Journal of Manufacturing Systems最新文献_第3页

Collaborative optimization for multirobot manufacturing system reliability through integration of SysML simulation and maintenance knowledge graph 基于SysML仿真和维护知识图谱的多机器人制造系统可靠性协同优化

IF 12.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2025-04-21 DOI: 10.1016/j.jmsy.2025.04.010

Jian Zhou , Lianyu Zheng , Yiwei Wang

{"title":"Collaborative optimization for multirobot manufacturing system reliability through integration of SysML simulation and maintenance knowledge graph","authors":"Jian Zhou , Lianyu Zheng , Yiwei Wang","doi":"10.1016/j.jmsy.2025.04.010","DOIUrl":"10.1016/j.jmsy.2025.04.010","url":null,"abstract":"<div><div>In the rapidly advancing field of industrial automation, the reliability and maintenance of multirobot manufacturing systems are crucial. This paper proposes a collaborative optimization method for the reliability of multirobot system, combining SysML (System Modeling Language) model simulation with an operational and maintenance knowledge graph, aiming to ensure the reliable operation of multirobot manufacturing systems. The SysML model provides a comprehensive framework to represent the system architecture, workflows, and key parameters, identify critical components and potential bottlenecks, and perform detailed reliability analysis. Simultaneously, by embedding intelligent algorithms, the operational and maintenance knowledge graph enables automatic detection of operational anomalies and intelligent generation of maintenance strategies for industrial robots. By integrating the SysML model with the operational and maintenance knowledge graph, a collaborative optimization framework for the reliability of multirobot system is constructed. This framework not only dynamically adjusts key parameters in the simulation model, enhancing the accuracy and real-time performance of system reliability assessments, but also optimizes maintenance strategies based on system simulation indicators to ensure the reliable operation of multirobot system. Case studies validate that the proposed method improves the reliability of multirobot manufacturing systems, demonstrating that the combination of SysML simulation and the operational and maintenance knowledge graph can effectively address the complexity of modern manufacturing systems, offering significant reference value.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 749-775"},"PeriodicalIF":12.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-informed symbolic regression for tool wear and remaining useful life predictions in manufacturing 制造业中刀具磨损和剩余使用寿命预测的物理信息符号回归

IF 12.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2025-04-19 DOI: 10.1016/j.jmsy.2025.03.023

Seulki Han, Utsav Awasthi, George M. Bollas

{"title":"Physics-informed symbolic regression for tool wear and remaining useful life predictions in manufacturing","authors":"Seulki Han, Utsav Awasthi, George M. Bollas","doi":"10.1016/j.jmsy.2025.03.023","DOIUrl":"10.1016/j.jmsy.2025.03.023","url":null,"abstract":"<div><div>Prognostics and Health Management (PHM) plays a crucial role in enhancing the reliability and safety of engineering systems. Recently, physics-informed machine learning (PIML) methods have gained significant attention for their ability to incorporate domain-specific knowledge into data-driven models. This paper proposes a novel approach that integrates symbolic regression with recursive modeling to develop a robust framework for PHM of dynamic processes. Our framework was applied to a manufacturing process to build a generic model for tool wear prognostics across various machining scenarios. The proposed method integrates domain knowledge of milling processes under different conditions with recursive models using symbolic regression to achieve accurate and robust tool wear predictions. A recursive feature model and a recursive tool wear model were developedto accurately predict future tool wear, taking into consideration the strong correlation between features extracted from sensor signals and tool wear. The Genetic Programming-based Toolbox for Identification of Physical Systems (GPTIPS) was employed for symbolic regression. The results illustrate that the proposed framework can capture the dynamics of tool wear by recursively updating predictions with new data and can derive simple, interpretable mathematical expressions that represent the physical characteristics of the tool wear process. Benchmarking analysis demonstrated the effectiveness of the proposed approach, achieving lower root-mean-square error (RMSE) compared to other tool wear prognostic models in the literature.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 734-748"},"PeriodicalIF":12.2,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic synthesis augmented TimeGAN and adaptive temperature control for microwave heating 动态合成增强TimeGAN和自适应温度控制微波加热

IF 12.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2025-04-17 DOI: 10.1016/j.jmsy.2025.03.026

