IET Collaborative Intelligent Manufacturing最新文献

A Digital Twin and Big Data-Driven Opti-State Control Framework for Production Logistics Synchronisation System 生产物流同步系统的数字孪生和大数据驱动的最优状态控制框架

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2025-04-29 DOI: 10.1049/cim2.70024

Yongheng Zhang, Zhicong Hong, Yafeng Wei, Ting Qu, Geroge Q. Huang

{"title":"A Digital Twin and Big Data-Driven Opti-State Control Framework for Production Logistics Synchronisation System","authors":"Yongheng Zhang, Zhicong Hong, Yafeng Wei, Ting Qu, Geroge Q. Huang","doi":"10.1049/cim2.70024","DOIUrl":"https://doi.org/10.1049/cim2.70024","url":null,"abstract":"The randomness and persistence of dynamic disturbances pose significant challenges to resource integration, task allocation, and goal setting within production logistics system. To maintain the optimal operational state of production logistics system over the long term, predictive planning and intervention must occur before disturbances arise, whereas adaptive adjustments are necessary to correct system states after disturbances occur. However, the effective implementation of these control strategies is hindered by several obstacles, such as a lack of comprehensive data and valuable knowledge, which impedes the support for opti-state control (OsC). Fortunately, with the advancements in information technologies such as the IoT and digital twins, it is now possible to collect and process vast amounts of real-time, full-lifecycle big data, thereby enabling more informed optimisation decisions. This paper proposes a digital twin and big data-based opti-state control system (DTBD-OsCS). The architecture integrates big data analytics and service-driven patterns, effectively addressing the aforementioned challenges. Within this framework, both predictive opti-state control (POsC) and adaptive opti-state control (AOsC) strategies are incorporated, along with the development of key technologies for implementing big data analysis. The proposed architecture's effectiveness is demonstrated through application scenarios, and experimental results and findings are thoroughly discussed. The results show that the proposed architecture significantly enhances the efficiency of production logistics systems and effectively reduces the cost impact of disturbances on the system.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resource-Efficient Anomaly Detection in Industrial Control Systems With Quantized Recurrent Variational Autoencoder 基于量化循环变分自编码器的工业控制系统资源高效异常检测

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2025-04-25 DOI: 10.1049/cim2.70032

Daniel Fährmann, Malte Ihlefeld, Arjan Kuijper, Naser Damer

{"title":"Resource-Efficient Anomaly Detection in Industrial Control Systems With Quantized Recurrent Variational Autoencoder","authors":"Daniel Fährmann, Malte Ihlefeld, Arjan Kuijper, Naser Damer","doi":"10.1049/cim2.70032","DOIUrl":"https://doi.org/10.1049/cim2.70032","url":null,"abstract":"This work presents a novel solution for multivariate time series anomaly detection in industrial control systems (ICSs), specifically tailored for resource-constrained environments. At its core, the quantized gated recurrent unit variational autoencoder (Q-GRU-VAE) architecture, a significant evolution from conventional methods, offers an extremely lightweight yet highly effective solution. By integrating gated recurrent units (GRUs) in place of long short-term memory (LSTM) cells within a variational autoencoder (VAE) framework, and employing channel-wise dynamic post-training quantization (DPTQ), this model dramatically reduces hardware resource demands. The proposed solution exhibits performance on par with existing methods on the widely used secure water treatment (SWaT) and water distribution (WADI) benchmarks, while being tailored towards applications where computational resources are limited. This dual achievement of minimal resource consumption and preserved model efficacy paves the way for deploying advanced anomaly detection in resource-constrained environments, marking a significant leap forward in enhancing the resilience and efficiency of ICSs.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated Optimisation of Shop Scheduling and Machine Layout for Discrete Manufacturing Considering Uncertain Events Based on an Improved Immune Genetic Algorithm 考虑不确定事件的离散制造车间调度与机器布局的改进免疫遗传算法集成优化

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2025-04-24 DOI: 10.1049/cim2.70022

Zhaoxi Hong, Yixiong Feng, Amir M. Fathollahi-Fard, Zhiwu Li, Bingtao Hu, Jianrong Tan

