Journal of Industrial Information Integration最新文献

APPD:An Auditable and Privacy-Preserving Data Sharing scheme for Cloud-assisted Industrial Internet of Things APPD：面向云辅助工业物联网的可审计且隐私保护的数据共享方案

IF 10.4 1区计算机科学

Journal of Industrial Information Integration Pub Date : 2026-05-01 Epub Date: 2026-02-03 DOI: 10.1016/j.jii.2026.101084

Yanbo Yang , Yating Guo , Jiawei Zhang , Zhuo Ma , Jianfeng Ma

{"title":"APPD:An Auditable and Privacy-Preserving Data Sharing scheme for Cloud-assisted Industrial Internet of Things","authors":"Yanbo Yang , Yating Guo , Jiawei Zhang , Zhuo Ma , Jianfeng Ma","doi":"10.1016/j.jii.2026.101084","DOIUrl":"10.1016/j.jii.2026.101084","url":null,"abstract":"<div><div>Cloud-assisted Industrial Internet of Things (IIoT) is prevalent in offering high quality industrial service by accommodating a huge volume of industrial data to eliminate the heavy burden of resource-limited smart devices and providing convenient industrial data sharing services for participants. However, the outsourced industrial data in remote cloud contain strongly sensitive information of manufacturing and are essential for decisions with analysis. Unauthorized access by malicious users or even destruction to these data will cause severe privacy leakage or manufacturing negligence. Thus, access control, privacy preserving and data integrity are of great significance to industrial data sharing in Cloud-assisted IIoT. Although Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a powerful tool for cloud data sharing, it incurs several limitations when used in industry field. Many existing schemes lack the ability to deal with data integrity violation, malicious user revocation and user privacy leakage of cleartext access policy simultaneously. Meanwhile, the key escrow is also an important security risk. As a countermeasure, in this paper, we propose an Auditable and Privacy Preserving Data Sharing Framework (APPD) for Cloud-assisted IIoT. In our framework, we devise a novel decentralized CP-ABE scheme with large universe and data auditing to achieve both fine-grained access control with key escrow resistance over unbounded attributes and data integrity guarantee. The full policy hiding and user revocation mechanisms are employed to prevent user privacy from being leaked by access policy and malicious users. At last, we present detailed formal security analysis for our proposal and the thorough performance assessment also demonstrates its feasible in IIoT application.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"51 ","pages":"Article 101084"},"PeriodicalIF":10.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prior knowledge-embedded first-layer interpretable paradigm for rail transit vehicle human–computer collaboration fault monitoring 基于先验知识的轨道交通车辆人机协同故障监测第一层可解释范式

IF 10.4 1区计算机科学

Journal of Industrial Information Integration Pub Date : 2026-05-01 Epub Date: 2026-02-06 DOI: 10.1016/j.jii.2026.101068

Chao He , Hongmei Shi , Jing-Xiao Liao , Bin Liu , Qiuhai Liu , Jianbo Li , Zujun Yu

{"title":"Prior knowledge-embedded first-layer interpretable paradigm for rail transit vehicle human–computer collaboration fault monitoring","authors":"Chao He , Hongmei Shi , Jing-Xiao Liao , Bin Liu , Qiuhai Liu , Jianbo Li , Zujun Yu","doi":"10.1016/j.jii.2026.101068","DOIUrl":"10.1016/j.jii.2026.101068","url":null,"abstract":"<div><div>Rail transit vehicles endure large loads, high speeds, and harsh environment, leading to component failure. The first-layer interpretable paradigm (FLIP) embeds human prior knowledge into smart equipment, which is one of intelligent paradigms guided by customized manufacturing and embodied intelligence. It consists of first-layer interpretable modules, backbones, loss metrics. However, existing efforts rely on single-source information, an absence of interpretable backbones, an inability to feature fusion, thereby struggling with multi-excitation, coupled signals. To bridge this gap, a FLIP-based one-stage multi-view capsule fusion network (PIFCapsule) is proposed. Firstly, a signal processing prior-empowered first-layer interpretable module is devised to realize automatic parameter optimization and highlight the complementarity between multi-view features from different signal processing algorithms. Secondly, an interpretable capsule network serves as the backbone. To overcome the inefficiency and shortage of information fusion, an efficient attention fusion routing (AFR) is proposed to reduce the parameters (about 5.72 times) and the complexity (about 2.93 times) in contrast to the vanilla capsule-based networks. In response to the lack of physics-based constraints during training, a noise threshold amplitude ratio (NTAR) is posed as a regularization, which enhances weak periodic transient pulses by suppressing learned noises. The effectiveness and reliability are verified through three real-world rail transit vehicle datasets: PIFCapsule outperforms the state-of-the-art by 6.77% in accuracy with only ten samples. Given the lightweight nature, it holds substantial promise to be deployed in intelligent edge devices. Code is available at <span><span>https://github.com/liguge/PIFCapsule</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"51 ","pages":"Article 101068"},"PeriodicalIF":10.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Navigating digitalization and global value chains: Empirical insights from the Chinese manufacturing industry 数字化导航与全球价值链：来自中国制造业的实证分析

