Journal of Industrial Information Integration最新文献

{"title":"Image anomaly detection with a unified transformer model guided by dual-feature","authors":"Yuanbo Wang,&nbsp;Junfeng Jing,&nbsp;Xin Zhang","doi":"10.1016/j.jii.2025.100892","DOIUrl":"10.1016/j.jii.2025.100892","url":null,"abstract":"<div><div>Unsupervised industrial image anomaly detection has been extensively investigated by reconstruction-based frameworks. While demonstrating promising performance benchmarks, existing reconstruction networks frequently degenerate into identity mapping behavior: they are prone to directly copying inputs as outputs, which fails to truly learn the deep structural information and statistical distribution of the data, and often require training distinct models for individual object categories (one-class-one-model). In this paper, we propose a powerful multi-class unified model based on the Dual-Feature Guided Reconstruction Network (DFGR) for multi-class anomaly detection. One of DFGR strengthens the low-level feature to realize the guidance function of the model to reconstruct important normal features and significantly reducing the reliance on prior knowledge, and the other multi-layer fusion feature provides rich semantic features of the image. We utilize the main structural features to guide the reconstruction, realizes the interaction between the global information of the main structural features and the local information of the reconstructed feature map. Our method better balances the contribution of the low-level spatial structure information to the overall reconstruction process, and also effectively reduces the sharp response of the reconstructed network to small background noises. Experimental results on the MVTec dataset demonstrate an image-level area under the receiver operating characteristic (AUROC) of 98.0% and a pixel-level AUROC of 97.1%, and further validations on the DAGM2007, Rollei, and red and blue datasets confirm the feasibility of the dual-feature structure. The code: <span><span>https://github.com/wyanb/DFGR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100892"},"PeriodicalIF":10.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144504029","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 Bi-objective robust optimization and heuristic framework for designing resilient and responsive global supply chains with multimodal transportation","authors":"Bahman Manafi ,&nbsp;Hakan Sayan","doi":"10.1016/j.jii.2025.100895","DOIUrl":"10.1016/j.jii.2025.100895","url":null,"abstract":"<div><div>The design of resilient and responsive supply chain networks has become increasingly critical amid demand uncertainty and operational disruptions, especially in complex sectors such as home appliance manufacturing. This study presents a bi-objective mixed-integer linear programming (BOMILP) model that integrates multimodal transportation planning, strategic facility location, inventory control, and capacity management. The model aims to simultaneously (1) minimize total operational costs, including transportation, inventory, and shortage costs, and (2) maximize customer responsiveness, evaluated through service levels and fulfillment lead times. To address uncertainty, a hybrid approach is developed by combining machine learning-based forecasting and scenario-based robust optimization. Long Short-Term Memory (LSTM) networks forecast demand fluctuations using historical and external data, while the robust model ensures effective resource allocation across multiple demand scenarios. To solve the complex BOMILP model efficiently, a hybrid solution methodology is proposed, integrating the Hybrid Augmented ε-Constraint Method (HA-ε) with a Greedy Randomized Adaptive Search Procedure and Adaptive Variable Neighborhood Search (GRASP-AVNS) heuristic. Resilience is embedded through strategies such as redundant capacities, logistics flexibility, and robust optimization. Resilience performance is assessed using indicators like cost stability, service reliability, and responsiveness under uncertainty. A real-world case study involving a multinational home appliance manufacturer in Turkey demonstrates the model’s effectiveness. Results indicate that the proposed framework reduces total costs by up to 15% and enhances responsiveness by over 20% under disruption scenarios. The hybrid GRASP-AVNS heuristic combined with the augmented ε-constraint method demonstrates strong performance in solving large-scale BOMILP problems under uncertainty. This research provides a scalable, data-driven decision-support tool for manufacturers aiming to balance cost-efficiency, responsiveness, and resilience in uncertain global supply chain environments.