Jinhai Xu, Kuangrong Hao, Chengyang Meng, Yan Cheng, Xiaoyan Liu, Bing Wei

{"title":"Dynamic synthesis augmented TimeGAN and adaptive temperature control for microwave heating","authors":"Jinhai Xu, Kuangrong Hao, Chengyang Meng, Yan Cheng, Xiaoyan Liu, Bing Wei","doi":"10.1016/j.jmsy.2025.03.026","DOIUrl":"10.1016/j.jmsy.2025.03.026","url":null,"abstract":"<div><div>Microwave ovens are valued for their convenience and efficiency; however, many models still face issues with heating accuracy. While simulation analyses have made progress in addressing these challenges, the complexity and time requirements of multi-scenario data collection remain a challenge, as the lack of sufficient real-world data hinders the effective evaluation of model performance. To address this issue, we propose the Dynamic Synthesis Augmentation-TimeGAN (DSA-TGAN), which integrates a Discriminative Guided Warping (DGW) module to generate data that captures both the primary features of the heating process and additional perturbation information, effectively simulating the variations in microwave heating. The generated data serves as a pseudo-training set for TimeGAN, which is trained through an adaptive framework to produce sufficient experimental data. Additionally, we demonstrate that fine-tuning the pre-trained DSA-TGAN with a small amount of data from different microwave models enables successful transfer learning. Leveraging the synthetic data and feature analysis algorithms, we developed a process-adaptive temperature control method that enhances the accuracy and stability of microwave heating. Experimental results confirm that the DSA-TGAN model achieves the goals of high-quality data synthesis and effective transfer learning, significantly enhancing microwave heating performance. In addition, the proposed data augmentation model can be widely used in other microwave heating fields such as chemical processing and material synthesis.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 723-733"},"PeriodicalIF":12.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Challenges of randomness in tool wear with small samples: A physics-informed cross-domain monitoring method 小样本工具磨损随机性的挑战：一种物理信息跨域监测方法

IF 12.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2025-04-15 DOI: 10.1016/j.jmsy.2025.04.002

Delin Liu , Zhanqiang Liu , Bing Wang , Qinghua Song , Liangliang Li , Aisheng Jiang

{"title":"Challenges of randomness in tool wear with small samples: A physics-informed cross-domain monitoring method","authors":"Delin Liu , Zhanqiang Liu , Bing Wang , Qinghua Song , Liangliang Li , Aisheng Jiang","doi":"10.1016/j.jmsy.2025.04.002","DOIUrl":"10.1016/j.jmsy.2025.04.002","url":null,"abstract":"<div><div>Cutting tool wear monitoring is crucial for enabling predictive maintenance in machining processes. However, uncertainties in tool degradation during small-batch personalized machining present significant challenges to achieving accurate monitoring. This study addresses the randomness of tool wear through a dual-level approach: data and model. At the data level, an empirical tool wear model is developed based on nonlinear wear mechanisms, which is integrated with a domain-discriminative generative adversarial network to construct a target domain tool wear data generation framework. At the model level, a feature extractor tailored for transfer learning is designed using nonlinear relationships inherent in tool wear mechanisms, complemented by fine-tuning the classifier with the generated target domain tool life cycle data to handle domain shifts caused by randomness. The proposed method is validated using both public datasets and workshop experiments under both fixed and variable cutting conditions. Compared with baseline models, ablation models, and several state-of-the-art data generation and transfer learning models, the proposed approach demonstrates superior adaptability and robustness in handling the randomness in tool wear, even with highly imbalanced and small datasets. The results confirm the effectiveness of the proposed method in tool wear monitoring for small-batch personalized machining processes.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 694-722"},"PeriodicalIF":12.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient ship pipeline routing with dual-strategy enhanced ant colony optimization: Active behavior adjustment and passive environmental adaptability 基于双策略增强蚁群优化的船舶管道优化：主动行为调整和被动环境适应

IF 12.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2025-04-14 DOI: 10.1016/j.jmsy.2025.04.003

Xin Wang , Fengfeng Ning , Zemin Lin , Zhinan Zhang

{"title":"Efficient ship pipeline routing with dual-strategy enhanced ant colony optimization: Active behavior adjustment and passive environmental adaptability","authors":"Xin Wang , Fengfeng Ning , Zemin Lin , Zhinan Zhang","doi":"10.1016/j.jmsy.2025.04.003","DOIUrl":"10.1016/j.jmsy.2025.04.003","url":null,"abstract":"<div><div>The ship pipeline system is crucial as the transmission pathway for water, oil and gas, of which the layout design directly affects system efficiency, cost and safety. However, multiple objectives and constraints are involved in the large-scale ship pipe routing design problem, so the traditional ant colony algorithm is difficult to fully meet the requirements in terms of search efficiency and solution quality. This research proposes a Dual-Strategy Enhanced Ant Colony Optimization (DEACO) algorithm enhanced by both active and passive strategies. The active strategy, inspired by the behavior patterns of natural ant colonies, includes an adaptive greedy adjustment mechanism, heterogeneous pheromone deposition rule, and self-regulating pheromone secretion mechanism to enhance searching flexibility and efficiency. The passive strategy incorporates endpoint guidance enhancement and dynamic pheromone limits to adjust algorithm response, achieving fast path routing. Cases with two different environment settings show that DEACO outperforms traditional ACO, two latest ACOs and improved PSO in terms of key metrics such as pipe lengths and numbers of bends with faster computation speed. The algorithm achieves high stability within the same scenarios and strong robustness across various scenarios, yielding consistently favorable results despite randomness in searching and condition variations. Therefore, the proposed algorithm demonstrates effectiveness and superiority in ship pipeline automated routing.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 673-693"},"PeriodicalIF":12.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A bi-level supply chain resilience model using cloud manufacturing 采用云制造的双层供应链弹性模型