IF 10.4 1区计算机科学

Journal of Industrial Information Integration Pub Date : 2026-05-01 Epub Date: 2026-01-31 DOI: 10.1016/j.jii.2026.101083

Qingyu Zhang , Fakhar Shahzad , Chiranjibe Jana , Nikola Ivkovic , Gerhard-Wilhelm Weber

{"title":"Navigating digitalization and global value chains: Empirical insights from the Chinese manufacturing industry","authors":"Qingyu Zhang , Fakhar Shahzad , Chiranjibe Jana , Nikola Ivkovic , Gerhard-Wilhelm Weber","doi":"10.1016/j.jii.2026.101083","DOIUrl":"10.1016/j.jii.2026.101083","url":null,"abstract":"<div><div>In a rapidly digitalized and globalized world, enterprises understand how digitalization shapes the global value chain (GVC) to remain competitive. Previous studies have examined digitalization, trade openness, research and development (R&D) investment, foreign direct investment (FDI), and infrastructure quality, leaving a gap in understanding the integrated determinants of GVC. This study aims to fill this research gap by examining the integrated impact of digitalization on GVC. Unlike previous studies, this study develops a holistic framework that captures a multidimensional analysis of the interaction between digitalization and GVC participation. This study used panel data models to achieve the desired outcomes from China’s manufacturing sector, and the results were obtained using Machine Learning Techniques. This study shows that manufacturing, domestic and foreign digitalization, research and development, productivity, and GVC participation all improve a GVC’s position; however, foreign direct investment hampers this improvement. Trade openness, financial growth, and infrastructure all positively impact the relationship between digitalization and the GVC position. By explicitly integrating digital technologies with broader economic and institutional factors, these findings offer a comprehensive understanding of the drivers of GVC competitiveness and provide actionable insights for the manufacturing sectors of emerging economies undergoing rapid digital transformation.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"51 ","pages":"Article 101083"},"PeriodicalIF":10.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Industrial information integration through autonomous AI agents: paradoxical effects on transparency, dehumanization, and responsible retail operations 通过自主人工智能代理的工业信息集成：对透明度、非人性化和负责任的零售运营的矛盾影响

IF 10.4 1区计算机科学

Journal of Industrial Information Integration Pub Date : 2026-05-01 Epub Date: 2026-02-03 DOI: 10.1016/j.jii.2026.101089

Shaofeng Wang , Hao Zhang

{"title":"Industrial information integration through autonomous AI agents: paradoxical effects on transparency, dehumanization, and responsible retail operations","authors":"Shaofeng Wang , Hao Zhang","doi":"10.1016/j.jii.2026.101089","DOIUrl":"10.1016/j.jii.2026.101089","url":null,"abstract":"<div><div>Industrial information integration increasingly relies on autonomous artificial intelligence agents to synthesize operational data, coordinate cross-functional processes, and execute real-time decisions in retail environments. This study investigates the paradoxical effects of AI-enabled industrial informatization on operational transparency, algorithmic dehumanization, and responsible performance outcomes. Through a mixed-methods analysis of 419 cross-border e-commerce firms employing structural equation modeling, importance-performance mapping, fuzzy-set qualitative comparative analysis, and executive interviews, we uncover counterintuitive dynamics in human-technology information integration. Results demonstrate that AI agent autonomy enhances operational transparency and reduces algorithmic dehumanization by creating audit-ready information architectures and filtering impersonal computational tasks from human-centric work. However, human-centric governance mechanisms introduce bureaucratic friction that paradoxically weakens these positive integration effects. We identify two mechanisms—\"enlightened autonomy,\" wherein sophisticated systems necessitate built-in information traceability, and \"humanity-enabling filters,\" wherein automation redirects human attention toward relational tasks. These findings challenge prevailing assumptions about AI opacity in industrial information systems and reveal that governance structures intended to safeguard ethical integration may inadvertently undermine the benefits of advanced industrial informatization. The study contributes to industrial information integration theory by demonstrating that autonomous technological systems, when properly architected for information transparency, can foster more responsible and human-compatible industrial operations than traditional management approaches.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"51 ","pages":"Article 101089"},"PeriodicalIF":10.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A delphi-informed fuzzy multi-criteria decision framework for prioritizing sustainable development goals in industrial strategy: an application to the paint and coatings sector 工业战略中可持续发展目标优先排序的德尔菲信息模糊多标准决策框架：在油漆和涂料行业的应用

