Journal of Industrial Information Integration最新文献

{"title":"Online dynamic task planning with environment observation for human-robot collaboration","authors":"Fan Zeng, Yusheng Wang, Shinsuke Kondoh, Shinji Kanda, Yukio Honda, Hiroshi Mizoguchi, Masahiro Nishio, Koji Makino, Jun Ota","doi":"10.1016/j.jii.2026.101227","DOIUrl":"https://doi.org/10.1016/j.jii.2026.101227","url":null,"abstract":"Human–robot collaboration has become essential for leveraging the complementary strengths of humans and robots to split their duties. Real-time dynamic task planning that balances task completion time and human fatigue is essential for improving system efficiency and flexibility. However, most existing studies focus primarily on local task feasibility, neglecting system-level optimization in terms of time efficiency, human fatigue, and personalized collaboration adaptability. To address these limitations and enhance adaptability in dynamic environments, this study proposes an innovative real-time dynamic task planning framework integrated with environment observation for human–robot collaboration. On the environment observation side, a lightweight recognition and prediction network with only 10 M parameters is developed, providing rapid and accurate human behavior inference. On the dynamic task planning side, a multi-objective, fast-search strategy that utilizes environment observation outputs is proposed to synchronize robot actions effectively with human dynamically in real-time. A simulated industrial production line involving object handling from shelves to workbench is built, and a dataset of over one million frames capturing human operations is collected. Experimental results indicate the proposed environment observation architecture can rapidly identify human behaviors within 0.025 s including recognizing and predicting human actions and target objects, along with predicting human long-term (3.0 s) motion. The system achieves high accuracies in action recognition (99.4%), action prediction (97.4%), target object identification (96.7%), target object prediction (98.1%), and the motion predictor achieves the lowest MPJPE at horizons from 1.5 to 3.0 s on both PPAH and Human3.6M. Additionally, online dynamic task planning can adapt robot actions according to human dynamics with computation consistently below 0.020 s in our experiments. These performance underscore the novelty and practical application potential of our proposed method in industrial human–robot collaboration.","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"495 1","pages":""},"PeriodicalIF":15.7,"publicationDate":"2026-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149292748","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}

{"title":"Intelligent human-centric manufacturing in industry 5.0: A review of large language model enabled extended reality systems","authors":"Christa Daryn P., Rishika R., Prasad Jones Christydass Samuel, S.K. Karthika, Eswaran M.","doi":"10.1016/j.jii.2026.101229","DOIUrl":"https://doi.org/10.1016/j.jii.2026.101229","url":null,"abstract":"Manufacturing systems are progressively anticipated to evolve from automation-focused efficiency to human-centered intelligence, adaptability, and collaborative decision-making, in accordance with the Industry 5.0 vision. The amalgamation of Large Language Models (LLMs) with Extended Reality (XR) establishes a formidable framework for incorporating cognitive reasoning, contextual awareness, and intuitive interaction directly into industrial settings. Although recent research have shown the efficacy of LLM–XR systems in task execution and operational support, their comprehensive impact throughout the manufacturing lifecycle has not been extensively investigated. This analysis analyzes LLM–XR integration from a lifecycle viewpoint by combining industrial studies primarily published between 2015 and January 2026. It encompasses the entire manufacturing lifecycle, including applications in design, planning, assembly and disassembly, maintenance, training, quality inspection, and human–robot collaboration. These innovations collectively signify a shift from localized operational assistance to shared cognitive platforms that facilitate spatial-semantic reasoning, explicable decision-making, and human-AI collaboration. The research delineates essential technological facilitators that support the efficient implementation of LLM-XR, encompassing multimodal interaction, spatially grounded knowledge enhancement, reasoning-based intelligence, and ergonomics-conscious system design. Furthermore, ongoing issues of system reliability, grounding in domain knowledge, computational limitations, and user trust are rigorously analyzed. This review synthesizes LLM–XR integration across the manufacturing lifecycle and develops a generalized conceptual reference architecture that organizes recurring functional components, interaction mechanisms, information flows, and adaptive feedback processes across design, planning, execution, and quality-related activities.","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"5 1","pages":""},"PeriodicalIF":15.7,"publicationDate":"2026-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149292532","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}

{"title":"Gradual disturbance detection model inspired by dynamic graph theory for digital twin manufacturing cell","authors":"Yaguang Zhou, Guanghui Zhou, Chao Zhang, Zenghui Wang, Jiancong Liu, Asoke K. Nandi","doi":"10.1016/j.jii.2026.101232","DOIUrl":"https://doi.org/10.1016/j.jii.2026.101232","url":null,"abstract":"","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"1 1","pages":""},"PeriodicalIF":15.7,"publicationDate":"2026-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149232240","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}

{"title":"DT-SOPS: A digital twin-enabled scheduling optimization system to bridge the model-reality gap with case studies in the steelmaking industry","authors":"Zhangsheng Su,Raymond Chiong,Sheng-Long Jiang,Lubing Sun,Min Chen","doi":"10.1016/j.jii.2026.101220","DOIUrl":"https://doi.org/10.1016/j.jii.2026.101220","url":null,"abstract":"","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"16 1","pages":"101220"},"PeriodicalIF":15.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149209013","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}

