Journal of Industrial Information Integration最新文献

{"title":"Game-theoretic approach to consensus building in multi-objective optimization within multi-scale information systems","authors":"Yibin Xiao ,&nbsp;Jianming Zhan ,&nbsp;Zeshui Xu ,&nbsp;Rosa M. Rodríguez","doi":"10.1016/j.jii.2025.100914","DOIUrl":"10.1016/j.jii.2025.100914","url":null,"abstract":"<div><div>In modern industrial management, integrating heterogeneous data and achieving consensus among decision-makers (DMs) are crucial for optimizing complex systems. Multi-scale information systems (MSISs) have emerged as a powerful tool for managing and fusing diverse data sources. However, reaching consensus in multi-scale environments remains challenging due to data complexity and varying DM preferences. This paper proposes a consensus-reaching process (CRP) method based on MSISs, called MSIS-CRP. Specifically, the sequential clustering-based approach for constructing MSISs stands as the bedrock of this innovative framework. Through the meticulous development of a clustering-driven construction strategy, it excels at precisely discerning the surjective connections among various scales. This not only enables comprehensive data integration at a profound level but also paves the way for robust decision-making analysis, laying a reliable groundwork for subsequent steps in the process. Subsequently, scale weights and DMs’ weights are meticulously calculated according to the characteristics of decision information, thereby effectively reflecting the divergent importance levels of different information dimensions. A calculus-based consensus measure is introduced to quantitatively evaluate DMs’ opinions. To facilitate CRP, global and local consensus feedback mechanisms are established using a multi-objective programming model that balances consensus improvement and adjustment costs. The model is solved from a game-theoretic perspective, leveraging equilibrium concepts to enhance robustness. Comparative and experimental analyses demonstrate that MSIS-CRP effectively improves consensus levels while maintaining computational efficiency, outperforming existing approaches by providing more integrated and comprehensive decision results, especially in dynamic environments. Notably, in numerical experiments involving 48 alternatives and 5 DMs, the MSIS-CRP method achieves a group consensus level of 0.9662 after global feedback, followed by local feedback to reach the final consensus. It demonstrates an adjustment distance of 50.8850 and a running time of 3.7031 s, significantly outperforming seven comparative methods in both efficiency and consensus quality. Overall, this research offers a novel solution for complex decision-making challenges in industrial management by integrating MSISs with CRP.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100914"},"PeriodicalIF":10.4,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757960","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":"Machine Learning-integrated digital twins for process optimization in Industry 5.0","authors":"Carlos Jefferson de Melo Santos,&nbsp;Ava Santana Barbosa,&nbsp;Angelo Marcio Oliveira Sant’Anna","doi":"10.1016/j.jii.2025.100920","DOIUrl":"10.1016/j.jii.2025.100920","url":null,"abstract":"<div><div>This study responds to the challenges of Industry 5.0 by proposing a hybrid model that integrates Ordinary Differential Equations (ODEs) with Machine Learning (ML) algorithms within a Digital Twin (DT) architecture. The proposal is applied to a detergent manufacturing plant, updating processes focusing on sustainability, resilience, and human-centeredness. The study was conducted in a real industrial plant, with operational data collected from SCADA, MES, and ERP systems. This paper proposes a hybrid model that integrates Machine Learning (ML), Ordinary Differential Equations (ODEs), and a Digital Twin framework for process optimization in the manufacturing industry. The variables were treated in modular architecture and tested within the ISO 23247 framework, with real-time visualizations through human-machine interfaces (HMI). The hybrid approach showed significant gains in predicting chemical solutions (R² = 0.80), sulfonic acid consumption (R² = 0.9998), and intelligent reactor allocation (80.7 % accuracy). In addition, the system predicted laboratory delays with 78.1 % accuracy and enabled significant reductions in loading times and operational deviations. In contrast, raw materials such as caustic soda, water, and laurel showed lower predictive performance, reinforcing the need for additional explanatory variables. The model enhances the potential of predictive AI combined with physical modeling for more sustainable, resilient, and human-centered decisions. Integrating ML and ODEs into a DT promotes operational and strategic gains for the detergent industry, aligning with the principles of Industry 5.0. The demonstrated approach is effective, scalable, and capable of transforming industrial data into optimized decisions, directly impacting the production process's efficiency, sustainability, and autonomy.