IF 12.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2025-04-13 DOI: 10.1016/j.jmsy.2025.03.020

Wei Ye , Shanshan Yang , Xingyu Li

{"title":"A bi-level supply chain resilience model using cloud manufacturing","authors":"Wei Ye , Shanshan Yang , Xingyu Li","doi":"10.1016/j.jmsy.2025.03.020","DOIUrl":"10.1016/j.jmsy.2025.03.020","url":null,"abstract":"<div><div>Globalization has heightened supply chain vulnerability to disruptions such as pandemics and natural disasters. Emerging digital transformation technologies, including digital supply chain and cloud manufacturing, offer a promising approach to mitigate disruptions and improve supply chain resilience by connecting manufacturers through shared information; however, it is often hindered by data security and privacy concerns. This study introduces a bi-level supply chain resilience model incorporating cloud manufacturing and a three-tier data privacy classification to balance efficiency, resilience, and privacy preservation. At the network level, <em>share-aggregated</em>, <em>safe-to-share</em> data optimizes task assignment; at the node level, suppliers locally schedule operations based on <em>confidential</em> data. Through case studies leveraging NSGA-II and Mixed-Integer Programming (MIP) for optimization, the model demonstrates a trade-off between resilience and operational efficiency. Results show that the bi-level approach enables dynamic supply chain adaptation while protecting sensitive supplier data, reducing lead times and transportation costs while maintaining supply chain resilience. These findings highlight the potential of cloud manufacturing as a scalable and privacy-preserving solution for enhancing supply chain resilience.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 662-672"},"PeriodicalIF":12.2,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep reinforcement learning approach with graph attention network and multi-signal differential reward for dynamic hybrid flow shop scheduling problem 基于图注意网络和多信号差分奖励的深度强化学习方法研究动态混合流水车间调度问题

IF 12.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2025-04-13 DOI: 10.1016/j.jmsy.2025.03.028

Youshan Liu, Jiaxin Fan, Weiming Shen

{"title":"A deep reinforcement learning approach with graph attention network and multi-signal differential reward for dynamic hybrid flow shop scheduling problem","authors":"Youshan Liu, Jiaxin Fan, Weiming Shen","doi":"10.1016/j.jmsy.2025.03.028","DOIUrl":"10.1016/j.jmsy.2025.03.028","url":null,"abstract":"<div><div>In real-life manufacturing systems, production management often faces uncertainty due to urgent demands and dynamic job insertions. Such uncertain environments pose significant challenges for scheduling, particularly in minimizing delivery delays and improving overall efficiency. Deep reinforcement learning (DRL) brings potential for rapid real-time production decisions, but scheduling in these environments with the objective of reducing delivery delays remains a challenging problem. This paper investigates a hybrid flow-shop dynamic scheduling problem with job insertions for minimizing the total weighted tardiness (TWT). An end-to-end DRL based method, the proximal policy optimization with graph attention network (PPO-GAT), is proposed to address the problem. First, a multi-agent system is established to simulate the actual manufacturing system and serve as a foundation for implementing intelligent production scheduling. Then, a novel graph-based state representation is developed to observe instantaneous states for the hybrid flow-shop. Two graph models are designed to represent system features and job features, and are extracted and fused by graph attention networks (GAT) to form the global feature. Afterwards, a multi-signal differential reward (MSDR) function is designed to address the intractable reward sparsity caused by the TWT objective. Finally, ablation experiments are conducted to validate all the proposed algorithmic components, and the PPO-GAT is compared with benchmark methods. Experimental results demonstrate the superiority of the proposed GAT, MSDR, and PPO-GAT. Moreover, the PPO-GAT has been proven to make real-time scheduling decisions for hybrid flow-shops with any scale, which can be considered as a promising solution for extensive industrial applications.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 643-661"},"PeriodicalIF":12.2,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel method for intelligent reasoning of machining step sequences based on deep reinforcement learning 基于深度强化学习的加工步骤序列智能推理新方法

IF 12.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2025-04-11 DOI: 10.1016/j.jmsy.2025.04.005

Biao Xiao , Zhengcai Zhao , Baode Xu , Yao Li , Wei Zhang , Haixiang Huan , Honghua Su