{"title":"Retraction notice to \"COVID-19 Automotive Supply Chain Risks: A Manufacturer-Supplier Development Approach\" [Journal of Industrial Information Integration 38 (2024) 100576]","authors":"Aminreza Karamoozian, Chin An Tan, Desheng Wu, Amirhossein Karamoozian, Saied Pirasteh","doi":"10.1016/j.jii.2026.101142","DOIUrl":"10.1016/j.jii.2026.101142","url":null,"abstract":"","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"52 ","pages":"Article 101142"},"PeriodicalIF":11.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148641319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement learning for model transformations to support model-driven plug-and-play interoperability","authors":"Quentin Brilhault,&nbsp;Esma Yahia,&nbsp;Lionel Roucoules","doi":"10.1016/j.jii.2026.101150","DOIUrl":"10.1016/j.jii.2026.101150","url":null,"abstract":"<div><div>The digital transformation of industry is prompting companies to improve the interoperability of their systems, consolidating the <em>Digital Thread</em> at the core of their organizations. Interoperability ensures digital continuity between heterogeneous and isolated systems across the value chain and product life cycle, enabling a smooth, uninterrupted flow of data among stakeholders. However, the continual emergence of new technologies and increasing demands for flexibility and scalability in the value chain make establishing and maintaining interoperability in heterogeneous, dynamic environments a significant challenge. To address these requirements, the <em>Model-Driven Interoperability</em> (MDI) framework provides a concrete solution for achieving self-configuring (<em>plug-and-unplug</em>) and self-adjustable (<em>update-and-play</em>) mechanisms to enable <em>plug-and-play</em> interoperability between systems and support the digital thread. MDI employs two key mechanisms: <em>projection mechanisms</em>, which ensure <em>syntactic mapping</em> between systems, and <em>model-to-model</em> transformations, which guarantee <em>semantic mapping</em> between heterogeneous models. In this paper, we present an innovative <em>reinforcement learning-based approach</em> to automate the generation of model-to-model transformations, addressing the need for a self-configuring and self-adjusting interoperability mechanism. This method autonomously learns transformation rules that connect distinct metamodel concepts, significantly reducing the development time and human effort required to create transformation models. By addressing the challenges of dynamic semantic mapping between heterogeneous models, the proposed approach enables adaptive and on-demand interoperability. A case study on the digital thread in the space sector concludes the article and demonstrates the impact of the proposed solution in a complex context such as intelligent planning.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"52 ","pages":"Article 101150"},"PeriodicalIF":11.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An information-integrated circularity passport for tribological systems: a trajectory-aware framework and IIoT-compatible scoring algorithm","authors":"Valentina Ndou,&nbsp;Sara Attanasio,&nbsp;Gioconda Mele,&nbsp;Michele Scaraggi,&nbsp;Nicola Menga","doi":"10.1016/j.jii.2026.101151","DOIUrl":"10.1016/j.jii.2026.101151","url":null,"abstract":"<div><div>Tribological systems, governing friction, wear, lubrication, and sealing, account for approximately 20–23% of global energy consumption and represent a structurally underserved domain for circular economy (CE) governance. Existing Digital Product Passports (DPPs), mandated under the EU Ecodesign for Sustainable Products Regulation, treat sustainability attributes as static product properties, yet tribological performance evolves continuously as a function of interface degradation, regime transitions, and operational duty cycles. This trajectory-based character makes conventional, snapshot-based circularity frameworks systematically inadequate for this class of industrial devices. Adopting a Design Science Research (DSR) methodology, this study develops and demonstrates a data-driven Circularity Passport framework specifically designed for tribological devices. The artefact comprises two coupled components: (i) a matrix-based conceptual framework that structures sustainability assessment across three lifecycle phases, production, use, and end-of-life, using four tribology-specific sustainability dimensions, with explicit operating-envelope constraints and provenance-tagged data entries; and (ii) a multi-criteria scoring algorithm that normalizes heterogeneous lifecycle indicators onto a common 0–100+ scale, aggregates them through a transparent two-level weighting scheme, and maps the composite index to a five-class chromatic ecolabeling output (A–E). The algorithm is designed for state-dependent recalculation when operational monitoring detects regime transitions, making it compatible with Industrial Internet of Things (IIoT) data streams and digital twin architectures. The framework is demonstrated through the exemplar case of passenger car tyres, integrating production-phase carbon emissions, rolling-resistance energy losses, durability relative to vehicle service life, recycled content, and end-of-life material recovery into a reproducible composite score. The study advances lifecycle theory by demonstrating that in degradation-governed systems, circularity governance requires trajectory-aware information integration.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"52 ","pages":"Article 101151"},"PeriodicalIF":11.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A causal-aware spatiotemporal network with prediction-reconstruction for interpretable anomaly monitoring in complex industrial processes","authors":"Bei Sun,&nbsp;Weilong Tong,&nbsp;Mingjie Lv,&nbsp;Fakun Zheng,&nbsp;Yucheng Ke","doi":"10.1016/j.jii.2026.101148","DOIUrl":"10.1016/j.jii.2026.101148","url":null,"abstract":"<div><div>Anomaly monitoring in complex industrial processes is challenged by high-dimensional, nonstationary sensor data exhibiting intricate inter-variable couplings. Existing deep learning approaches, whether based on flat sequence models or graph-structured representations, construct variable relationships from statistical correlations, conflating genuine process dependencies with spurious confounding effects and limiting detection reliability and fault interpretability. We propose the Causal-Aware Spatiotemporal Network with Prediction-Reconstruction (CaST-PR), trained solely on unlabeled normal operating data. A Sequential Causal Graph Inference (SCGI) module learns directed inter-variable dependencies via a gated predictive causal inference mechanism with permutation-based edge validation. The SCGI's gated adjacency output guides a spatiotemporal encoder integrating time-frequency feature extraction with residual graph attention; the permutation-refined causal graph enables root-cause tracing by overlaying per-variable anomaly contributions to isolate initiating fault variables from downstream consequences, supporting actionable maintenance decisions within enterprise information workflows. Anomalies are detected via variational residual reconstruction, yielding a statistically grounded anomaly score. Validated on a benchmark dataset and two real-world industrial cases, CaST-PR achieves near-zero false alarm rates with high fault detection rates, consistently outperforming seven baselines. Our code is publicly available at <span><span>https://github.com/wuyouxiaozhu/Code</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"52 ","pages":"Article 101148"},"PeriodicalIF":11.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288671","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}