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100895"},"PeriodicalIF":10.4,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502506","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":"Graph neural networks to model and optimize the operation of Water Distribution Networks: A review","authors":"Giacomo Vittori ,&nbsp;Yelizaveta Falkouskaya ,&nbsp;Daniel M. Jimenez-Gutierrez ,&nbsp;Tiziana Cattai ,&nbsp;Ioannis Chatzigiannakis","doi":"10.1016/j.jii.2025.100880","DOIUrl":"10.1016/j.jii.2025.100880","url":null,"abstract":"<div><div>Water Distribution Networks (WDNs) have become increasingly complex and interconnected, and the need for advanced modeling and optimization techniques has become fundamental to ensure an efficient and reliable clean water supply. Representing WDNs as graphs naturally models the underlying interacting physical structure and enables the usage of Graph Neural Networks (GNN) that combine the physical structure with abstract notions to capture local and global relationships. GNNs offer significant advantages in contrast to generic Deep Learning (DL) techniques and stand out as a promising solution to model intricate dependencies and enable the investigation of key challenges such as leak detection, water quality monitoring, and demand forecasting. This review presents the physics and hydraulics involved in WDN and the prevalent graph-based models used in the literature. The theoretical foundations of GNNs are shown, highlighting their capabilities in capturing complex spatial relationships and dependencies inherent in the network topology. The most promising GNN-based solutions that can address some of the most critical challenges of WDNs are discussed in detail. We outline the open challenges and potential directions for future developments in this field. By combining multidisciplinary and real-world aspects, this critical review highlights the role of GNNs in modeling and optimizing WDNs.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100880"},"PeriodicalIF":10.4,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482429","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 smart industrial information system using a business process model, discrete events simulation, optimization, and machine learning algorithms","authors":"Seyed Alireza Mansoori Al-yasin ,&nbsp;Mohammad Gheibi ,&nbsp;Hassan Montazeri ,&nbsp;Reza Yeganeh Khaksar ,&nbsp;Mehran Akrami ,&nbsp;Amir M. Fathollahi-Fard ,&nbsp;Kuan Yew Wong","doi":"10.1016/j.jii.2025.100896","DOIUrl":"10.1016/j.jii.2025.100896","url":null,"abstract":"<div><div>In industrial systems, managers face the critical challenge of efficiently managing resources to reduce production costs and time while maximizing profits. To address these challenges, production managers require advanced industrial information systems that optimize production time, costs, and profits. This paper presents a smart industrial information system that integrates Business Process Model and Notation (BPMN), AnyLogic simulation software for Discrete Event (DE) modeling, Response Surface Methodology (RSM), and Machine Learning (ML) algorithms. The system’s effectiveness is demonstrated through its application in an industrial steel skeleton production facility in Iran. To enhance revenue, we optimize key factors of the production process through simulation. Various ML algorithms, including Random Forest (RF), Random Tree (RT), and Bagging, were employed to improve system performance, with the Bagging model yielding the best results. The findings indicate that small hardener chamfer and welder for spare parts, with P-values of 0.0002 and &gt;0.0001 respectively, are the most significant parameters impacting total costs and profits. Ultimately, the proposed industrial information system provides a cost-effective simulation approach that improves process-driven business operations, aligning with BPMN standards and economic criteria.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100896"},"PeriodicalIF":10.4,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335614","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":"An inversion-based group decision-making method for evaluating industrial information platforms","authors":"Chuan Yue","doi":"10.1016/j.jii.2025.100881","DOIUrl":"10.1016/j.jii.2025.100881","url":null,"abstract":"<div><div>Quality evaluation of industrial information platforms represents a typical multi-dimensional decision-making problem that requires comprehensive integration of multi-stakeholder perspectives. This paper proposes a novel group decision-making evaluation framework with two key innovations: (1) The introduction of the inversion number concept from linear algebra to quantify evaluators’ data quality, combined with median statistics to establish a dynamic weight allocation mechanism for decision-makers; (2) Building upon the traditional VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods’ group utility measure, this work innovatively incorporates group regret and group satisfaction matrices, constructing a tripartite “utility-regret-satisfaction” evaluation system through a normalized projection technology, thereby forming an extended VIKOR decision architecture. The proposed method’s feasibility and practicality are validated through a case study on industrial information platform assessment. Experiments demonstrate that: (i) Different data centers can lead to distinct decision outcomes; (ii) Different measures can lead to different decision outcomes; (iii) The inversion-based data quality metric outperforms entropy-based alternatives (with 10% accuracy improvement); (iv) Alternative rankings maintain 70%–100% stability intervals. This research provides a quantifiable, highly robust theoretical tool for multi-attributes decision-making in complex industrial systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100881"},"PeriodicalIF":10.