{"title":"Integrated Optimisation of Shop Scheduling and Machine Layout for Discrete Manufacturing Considering Uncertain Events Based on an Improved Immune Genetic Algorithm","authors":"Zhaoxi Hong, Yixiong Feng, Amir M. Fathollahi-Fard, Zhiwu Li, Bingtao Hu, Jianrong Tan","doi":"10.1049/cim2.70022","DOIUrl":"https://doi.org/10.1049/cim2.70022","url":null,"abstract":"Shop scheduling and machine layout are two important aspects of discrete manufacturing. There are strong coupling relationships between them, but they were conducted separately in the past, which significantly limits the production performance improvement of discrete manufacturing. At the same time, in the actual process of workshop production, uncertain events not only often occur but also may make the existing scheduling schemes no longer suitable. To address such issues, the integrated optimisation of shop scheduling and machine layout for discrete manufacturing considering uncertain events is proposed in this paper, where the minimum material handling cost, the maximum space utilisation rate and the minimum production completion time are selected as the optimisation objectives. An improved immune genetic algorithm is designed to solve the corresponding mathematical model efficiently by dual-layer encoding, which is good at global optimisation. Moreover, multistrategy redundancy-aware workshop rescheduling is performed to respond to uncertain events that are regarded as production disturbances. The rationality and superiority of the proposed method are verified by a numerical case study of a discrete manufacturing workshop for wood–plastic composite materials with its integrated optimisation of shop scheduling and machine layout, as well as its rescheduling schemes under machine failures.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel DQN-Based Hybrid Algorithm for Integrated Scheduling and Machine Maintenance in Dynamic Flexible Job Shops 一种基于dqn的动态柔性作业车间调度与机器维护集成混合算法

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2025-04-21 DOI: 10.1049/cim2.70028

Nanxing Chen, Yong Chen, Wenchao Yi, Zhi Pei

{"title":"A Novel DQN-Based Hybrid Algorithm for Integrated Scheduling and Machine Maintenance in Dynamic Flexible Job Shops","authors":"Nanxing Chen, Yong Chen, Wenchao Yi, Zhi Pei","doi":"10.1049/cim2.70028","DOIUrl":"https://doi.org/10.1049/cim2.70028","url":null,"abstract":"This paper focuses on the dynamic flexible job shop scheduling problem with constrained maintenance resources (DFJSP-CMR), a pressing challenge in modern manufacturing systems. As traditional rigid scheduling models fall short in meeting the demands of today's dynamic production environments, there is a growing need for intelligent approaches that can seamlessly integrate production scheduling and maintenance planning under resource limitations. To tackle this challenge, we propose a novel hybrid algorithm aimed at minimising makespan while addressing machine deterioration, unexpected failures and constrained maintenance resources. The core of our approach is a deep Q-network with maintenance insertion algorithm (DQN-MI) specifically designed for efficient maintenance scheduling. The algorithm features a 5×3 action space, constructed as compound rules, along with a reward structure that balances machine utilisation efficiency with effective maintenance operations. Extensive computational experiments conducted on diverse problem instances demonstrate that DQN-MI delivers superior performance, further validating the effectiveness and versatility of the proposed method in addressing complex scheduling challenges while maintaining the stability and reliability of manufacturing systems. This research contributes to the advancement of intelligent manufacturing by presenting a robust and practical solution for the integrated management of production scheduling and maintenance planning.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scheduling Reentrant FlowShops: Reinforcement Learning-guided Meta-Heuristics 调度可重入流商店：强化学习引导的元启发式

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2025-04-05 DOI: 10.1049/cim2.70029

Jingwen Yuan, Kaizhou Gao, Adam Slowik, Benxue Lu, Yanan Jia

{"title":"Scheduling Reentrant FlowShops: Reinforcement Learning-guided Meta-Heuristics","authors":"Jingwen Yuan, Kaizhou Gao, Adam Slowik, Benxue Lu, Yanan Jia","doi":"10.1049/cim2.70029","DOIUrl":"https://doi.org/10.1049/cim2.70029","url":null,"abstract":"The reentrant flowshop scheduling problems (RFSP) are ubiquitous in high-tech industries such as semiconductor manufacturing and liquid crystal display (LCD) production. Given the complexity of RFSP, it is significant to improve the production efficiency using effective intelligent optimisation techniques. In this study, four meta-heuristics assisted by two reinforcement learning (RL) algorithms are proposed to minimise the maximum completion time (makespan) for RFSP. First, a mathematical model for RFSP is established. Second, four meta-heuristics are improved. The Nawaz–Enscore–Ham (NEH) heuristic is utilised for population initialisation. Based on the problem characteristics, we design six local search operators, which are integrated into the four meta-heuristics. Third, two RL algorithms, Q-learning and state–action-reward–state–action (SARSA), are employed to select the appropriate local search operator during iterations to enhance the convergence in a local space. Finally, the results of solving 72 instances indicate that the proposed algorithms perform effectively. The RL-guided local search can significantly enhance the overall performance of the four meta-heuristics. In particular, the artificial bee colony algorithm (ABC) combined with SARSA-guided local search yields the highest performance.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comprehensive Systematic Literature Review on Cognitive Workload: Trends on Methods, Technologies, and Case Studies 认知负荷的综合系统文献综述：方法、技术和案例研究的趋势