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100920"},"PeriodicalIF":10.4,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757961","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":"Design an enhanced granulation-degranulation mechanism using group-exchange particle swarm optimization","authors":"Peng Nie,&nbsp;Yuan Gan,&nbsp;Qiang Yu","doi":"10.1016/j.jii.2025.100919","DOIUrl":"10.1016/j.jii.2025.100919","url":null,"abstract":"<div><div>The information granulation-degranulation mechanism is a fundamental content in granular computing theory. For the traditional fuzzy granulation-degranulation mechanism, it is a randomness in the initialization selection of prototypes, which can affect the results by abnormal data and the obtained results of prototypes are not optimal. Besides, the process of granulation-degranulation is accompanied by the generation of reconstruction data errors. In this study we propose a group-exchange particle swarm optimization (GPSO) to enhance the granulation-degranulation mechanism and improve the search strategy for data prototypes. The prototype plays a crucial role in generating reconstruction errors during the granulation-degranulation process. The GPSO can continuously drive the exchange of particle information between different groups to obtain the prototypes and membership matrix that optimize the performance indicators of the granulation-degranulation model. It can minimize the reconstruction errors and obtain optimal solutions from the best set of data prototypes in the solution space, accelerating the process of searching for the best data prototypes, reducing reconstruction errors of the granulation-degranulation mechanism. The experimental results indicate that the performance of our proposed GPSO granulation-degranulation model is improved by 6.81% -45.77%, 2.64% -30.00%, and 10.93% -50.10% compared to the granulation-degranulation models constructed based on FCM algorithm, PSO algorithm, and Boolean algorithm on the testing dataset of different datasets, respectively.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100919"},"PeriodicalIF":10.4,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738953","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":"Industrial digital twins based on enterprise modeling: architecture, methodology, and engineering applications","authors":"Mengjin Qu,&nbsp;Yining Yao,&nbsp;Minhui Sun,&nbsp;Xinran Chang,&nbsp;Qing Li","doi":"10.1016/j.jii.2025.100912","DOIUrl":"10.1016/j.jii.2025.100912","url":null,"abstract":"<div><div>As AI technology increasingly permeates industrial applications, scenario management within the context of industrial intelligence presents a complex, multidisciplinary, and spatiotemporally coupled challenge. The integration characteristics of cyber-physical-social systems make Digital Twins (DT) a promising solution. However, constructing an effective DT model for such scenarios necessitates the incorporation of detailed industrial knowledge related to the corresponding data. Formal modeling serves as a unified cognitive approach and a robust foundation for system development. Hence, this paper focuses on the scenario management challenges within the industrial intelligence context and introduces the concept of using formal modeling languages as the foundation for DTs. We propose a model management architecture and a modeling methodology tailored for scenario-specific digital twins and provide a corresponding metamodel for the modeling approach. To validate the efficacy of our architecture and models, we analyze a multi-enterprise network collaboration scenario in remote maintenance of engineering machinery. This exploration not only demonstrates the validity of the proposed models and architecture but also offers a conceptual DT model for enhancing remote machinery maintenance.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100912"},"PeriodicalIF":10.4,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738954","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":"Green-resilient supplier selection via a new integrated rough multi-criteria framework","authors":"Alptekin Ulutaş ,&nbsp;Ayşe Topal ,&nbsp;Fatih Ecer","doi":"10.1016/j.jii.2025.100913","DOIUrl":"10.1016/j.jii.2025.100913","url":null,"abstract":"<div><div>Initially, companies focused solely on the economic aspects of business processes; now, they have begun to prioritize environmental and social issues to mitigate adverse impacts on the ecology and community. Furthermore, resilience is crucial in ensuring the supply chain remains uninterrupted. Therefore, evaluating the suppliers' sustainability and resilience performance is paramount. A unique mathematical tool is required to integrate resilience and sustainability considerations into supplier selection decisions. Hence, the research fulfills this necessity by introducing a novel, multi-criteria rough methodology. The studies in the literature primarily assess suppliers from either a green or resilient perspective, employing fuzzy MCDM methods to address uncertainty. However, they struggle to cope with uncertainty when faced with limited information. To address this gap, this study proposes a novel approach based on rough set theory to handle interpersonal ambiguity and vagueness flexibly without requiring additional information. It determines the weights of criteria used for green-resilient supplier selection and evaluates the green-resilient performance of suppliers. To this end, rough logarithmic percentage change-driven objective weighting and rough maximum of criterion frameworks are developed to determine criteria weights, whereas the rough mixed aggregation by the comprehensive normalization technique model is designed to decide alternative rankings. This approach requires less prior information than fuzzy set-based methodologies and offers additional flexibility in handling imprecision. To demonstrate its practicality, a real case study from a garment-textile factory in Turkey is presented. The work is the first study of this issue to employ the introduced methodology. Findings highlight that the impact on the local community is the foremost driver for green-resilient supplier selection, followed by cost and supplier sustainability. The model's reliability is validated by comparative and sensitivity analysis. The research contributes to the field by providing a reliable tool that combines rough sets with resilience and sustainability approaches, thus improving the effectiveness and credibility of supplier selection activities in engineering. The work provides executives with an effective supplier evaluation process that jointly addresses sustainability and resilience assessments under uncertainty.