IF 10.4 1区计算机科学

Journal of Industrial Information Integration Pub Date : 2026-05-01 Epub Date: 2026-02-05 DOI: 10.1016/j.jii.2026.101091

Seyed Pendar Toufighi , Reza Tafazoli , Sara Habibi , Jan Vang

{"title":"A delphi-informed fuzzy multi-criteria decision framework for prioritizing sustainable development goals in industrial strategy: an application to the paint and coatings sector","authors":"Seyed Pendar Toufighi , Reza Tafazoli , Sara Habibi , Jan Vang","doi":"10.1016/j.jii.2026.101091","DOIUrl":"10.1016/j.jii.2026.101091","url":null,"abstract":"<div><div>Industrial sustainability decision-making requires the integration of heterogeneous, uncertain, and expert-based information into actionable strategic priorities. Despite the widespread adoption of the Sustainable Development Goals (SDGs), managers still lack robust decision-support tools to prioritize SDGs and translate them into concrete industrial strategies under uncertainty. To address this gap, this study develops a Delphi-informed fuzzy multi-criteria decision analysis framework that integrates the fuzzy BWM and fuzzy TOPSIS to support SDG prioritization and sustainability-oriented capability ranking. The framework is empirically applied to the paint and coatings industry, a resource- and energy-intensive sector facing increasing sustainability pressures. Based on expert consensus, the results identify SDG 8 (decent work and economic growth), SDG 7 (affordable and clean energy), and SDG 9 (industry, innovation, and infrastructure) as the most critical priorities. Among alternative strategies, sustainability-oriented branding and innovation management emerge as the most effective capabilities for advancing these goals. The study contributes scientifically by operationalizing SDGs as computable decision criteria, integrating stakeholder legitimacy and resource-based perspectives into a unified decision-intelligence framework, and demonstrating how fuzzy techniques can support sustainability-driven industrial strategy formulation under uncertainty.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"51 ","pages":"Article 101091"},"PeriodicalIF":10.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep acoustic–visual fusion for robust material recognition in intelligent robotic perception 基于深度声视融合的智能机器人感知材料鲁棒识别

IF 10.4 1区计算机科学

Journal of Industrial Information Integration Pub Date : 2026-05-01 Epub Date: 2026-01-28 DOI: 10.1016/j.jii.2026.101074

Bo Zhu , Tao Geng , Jia Zhang , Jianlei Cui , Boxin Ren

{"title":"Deep acoustic–visual fusion for robust material recognition in intelligent robotic perception","authors":"Bo Zhu , Tao Geng , Jia Zhang , Jianlei Cui , Boxin Ren","doi":"10.1016/j.jii.2026.101074","DOIUrl":"10.1016/j.jii.2026.101074","url":null,"abstract":"<div><div>Accurate material recognition is crucial for intelligent robotic perception, enabling autonomous interaction, grasping, and navigation in complex environments. While traditional single-modality approaches often lack comprehensive information, which limits their performance, multimodal methods that combine acoustic and visual data provide a more robust solution by leveraging complementary cues. However, existing techniques face challenges in effectively integrating these modalities, resulting in suboptimal recognition accuracy under certain conditions. To address these limitations, we propose <span><math><msup><mrow><mi>M</mi></mrow><mrow><mn>3</mn></mrow></msup></math></span>CNet, a novel multimodal material classification network that incorporates adaptive frequency filtering, dual-branch feature fusion, cross-attention, and modality fusion attention. The adaptive frequency filtering block dynamically optimizes acoustic frequency bands to enhance the extraction of discriminative features. Meanwhile, the dual-branch feature fusion block captures local and global visual features at multiple scales, improving texture representation. To strengthen inter-modal relationships, the cross-attention block enables mutual reinforcement between acoustic and visual features, while the modality fusion attention block adaptively balances the contributions of each modality at both the channel and spatial levels. This ensures robustness even in the presence of incomplete or noisy data. Extensive experiments on multiple multimodal texture datasets demonstrate that <span><math><msup><mrow><mi>M</mi></mrow><mrow><mn>3</mn></mrow></msup></math></span>CNet consistently outperforms other methods in accuracy, precision, and recall.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"51 ","pages":"Article 101074"},"PeriodicalIF":10.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A nested goal programming model integrated with an improved genetic bee colony algorithm supported by machine learning methods 基于机器学习的改进遗传蜂群算法的嵌套目标规划模型