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2025-03-14 DOI: 10.1049/cim2.70025

A. Lucchese, A. Padovano, F. Facchini

{"title":"Comprehensive Systematic Literature Review on Cognitive Workload: Trends on Methods, Technologies, and Case Studies","authors":"A. Lucchese, A. Padovano, F. Facchini","doi":"10.1049/cim2.70025","DOIUrl":"https://doi.org/10.1049/cim2.70025","url":null,"abstract":"Cognitive workload (CWL) assessment has gained traction in Industry 4.0 and 5.0, where human-machine interactions are becoming more intricate. However, there is a lack of comprehensively addressed CWL assessment by considering methodologies, technologies, and case studies. The present work reviews 70 articles related to the CWL assessment. The review identifies five main methodologies for the CWL assessment: physiological measures (e.g. EEG, HRV, and eye-tracking), subjective evaluation (e.g. NASA-TLX), performance evaluation, cognitive load models, and multimodal approaches. The analysis shows an increasing trend towards multimodal approaches that combine subjective assessment methods with physiological measures obtained from electroencephalography, eye-tracking, and heart rate monitoring devices. Additionally, emerging technologies such as augmented reality and collaborative robots are increasingly considered in case studies that address the CWL assessment in current work environments. Results reveal significant advancements in physiological and multimodal assessment methods, particularly emphasising real-time monitoring capabilities and context-specific applications. Case studies underscore the key role of CWL management in assembly, maintenance, and construction tasks, demonstrating its impact on performance, safety, and adaptability in dynamic environments. This review establishes a framework for advancing CWL research by addressing methodological limitations and proposing future research directions, including the development of personalised, adaptive systems for real-time workload management.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Event-Triggered Consensus for Switched Nonlinear Systems in Intelligent Manufacturing 智能制造中切换非线性系统的动态事件触发一致性

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2025-03-14 DOI: 10.1049/cim2.70023

Shanyan Hu, Mengling Wang, Yixiong Feng, Yan Jiang, Lie Chen

{"title":"Dynamic Event-Triggered Consensus for Switched Nonlinear Systems in Intelligent Manufacturing","authors":"Shanyan Hu, Mengling Wang, Yixiong Feng, Yan Jiang, Lie Chen","doi":"10.1049/cim2.70023","DOIUrl":"https://doi.org/10.1049/cim2.70023","url":null,"abstract":"Multiagent cooperative control enhances system efficiency through the facilitation of distributed collaboration, demonstrating significant applications in intelligent manufacturing. As a fundamental issue of cooperative control, multiagent consensus has been implemented extensively in numerous domains. Therefore, this paper studies the asymptotic consensus issue of a nonlinear system under switching topologies. The changeable topological structures hinder the system's ability to stabilise or require a substantial amount of time for stabilisation. To address this issue, we have incorporated topological information into the traditional Riccati equation. Subsequently, a topology-based dynamic event-triggered mechanism is presented by introducing an internal dynamic variable based on the solution of the Riccati equation. Furthermore, this research proposes a novel control protocol that utilises the full information of the switching topologies. This protocol contains a changeable control gain, which allows for the adjustment of the control law in response to the communication topology. Then, the Lyapunov stability theory guarantees that the nonlinear system reaches an asymptotic consensus under the proposed control law. This study also proves that the system does not exhibit Zeno behaviour. Ultimately, the simulation results confirm the viability of the control protocol.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Barriers for the Implementation of Industry 4.0 in Storage Drive Manufacturing Industry 存储驱动器制造业实施工业4.0的障碍

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2025-03-14 DOI: 10.1049/cim2.70026