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307047","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":"Personas to inform cognitive interaction with a Digital Twin","authors":"Claire Palmer ,&nbsp;Ella-Mae Hubbard ,&nbsp;Rebecca Grant ,&nbsp;Yee Mey Goh","doi":"10.1016/j.jii.2025.100893","DOIUrl":"10.1016/j.jii.2025.100893","url":null,"abstract":"<div><div>The usability of Digital Twins will be improved by considering what humans need to know when interacting with them. This paper begins by presenting a human-centric definition of a Digital Twin (DT) which includes a consideration of DT user requirements presented as a requirements levels matrix. There are very few studies considering human cognitive interaction with a DT. To identify and model the information requirements of DT users through a human-centric methodology, a people-led approach is presented. This phased approach consists of applying control task analysis (ConTA) to personas, expressing the control tasks derived as decision ladders and modelling the persona’s cognitive interactions within UML information flow diagrams. To demonstrate the approach a Digital Twin of an Industrial Gearbox Product-Service is considered as it is a relatively simple example which is easy to comprehend.</div><div>The persona-based approach is shown to be an effective method for understanding human requirements for a DT. The research introduces novel applications of ConTA to DTs and UML-based information flow modelling and provides examples of decision ladders for the manufacturing domain. The accessibility and applicability of the DT user requirements matrix and the People-led approach to gathering information requirements for a DT is confirmed through validation with representatives from industrial organisations which utilize DT technology.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100893"},"PeriodicalIF":10.4,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279050","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":"Designing a knowledge-enhanced framework to support supply chain information management","authors":"Peng Su,&nbsp;Dejiu Chen","doi":"10.1016/j.jii.2025.100874","DOIUrl":"10.1016/j.jii.2025.100874","url":null,"abstract":"<div><div>With globalization and outsourcing trends, modern industrial companies often rely on an extensive network of suppliers to construct sophisticated products. To maintain effective production planning and scheduling, integrating and managing the extensive information from supply chain has become increasingly critical. In particular, industrial companies, particularly those aiming to achieve Industry 4.0, enable to collect and analyze data related to their supply chains. Due to the vast amount of collected data, there is a continuous challenge in integrating and analyzing dependencies within the supplier network. While the development of Artificial Intelligence (AI) offers a promising solution for extracting and analyzing features from data, the inherently opaque and training-intensive nature of AI-enabled methods still present obstacles to effectively and efficiently analyzing information. To cope with this issue, this paper presents a knowledge-enhanced framework to support supply chain information integration and analysis by combining Knowledge Base (KB) and Graph Neural Networks (GNN). Specifically, constructing a KB enables the integration of extensive collected data with domain knowledge to generate structured and relational information. These knowledge-enhanced data support the training of GNN to encode information about supply chains. The resulting embeddings enable multiple inference tasks for analyzing graph-based data, supporting supply chain management. The case studies cover the usage of encoded embeddings for node classification, link prediction, and scenario classification. The proposed GNN outperforms baseline methods, demonstrating a promising solution for analyzing graph-based data in the context of supply chain management.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100874"},"PeriodicalIF":10.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240140","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":"Adaptive supplier selection framework for sustainable and resilient additive manufacturing supply chains","authors":"Shubhendu Singh ,&nbsp;Subhas Chandra Misra ,&nbsp;Gaurvendra Singh","doi":"10.1016/j.jii.2025.100882","DOIUrl":"10.1016/j.jii.2025.100882","url":null,"abstract":"<div><div>Supplier selection has become a key strategic decision in the area of supply chain management, especially after the advent of the COVID-19 pandemic. Supply chain managers worldwide are being tested for their ingenuity, resilience, and adaptability as they seek to keep their organization’s essential activities operating smoothly in the face of the massive disruption that COVID-19 has brought to supply networks around the globe. This research, thus, proposes a supplier selection framework encompassing resilience and sustainability-enhancing attributes for an additive manufacturing incorporated supply chain. However, since supplier selection problems are influenced by cognitive and stochastic uncertainties, which cannot be dealt with traditional approaches, therefore, Grey relational theory (GRA) has been