IF 10.4 1区计算机科学

Journal of Industrial Information Integration Pub Date : 2026-05-01 Epub Date: 2026-01-26 DOI: 10.1016/j.jii.2026.101082

N. Gosheh Dezfouli, Behnam Vahdani, E. Mehdizadeh, H.R. Gholami

{"title":"A nested goal programming model integrated with an improved genetic bee colony algorithm supported by machine learning methods","authors":"N. Gosheh Dezfouli, Behnam Vahdani, E. Mehdizadeh, H.R. Gholami","doi":"10.1016/j.jii.2026.101082","DOIUrl":"10.1016/j.jii.2026.101082","url":null,"abstract":"<div><div>Formulating engine oil additives is challenging because it requires simultaneously optimizing production efficiency, cost, and compliance with strict quality standards. This study presents an advanced optimization framework for 10W-40 API SL engine oil that combines a nested goal programming model with machine learning (ML) techniques to predict production rates and quality metrics that cannot be expressed in closed-form equations. To address the inability of conventional ML approaches to generate novel additive combinations, we propose an enhanced genetic bee colony algorithm incorporating arithmetic crossover, Makinen–Periaux–Toivanen mutation operators, and a Cauchy distribution-based local search. These modifications significantly improve the algorithm’s ability to explore and evaluate new formulations. The resulting framework achieves 98.76% of nominal production capacity—very close to the theoretical optimum—while reducing quality-related costs by an average of 20.44%. These results represent substantial improvements in production efficiency, cost savings, and overall formulation quality, providing a powerful and practical tool for the engine oil industry.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"51 ","pages":"Article 101082"},"PeriodicalIF":10.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedAHPIP: Federated Learning with Adaptive Hot Parameter Identification and Personalized Anchoring for multi-agent collaboration FedAHPIP：基于自适应热参数识别和个性化锚定的多智能体协作的联邦学习

IF 10.4 1区计算机科学

Journal of Industrial Information Integration Pub Date : 2026-05-01 Epub Date: 2026-02-05 DOI: 10.1016/j.jii.2026.101087

Cangming Liang , Zulong Diao , Xin Wang , Yingzi Huo , Kuanching Li , Dacheng He , Wei Liang

{"title":"FedAHPIP: Federated Learning with Adaptive Hot Parameter Identification and Personalized Anchoring for multi-agent collaboration","authors":"Cangming Liang , Zulong Diao , Xin Wang , Yingzi Huo , Kuanching Li , Dacheng He , Wei Liang","doi":"10.1016/j.jii.2026.101087","DOIUrl":"10.1016/j.jii.2026.101087","url":null,"abstract":"<div><div>Smart manufacturing seeks to achieve collective intelligence through collaboration. However, such collaboration must be secure and personalized to handle heterogeneous industrial agents. Federated learning offers a promising paradigm for this setting but faces two fundamental challenges: privacy leakage through gradient inversion attacks (e.g., DLG) and data heterogeneity requiring personalized models. To address these challenges, we propose FedAHPIP, a federated learning framework that integrates secure aggregation with personalized learning. Our approach includes an adaptive hot parameter identification mechanism that dynamically identifies sensitive parameters (hot parameters) based on their update momentum, layer semantics, and potential label leakage risks. By focusing encryption on these hot parameters, FedAHPIP drastically reduces the privacy leakage surface. We also develop a personalized anchoring strategy that allows each agent to retain its critical parameters while assimilating knowledge from the global model, effectively balancing personalization and collaboration. Extensive experiments on benchmark and industrial datasets demonstrate that FedAHPIP achieves superior personalized accuracy under extreme non-IID settings, provides robust security against DLG attacks, and maintains minimal computational overhead. FedAHPIP thus offers a practical solution for trustworthy collective intelligence in smart manufacturing environments.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"51 ","pages":"Article 101087"},"PeriodicalIF":10.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multi-level multi-source digital twin model for performance enhancement and optimization decision-making in precision milling machines 精密铣床性能提升与优化决策的多级多源数字孪生模型