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100913"},"PeriodicalIF":10.4,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721862","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":"Explainable AI for industrial fault diagnosis: A systematic review","authors":"J. Cação ,&nbsp;J. Santos ,&nbsp;M. Antunes","doi":"10.1016/j.jii.2025.100905","DOIUrl":"10.1016/j.jii.2025.100905","url":null,"abstract":"<div><div>The integration of Artificial Intelligence (AI) and Machine Learning (ML) into industrial environments, particularly for optimising fault detection and diagnosis, has accelerated with Industry 4.0 and 5.0. However, the “black-box” nature of these methods hinders practical implementation, as trust, interpretability, and explainability are crucial for informed decision-making. Furthermore, impending regulatory frameworks like the EU AI Act make directly implementing opaque AI for critical industrial tasks infeasible. Explainable AI (XAI) offers a promising solution by enhancing ML model interpretability and auditability through human-understandable explanations. This review comprehensively analyses recent XAI advancements for industrial fault detection and diagnosis, presenting a novel taxonomy for XAI methods and discussing how XAI outputs are generated, conveyed to end-users, and evaluated. It then systematically reviews real-world industrial XAI implementations, highlighting their applications, methods, and output presentation approaches. Key identified trends include the dominance of post-hoc feature attribution methods, widespread use of SHAP and GradCAM, and a strong reliance on graphical explanation tools. Finally, it identifies current challenges and outlines future research directions to promote the development of interpretable, trustworthy, and auditable AI systems in industrial settings.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100905"},"PeriodicalIF":10.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664947","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":"Switching fuzzy transfer learning for multimodal reservoir fuzzy echo state networks","authors":"Jiawei Lin ,&nbsp;Fu-lai Chung ,&nbsp;Shitong Wang","doi":"10.1016/j.jii.2025.100911","DOIUrl":"10.1016/j.jii.2025.100911","url":null,"abstract":"<div><div>In this study, an interesting yet previously uncared concept—switching fuzzy transfer learning for multimodal reservoir echo state networks—is proposed from three overwhelming phenomena under multimodal reservoir transfer learning environments. Due to the use of reservoir transition, the first phenomenon that the discrimination between different classes along each modal may perhaps be weakened actually encourages the use of fuzzy classification. And then the second one is that source and target domains may occasionally have more overlapping caused by the randomness of reservoir computing, which actually encourages the use of fuzzy similarity for these two domains. The third one that the similarity between source and target domains along each modal and even across different modals may perhaps be distorted inspires the switching of transfer learning across modals. This study explores a novel switching fuzzy transfer learning (SFTL) framework for multimodal reservoir echo state networks. SFTL begins with the calculation of the theoretically derived fuzzy reservoir Stein discrepancies (fuzzy RSDs) between target domain and source domains in the same and even different modals. After that, SFTL trains each modal’s fuzzy transfer learning classifier by taking the proposed adaptive multimodal source switching strategy for an appropriate source domain selection. Finally, SFTL achieves promising multimodal learning through moving from linear aggregation level of each fuzzy transfer learning classifier to the mixture level of both this linear aggregation and the switching ensemble of multimodal source domains. The comprehensive experiments on 31 adopted datasets demonstrate the superiority of SFTL, achieving an average classification accuracy of 85.00 % in the focused multimodal reservoir transfer learning scenario.