employed in this research work. Using a real-world case study of a maintenance, repair, and overhaul (MRO) supply chain with five different suppliers, grey possibility values are computed based on which all the prospective suppliers are prioritized. To validate the applicability of the proposed framework, check the efficacy of the GRA technique and comprehend the extent of our performance, the study’s findings have also been compared to the analytic hierarchy process (AHP) method. By enabling informed, traceable, and data-driven supplier decisions under uncertainty, the study contributes to the industrial information integration literature. It demonstrates how intelligent decision-support systems can aid in managing digital manufacturing ecosystems, thereby supporting industrial digitization, integration, and supply chain agility in increasingly volatile environments.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100882"},"PeriodicalIF":10.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144270673","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":"Improved LVRT control in grid tied hybrid renewable energy system with optimized high gain converter","authors":"G. Ezhilarasi ,&nbsp;R. Senthil Kumar","doi":"10.1016/j.jii.2025.100878","DOIUrl":"10.1016/j.jii.2025.100878","url":null,"abstract":"<div><div>This research introduces advanced Low Voltage Ride through (LVRT) control strategies designed to enhance the resilience and stability of grid-connected wind and solar power generation systems. This LVRT control method is intended to efficiently infuse reactive power into grid according to grid code rules, using a Voltage Source Inverter (VSI). The proposed approach employs a LVRT method that determines the injection quantities of reactive and active currents. The strategy's adaptability is contingent upon the dropping ratio grid voltage, ensuring a dynamic response to varying grid conditions. For solar power production systems, the Photovoltaic (PV) voltage is augmented through the implementation of a Modified High Gain Boost Converter. This converter is equipped with a Chaotic Pigeon Optimized Proportional-Integral (CPO-PI) controller, providing an innovative solution for enhancing voltage levels in PV systems. The chaotic pigeon optimization aids in fine-tuning the PI controller, optimizing its performance for varying operational conditions. In the case of wind power generation systems, the control of Doubly-Fed Induction Generator (DFIG) based Wind Energy Conversion Systems (WECS) is achieved through a conventional PI controller. The entire work is simulated using Matlab Simulink platform and the attained outcomes prove that the developed work has highest conversion efficiency of 97.1 %. The integration of these strategies ensures an improved response to grid disturbances and voltage fluctuations, ultimately enhancing the overall performance and reliability of grid-connected renewable energy systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100878"},"PeriodicalIF":10.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254720","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":"Artificial intelligence use in collaborative network processes","authors":"B. Andres ,&nbsp;P. Urze ,&nbsp;E. Araujo ,&nbsp;L.M. Camarinha-Matos","doi":"10.1016/j.jii.2025.100883","DOIUrl":"10.1016/j.jii.2025.100883","url":null,"abstract":"<div><div>This paper reviews the literature to analyse the use of artificial intelligence (AI) in collaborative processes among supply chain (SC) partners, thereby forming a collaborative network (CN). Given the growth of AI and its limited exploration in many business strategies, especially when collaboration among SC partners’ is established, this paper focuses on defining the lines of research and application of AI in CN processes, by presenting insights into how AI can improve the resilience and the antifragility. It examines the integration of AI in CN processes from the following perspectives: (i) the collaborative processes addressed among the CN partners, (ii) the decision-making level of the collaborative processes performed, (iii) the SC partners involved in the collaboration; (iv) the technologies combined with AI to support CN processes; (v) the programming languages implemented to develop AI algorithms; (vi) the SC sectors in which AI is mainly implemented to perform collaborative processes; and (vii) the potential of implementing AI in CN processes, in an increasingly turbulent and disruptive business world. The study focuses on SC in various sectors, including food, transport, logistics, manufacturing, healthcare or electronics, among others. In addition, the review provides a comprehensive understanding of the interplay between collaborative processes and AI-driven advances, identifying the technologies that can merge with AI to support CN processes. The results have enabled the development of a conceptual framework for AI use collaborative processes and outline the benefits, risks and challenges associated with the use of AI in CN, while proposing future research directions in this area.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100883"},"PeriodicalIF":10.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221196","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}