IF 10.4 1区计算机科学

Journal of Industrial Information Integration Pub Date : 2026-05-01 Epub Date: 2026-01-23 DOI: 10.1016/j.jii.2026.101080

Yang Xie , Shulong Mei , Fei Wang , Chaoyong Zhang

{"title":"A multi-level multi-source digital twin model for performance enhancement and optimization decision-making in precision milling machines","authors":"Yang Xie , Shulong Mei , Fei Wang , Chaoyong Zhang","doi":"10.1016/j.jii.2026.101080","DOIUrl":"10.1016/j.jii.2026.101080","url":null,"abstract":"<div><div>The transition of CNC machining toward digitalization and low-carbon manufacturing is essential for the advancement of intelligent production. However, conventional parameter configuration methods fail to balance efficiency and sustainability. To overcome this limitation, this study proposes an intelligent optimization framework that integrates digital twin (DT) technology with multi-objective optimization. A multi-level virtual machine tool model is established to enable operational condition mapping and structural response modeling of key machining parameters. A Simulation Augmentation Collaboration Mechanism (SACM) is further introduced, in which the DT generates high-fidelity distribution information to guide a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) in producing realistic samples under critical operating conditions. These augmented data iteratively refine the model, significantly enhancing predictive generalization. An Improved Meta-Learning-Optimized XGBoost (IMeta-XGBoost) model is then established to predict three performance indicators: spindle energy consumption, specific cutting energy, and machining noise. A Predicted-Fitness-Guided Multi-Objective Deep Q-Network (PF-MO-DQN) is then employed for global optimization, followed by entropy-weighted TOPSIS to determine the optimal machining parameters experimental validation demonstrates reductions of 8.95% in spindle energy consumption, 18.03% in specific cutting energy, and 10.15% in machining noise, confirming significant improvements in energy efficiency, productivity, and noise mitigation. This work provides a robust and scalable approach for multi-objective optimization in complex machining environments.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"51 ","pages":"Article 101080"},"PeriodicalIF":10.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BIM-based construction scheduling optimization through graph neural network-driven spatial semantic reasoning 基于bim的图神经网络驱动空间语义推理施工调度优化

IF 10.4 1区计算机科学

Journal of Industrial Information Integration Pub Date : 2026-05-01 Epub Date: 2026-02-07 DOI: 10.1016/j.jii.2026.101088

Wenbo Zhang , Zhengtao Wang , Zhenqing Yang , Zhi Cai

{"title":"BIM-based construction scheduling optimization through graph neural network-driven spatial semantic reasoning","authors":"Wenbo Zhang , Zhengtao Wang , Zhenqing Yang , Zhi Cai","doi":"10.1016/j.jii.2026.101088","DOIUrl":"10.1016/j.jii.2026.101088","url":null,"abstract":"<div><div>With the widespread adoption of Building Information Modeling (BIM) in construction engineering, its embedded spatial semantic information provides a valuable foundation for intelligent task scheduling. However, existing methods often fail to fully exploit deep spatial relationships or adapt to dynamically evolving scenarios influenced by multiple constraints. To address these challenges, this paper proposes a construction scheduling optimization framework based on Graph Neural Network (GNN)-driven spatial semantic reasoning. First, a three-dimensional semantic graph is constructed from BIM data by integrating geometric, topological, and attribute information to explicitly represent relationships such as containment, embedding, and contact among components. Then, an enhanced GraphSAGE model is employed to learn implicit precedence dependencies, replacing static rule templates and improving the adaptability and generalization of scheduling logic. Furthermore, a Dynamic Layering Scheduling Algorithm (DLSA) is designed to exploit spatial adjacency and parallel constructability, enabling structured and controllable scheduling. A multi-dimensional priority model is also incorporated, accounting for component criticality, spatial position, and resource conflicts to dynamically generate construction sequences. Experiments conducted on three representative BIM project models demonstrate that the proposed framework improves resource utilization (RU) from 68.24% to 87.93% and reduces abnormal construction events (AC) from 185 to 41, representing a 19.69 percentage point gain in efficiency and a 77.8% reduction in conflicts. These results confirm the effectiveness, scalability, and industrial applicability of integrating GNN-driven reasoning with BIM for intelligent construction scheduling optimization.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"51 ","pages":"Article 101088"},"PeriodicalIF":10.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0