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100911"},"PeriodicalIF":10.4,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664925","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 broker-embedded data distribution service for resource-constrained IoT environments","authors":"Hwimin Kim,&nbsp;Dae-Kyoo Kim","doi":"10.1016/j.jii.2025.100909","DOIUrl":"10.1016/j.jii.2025.100909","url":null,"abstract":"<div><div>The Data Distribution Service (DDS) is a middleware framework that facilitates brokerless, data-centric publish–subscribe communications across diverse domain networks. Its decentralized nature and a wide range of Quality of Service (QoS) policies enable DDS to be both scalable and reliable. However, DDS requires substantial resources, making it challenging to use with small devices, such as those commonly found in Internet of Things (IoT) environments – networks of interconnected physical devices embedded with sensors, software, and connectivity that collect and exchange data over the Internet, often under resource constraints. To address this, we present <span><math><mi>b</mi></math></span>-DDS (broker-embedded DDS), a novel approach that extends DDS by integrating broker functionalities. This enhances DDS’s compatibility with lightweight devices and its adoptability in resource-limited networks, while retaining the advantages of DDS, including scalability and reliability. The model was evaluated using a pedestrian safety system in the Vehicle-to-Everything (V2X) domain, and the results demonstrate that the model improves network traffic by 71.83% compared to standard DDS, provides resilience to the single-point failure problem in broker-based protocols, and exhibits the efficiency to satisfy stringent performance benchmarks for real-time systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100909"},"PeriodicalIF":10.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144652992","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":"Blockchain-based cloud controllers for reliable networked control systems","authors":"Zhongcheng Lei ,&nbsp;Luis de la Torre ,&nbsp;Francisco-José Mañas-Álvarez ,&nbsp;Wenshan Hu","doi":"10.1016/j.jii.2025.100902","DOIUrl":"10.1016/j.jii.2025.100902","url":null,"abstract":"<div><div>Networked control systems are critical in industrial applications but remain vulnerable to controller failures, which can destabilize operations. Blockchain technology offers a decentralized solution to enhance reliability. While blockchain technologies have been mainly used in financial systems (such as cryptocurrencies) so far, they are now being used in an increasing number of applications (such as logistics, power grids, or the Internet of Things) due to their powerful features and advantages. In this article, the use of blockchains is proposed and explored to deploy decentralized and reliable controllers. A blockchain-based controller architecture is presented to provide controllers that are permanently available, open accessible, and open source. Time to transaction finality and cost for transactions are analyzed in different blockchain networks, thus identifying their suitability. Our analysis reveals that blockchain networks can potentially be applied in slow processes with big enough time constants. Moreover, we propose the integration of event-based control to reduce transaction costs, thereby enhancing the viability of blockchain technologies in networked control systems. To demonstrate the practical application and cost efficiency of our approach, we present a case study focusing on a greenhouse climate control system. Results show that feasible blockchain networks – those compatible with sampling period constraints – consistently reduce control costs. For instance, on Fantom blockchain, event-based control achieved a 27.73-fold reduction in average control costs across six system variables over the eight-day operation period.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100902"},"PeriodicalIF":10.4,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623613","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":"Strategic Cost Allocation with Value Stream Costing: Data-Driven Decision Analysis in Textile Manufacturing","authors":"Mehmet Özçalıcı, Hasan Emin Gürler, Ahmet Kaya, Dragan Pamucar, Nazan Güngör Karyağdı, Ayşegül Ciğer","doi":"10.1016/j.jii.2025.100906","DOIUrl":"https://doi.org/10.1016/j.jii.2025.100906","url":null,"abstract":"Cost allocation holds paramount importance for businesses, serving as a fundamental aspect of competitiveness in the contemporary market milieu. This includes maintaining high-quality standards in products or services, alongside operational flexibility to adapt to changing customer demands and market conditions. Moreover, accurate assessment of product costs is crucial for guaranteeing the profitability of the company and maximizing the efficient utilization of operational resources. Businesses using the traditional costing approach may face difficulties when allocating total production expenses to individual products. Hence, this study aims to comprehensively analyze the cost system of a medium-sized textile company by incorporating principles of value stream costing. It employs methodologies namely COBRAC, FUCOM, and BWM to identify key cost drivers. The firm identified 12 value streams for its five products and relied on expert opinion to assess costs for eight of them without drivers. The results indicated that the method chosen for cost calculation notably impacts the gross profit showcased in the income statement. Specifically, the FUCOM approach emerges with the highest gross profit among the evaluated methods, closely trailed by the BWM technique, whereas the employment of the COBRAC method yields a relatively lower gross profit value. The suggested model will empower manufacturing firms to pinpoint product costs and effectively attain a sustainable competitive advantage.","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"38 1","pages":""},"PeriodicalIF":15.7,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144621916","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}