航天大学机电学院教授，主要研究方向为工业互联网与数字孪生。" ]
- 基金信息：
  
  国家自然科学基金重大研究计划项目(92267109);国防基础科学研究计划项目(JCKY2023203C026);国家资助博士后研究人员计划B档项目(GZB20240972);中国博士后科学基金资助项目(2024M754122);江苏省自然科学基金资助项目(BK20241389);江苏省卓越博士后计划项目(2024ZB194)
- DOI：10.11959/j.issn.2096-3750.2025.00468
  中图分类号：
- 收稿：2024-11-28，
  
  修回：2025-01-23，
  
  纸质出版：2025-12-10
- 稿件说明：
移动端阅览
王立平,赵振,刘长春等.基于蒙特卡洛树搜索的自组织制造车间任务调度优化[J].物联网学报,2025,09(04):91-104.

WANG Liping,ZHAO Zhen,LIU Changchun,et al.Optimization of task scheduling in self organizing manufacturing workshop based on Monte Carlo tree search[J].Chinese Journal on Internet of Things,2025,09(04):91-104.
王立平,赵振,刘长春等.基于蒙特卡洛树搜索的自组织制造车间任务调度优化[J].物联网学报,2025,09(04):91-104. DOI： 10.11959/j.issn.2096-3750.2025.00468.

WANG Liping,ZHAO Zhen,LIU Changchun,et al.Optimization of task scheduling in self organizing manufacturing workshop based on Monte Carlo tree search[J].Chinese Journal on Internet of Things,2025,09(04):91-104. DOI： 10.11959/j.issn.2096-3750.2025.00468.

摘要

自组织制造模式结合现有的工业互联技术、无线网络技术、分布式计算技术和人工智能技术并加以应用，将传统制造资源封装为具有高自主性、高适应性和高功能性的制造单元，并通过与其他制造单元的交互，完成制造任务的自组织协商分配。在这个过程中，为了构建制造任务智能分配和自组织资源配置的快速响应机制，实现制造任务与制造资源的高效、动态匹配，将多智能体合同网协议与蒙特卡洛树搜索算法相结合，提出了制造车间控制系统自组织运作机制与制造任务调度优化方法。最后，通过离散车间任务分配案例对所提方法的实际可行性进行了验证。实验结果证明，该方法更易实现“单步协商、全域寻优”这一目标。

Abstract

The self organizing manufacturing model was combined with the existing industrial interconnection technology

wireless network technology

distributed computing technology

and artificial intelligence technology

which can encapsulate traditional manufacturing resources into manufacturing units with high autonomy

adaptability

and functionality. Through interaction with other manufacturing units

self-organizing negotiation and allocation of manufacturing tasks were completed. To develop a rapid response mechanism for the intelligent allocation of manufacturing tasks and self-organizing resource distribution

the multi-agent contract net protocol (CNP) was integrated with the Monte Carlo tree search (MCTS) algorithm. Based on this

dynamic matching between manufacturing tasks and resources was achieved. Apart from this

a self-organizing operational mechanism and an optimization method were accomplished for the scheduling of manufacturing workshop control systems. Finally

the practical feasibility of the proposed method was verified through a discrete workshop task allocation case. The experimental results indicate that the method can better achieve the goal of "one-step negotiation and global optimization".

关键词

Keywords

references

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