Thurein Lin, Jirapan Liangrokapart

{"title":"Barriers for the Implementation of Industry 4.0 in Storage Drive Manufacturing Industry","authors":"Thurein Lin, Jirapan Liangrokapart","doi":"10.1049/cim2.70026","DOIUrl":"https://doi.org/10.1049/cim2.70026","url":null,"abstract":"Employing advanced technology in manufacturing will improve productivity and resource efficiency as well as reduce long term operating cost. Storage drive manufacturers focus on the advanced technology adoption as a way to reduce their operating cost. Despite many benefits of Industry 4.0, integration and implementation are not easily achievable. This research aims to identify the barriers of Industry 4.0 implementation in storage drive industry in the context of hard disk drive (HDD) and solid-state drive (SSD) manufacturing and to suggest guidelines to overcome the barriers. Starting from extensive literature review, followed by expert justification, 15 barriers for the implementation of Industry 4.0 in storage drive manufacturing industry were identified. The fuzzy AHP approach was used to prioritise the barriers. The study found that for both HDD and SSD industries, ‘economic’ criteria is the priority followed by ‘technology’ and ‘organisation’ criteria. The result suggests that decision makers should find avenues to overcome these three barriers before implementing Industry 4.0 in the storage drive manufacturing industry. Getting sufficient financial fund for capital investment, being technological-oriented organisation and getting strong management support for new technology are the main guideline for the industry. The research methodology in this study could be applied in other manufacturing industries to identify barriers and plan for strategic actions before the intelligent manufacturing implementation.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extending the Welding Seams Detection as Preparation Towards the Digital Twin Technology 扩展焊缝检测作为数字孪生技术的准备

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2025-03-12 DOI: 10.1049/cim2.70027

János Hegedűs-Kuti, József Szőlősi, Márton Tamás Birosz, Attila Csobán, Izolda Popa-Müller, Mátyás Andó

{"title":"Extending the Welding Seams Detection as Preparation Towards the Digital Twin Technology","authors":"János Hegedűs-Kuti, József Szőlősi, Márton Tamás Birosz, Attila Csobán, Izolda Popa-Müller, Mátyás Andó","doi":"10.1049/cim2.70027","DOIUrl":"https://doi.org/10.1049/cim2.70027","url":null,"abstract":"Detection and identification of defects in manufactured products, a task related to the basic requirements of quality management systems. By moving to higher levels, under the right conditions, these defects can be avoided, for example, by preventing manufacturing defects from occurring. Quality control and monitoring of welds are closely linked to the requirements of Industry 4.0. In the case of welding processes, quality assurance is a multifaceted area, including not only the analysis of input parameters but also the quality of the weld surface. By superimposing the point clouds of the parts under test, geometric features are generated to the initial manufacturing parameters to help increase manufacturing efficiency. In our work, the information data recorded by the data acquisition framework, which is captured during the welding process, is integrated with the outputs of the point cloud characteristics of the examined by the structured light scanning technology, as well as the value of the seam width magnitude extracted by the image recognition algorithms. This contributes to the possibilities of broadening the seam detection processes.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Agent-based simulation system for optimising resource allocation in production process 基于agent的生产过程资源优化配置仿真系统

IF 2.5

IET Collaborative Intelligent Manufacturing Pub Date : 2025-02-17 DOI: 10.1049/cim2.70020

Jingjing Zhao, Fan Zhang

{"title":"Agent-based simulation system for optimising resource allocation in production process","authors":"Jingjing Zhao, Fan Zhang","doi":"10.1049/cim2.70020","DOIUrl":"https://doi.org/10.1049/cim2.70020","url":null,"abstract":"Efficient sequencing of processes and resource allocation are critical in production planning scenarios, such as manufacturing workshops and construction projects, to enhance efficiency and reduce operational costs. Resource allocation in such environments is often challenged by temporal constraints, process interdependencies, and resource limitations, which complicate scheduling and increase the risk of delays. This study presents a multi-agent-based simulation system to address these challenges. A scheduling optimisation model is developed to simulate and optimise resource allocation in complex processes with network structures and temporal constraints. The primary objective is to minimise production completion time while ensuring effective resource allocation. Additionally, an adaptive, partially distributed Agent-Based Modelling and Simulation framework is proposed to simulate the execution logic of real-world processes, integrating key factors such as resource limitations, process interdependencies, and real-time decision-making. A priority-based genetic algorithm is also designed and embedded into the multi-agent system to further optimise process sequencing and resource distribution. Simulation experiments across varying case scales validate the model and algorithm. This study highlights the potential of agent-based simulation for solving complex engineering challenges and provides new insights for addressing resource allocation problems in network-structured, time-constrained environments.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0