{"title":"A scalable deep learning framework for job completion time prediction in cloud-edge environments","authors":"Sicheng Liu,&nbsp;Lingyan Li,&nbsp;Fujia Sun,&nbsp;Renzhe Zhang","doi":"10.1016/j.jii.2026.101154","DOIUrl":"10.1016/j.jii.2026.101154","url":null,"abstract":"<div><div>Accurate makespan prediction is essential for optimizing scheduling and operational decisions in collaborative manufacturing environments, particularly under conditions of uncertainty and dynamic system behavior. However, conventional prediction models often fall short in capturing complex non-linear spatial–temporal dependencies, adapting to evolving shop-floor states, and handling noisy inputs or service disruptions commonly encountered in practice. To overcome these limitations, this study presents a robust hybrid deep learning framework that combines an attention-based configurable convolutional neural network with an attention-enhanced bidirectional long short-term memory network. The proposed architecture is designed to selectively emphasize salient machine–task interactions and model temporal dependencies in both forward and backward directions, thereby enhancing resilience to incomplete data and transient service failures. To evaluate its effectiveness, extensive experiments were conducted under diverse scheduling scenarios, including metaheuristic, reinforcement learning, and agent-based strategies. Results demonstrate that the proposed model consistently outperforms a range of traditional deep learning baselines and advanced neural architectures, achieving up to a 24.2% reduction in mean squared error and a 24.7% improvement in mean absolute percentage error. These findings underscore the model’s robustness and practical value for real-time, reliable makespan prediction in complex manufacturing settings.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"52 ","pages":"Article 101154"},"PeriodicalIF":11.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148313232","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}

{"title":"Human-Robot Collaborative Supply Chain Scheduling via Reinforcement Learning, Large Neighborhood Search, and Large Language Models","authors":"Weijian Zhang,&nbsp;Chao Zuo,&nbsp;Min Kong,&nbsp;Tinglong Zhang,&nbsp;Amir M. Fathollahi-Fard,&nbsp;Maxim A. Dulebenets","doi":"10.1016/j.jii.2026.101152","DOIUrl":"10.1016/j.jii.2026.101152","url":null,"abstract":"<div><div>Compressors are critical components in automobile manufacturing, and delays in their production may directly affect downstream vehicle assembly. Motivated by this practical challenge, this study investigates a supply chain-oriented distributed hybrid flow shop scheduling problem (SCDHFSSP), in which multiple factories jointly perform production and testing tasks under coordinated supply chain requirements. To formulate this problem, a mixed-integer linear programming model is developed. Since the problem is highly complex optimization model, a reinforcement learning-enhanced adaptive large neighborhood search algorithm, termed R-ALNS, is proposed. In the proposed framework, reinforcement learning is used to adaptively select destruction and repair operators, thereby improving the search efficiency and robustness of ALNS. Extensive computational experiments and statistical analyses show that R-ALNS consistently outperforms classical meta-heuristic algorithms, achieving performance improvements of more than 10% across diverse benchmark instances. Comparisons with the Gurobi solver and several state-of-the-art hybrid meta-heuristics further confirm the effectiveness and superiority of the proposed method. In addition, the proposed algorithm is embedded into an LLM-based scheduling agent to support natural language interaction and real-time decision-making, demonstrating its potential for human-machine collaborative scheduling in smart manufacturing.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"52 ","pages":"Article 101152"},"PeriodicalIF":11.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288551","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}