{"title":"A novel method for intelligent reasoning of machining step sequences based on deep reinforcement learning","authors":"Biao Xiao , Zhengcai Zhao , Baode Xu , Yao Li , Wei Zhang , Haixiang Huan , Honghua Su","doi":"10.1016/j.jmsy.2025.04.005","DOIUrl":"10.1016/j.jmsy.2025.04.005","url":null,"abstract":"<div><div>High-quality and efficient process planning methods are crucial for ensuring product manufacturing quality. However, traditional methods have several drawbacks, namely, they are time-consuming, highly dependent on expert experience, and involve considerable repetitive workloads. To overcome these limitations and enhance the efficiency and intelligence of process planning for complex structured parts, this study proposes a machining step sequence reasoning method based on deep reinforcement learning. First, historical process data are preprocessed to convert the knowledge stored in the process files into structured and vectorized data. Second, the process routes and feature step sets serve as inputs, and a proximal policy optimization algorithm is employed to train the historical process instances. The sequencing patterns discovered during training are then integrated with advanced sorting strategies to efficiently generate the machining step sequences. To evaluate the effectiveness of the proposed method, 50 complex structured parts were tested, with 25 representative parts selected for detailed comparative analysis. The training performance of the proposed algorithm was evaluated against those of the advantage actor-critic and soft actor-critic algorithms. In addition, the reasoning results of various state-of-the-art algorithms were analyzed using these test cases. Experimental results demonstrate that the proposed method is effective and competitive for process planning of complex structural parts. Therefore, this study provides practical guidance for enhancing the efficiency and intelligent automation of process planning of complex structural parts.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 626-642"},"PeriodicalIF":12.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A key process identification framework for aircraft assembly production based on the network with physical attributes 基于物理属性网络的飞机装配生产关键过程识别框架

IF 12.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2025-04-10 DOI: 10.1016/j.jmsy.2025.03.024

Jin-Hua Hu , Yan-Ning Sun , Wei Qin

{"title":"A key process identification framework for aircraft assembly production based on the network with physical attributes","authors":"Jin-Hua Hu , Yan-Ning Sun , Wei Qin","doi":"10.1016/j.jmsy.2025.03.024","DOIUrl":"10.1016/j.jmsy.2025.03.024","url":null,"abstract":"<div><div>Accurate identification of aircraft assembly key processes plays an important role in aircraft production management. However, due to complex processes, multiple attributes, and the aggregation phenomenon of the aircraft assembly process, identifying the key processes faces huge challenges. Therefore, a network-based key process identification framework is proposed in this paper. Firstly, according to assembly processes and vital physical attributes, an aircraft assembly network and the node attribute matrix are constructed. Then, the SC-<em>Q</em>-walktrap algorithm is designed to adaptively identify the aircraft assembly network community structure. Subsequently, the network-based influential node identification algorithm is proposed to recognize key process nodes, which consists of two steps. Within the community, local influence is evaluated based on node entropy and network topology. Between the communities, global influence is measured based on neighboring nodes in different communities. Finally, the proposed framework is compared with the traditional centrality measurements on the datasets from PSPLIB and commercial aircraft assembly datasets. The experiment results demonstrate that the network-based influential process identification algorithm can effectively identify the key processes.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":""},"PeriodicalIF":12.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg games 基于模块化状态的Stackelberg博弈在分布式制造系统中的自优化

IF 12.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2025-04-09 DOI: 10.1016/j.jmsy.2025.03.025

Steve Yuwono , Ahmar Kamal Hussain , Dorothea Schwung , Andreas Schwung

{"title":"Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg games","authors":"Steve Yuwono , Ahmar Kamal Hussain , Dorothea Schwung , Andreas Schwung","doi":"10.1016/j.jmsy.2025.03.025","DOIUrl":"10.1016/j.jmsy.2025.03.025","url":null,"abstract":"<div><div>In this study, we introduce Modular State-based Stackelberg Games (Mod-SbSG), a novel game structure developed for distributed self-learning in modular manufacturing systems. Mod-SbSG enhances cooperative decision-making among self-learning agents within production systems by integrating State-based Potential Games (SbPG) with Stackelberg games. This hierarchical structure assigns more important modules of the manufacturing system a first-mover advantage, while less important modules respond optimally to the leaders’ decisions. This decision-making process differs from typical multi-agent learning algorithms in manufacturing systems, where decisions are made simultaneously. We provide convergence guarantees for the novel game structure and design learning algorithms to account for the hierarchical game structure. We further analyse the effects of single-leader/multiple-follower and multiple-leader/multiple-follower scenarios within a Mod-SbSG. To assess its effectiveness, we implement and test Mod-SbSG in an industrial control setting using two laboratory-scale testbeds featuring sequential and serial–parallel processes. The proposed approach delivers promising results compared to the vanilla SbPG, which reduces overflow by 97.1%, and in some cases, prevents overflow entirely. Additionally, it decreases power consumption by 5%–13% while satisfying the production demand, which significantly improves potential (global objective) values.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 578-594"},"PeriodicalIF":12.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0