Journal of Industrial Information Integration最新文献

{"title":"Evaluation of crawler cranes for large-scale construction and infrastructure projects: An intuitionistic fuzzy consensus-based approach","authors":"Ömer Faruk Görçün ,&nbsp;Abhijit Saha ,&nbsp;Fatih Ecer","doi":"10.1016/j.jii.2025.100784","DOIUrl":"10.1016/j.jii.2025.100784","url":null,"abstract":"<div><div>Choosing the proper and best crawler crane is a complicated decision-making issue due to several conflicting criteria and vagueness in the construction and project logistics industries. This decision-making problem has become compounded due to insufficient studies on crawler crane selection in the relevant literature. The current study introduces an intuitionistic fuzzy consensus-based complex proportional assessment model (IF-c-COPRAS) developed to address the existing research gaps and identify the best and most suitable crawler crane. The acquired conclusions revealed that the most potent criterion influencing the crawler crane selection is \"job potential,\" with a weighted score of 0.7665, followed by \"periodic control and inspection\" and \"crane model year.\" Once the following findings of the paper regarding crawler crane variants are evaluated, the crawler crane manufactured by Liebherr Co. is the most feasible alternative, with a relative significance score of 0.8324. These outcomes provide sensible implications and insights for practitioners and decision-makers in the construction and project logistics (overweight/oversized cargo lifting and transport firms) industries, providing an applicable guideline for improving the quality of construction operations. Additionally, crane manufacturers can consider these managerial and policy implications and insights to improve the abilities and quality of the crawler cranes they produce.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"44 ","pages":"Article 100784"},"PeriodicalIF":10.4,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419958","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 information integration framework toward cross-organizational management of integrated energy systems","authors":"F. Babaei,&nbsp;R. Bozorgmehry Boozarjomehry,&nbsp;Z. Kheirkhah Ravandi,&nbsp;M.R. Pishvaie","doi":"10.1016/j.jii.2025.100791","DOIUrl":"10.1016/j.jii.2025.100791","url":null,"abstract":"<div><div>Integrating information systems in supply chains and energy systems presents significant challenges due to diverse knowledge domains and cross-organizational processes. This study bridges the gap by employing industrial information integration engineering concepts. We propose a domain ontology framework to integrate supply chain conceptions, upon which several application-level semantic models in energy networks are developed. These ontologies, functioning as interoperable systems, enhance information sharing and data integration across strategic, tactical, and operational decision-making levels. Our proposed framework adheres to Industry 4.0 principles, offering a novel formalization of essential supply chain concepts and activities, ensuring logical consistency. This dual-level ontological approach surpasses previous models by enabling vertical and horizontal integration across supply chain hierarchies. It facilitates seamless communication between supply chain constituents, expert modelers, and software agents. Additionally, the application-level ontologies for energy networks capture various organizational operations, multi-energy vectors, demands, and conversion technologies. These semantic models reduce the knowledge management gap in integrated energy systems, aligning with Industry 4.0 objectives. Two scenarios demonstrate the framework's capabilities: virtual agents coordinate the water-energy nexus and configure integrated energy systems. Results indicate that the domain and application knowledge integration systems comprehensively cover corresponding business processes across operational hierarchies. Thus, the proposed framework supports intra- and inter-agent communications, with ontologies serving as knowledge repositories, ultimately facilitating better industrial integration.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"44 ","pages":"Article 100791"},"PeriodicalIF":10.4,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395359","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 teacher-student framework leveraging large vision model for data pre-annotation and YOLO for tunnel lining multiple defects instance segmentation","authors":"Hanlong Yang ,&nbsp;Lujie Wang ,&nbsp;Yue Pan ,&nbsp;Jin-Jian Chen","doi":"10.1016/j.jii.2025.100790","DOIUrl":"10.1016/j.jii.2025.100790","url":null,"abstract":"<div><div>To achieve an accurate and efficient instance segmentation task for multiple defects within tunnel linings, this paper proposes a simple yet powerful Teacher-Student Framework (TeSF) leveraging the emerging Large Vision Model (LVM) and the advanced You Only Look Once v5 (YOLO v5) model. TeSF integrates a pre-trained LVM within the Teacher Module to alleviate data annotation efforts. Concurrently, the Student Module introduces a novel top-down model architecture, amalgamating YOLO v5 for top-level Classification &amp; Localization and a Segment Head for down-level Segmentation, resulting in YOLO-SH. The Teacher Module acts as a data engine for automatic learning in the Student Module through a well-designed loss function. The proposed TeSF is tested in images collected from Shanghai metro tunnels to automatically recognize five different types of tunnel surface defects. Experiment results indicate that: (1) The LVM-based data annotation procedure in the Teacher Module surpasses the efficacy of the traditional manual method. (2) Optimal equilibrium between computational efficiency and segmentation accuracy is achieved with a medium-sized backbone for YOLO v5, yielding mask [email protected] values of 0.644 and 0.694, all within an inference time of 6.2ms/image. (3) The top-down Student Module with YOLO-SH v5m exhibits superior performance in instance segmentation compared to state-of-the-art models, bringing improvements of no less than 8.2% and 6.3% in box [email protected] and mask [email protected], respectively. In short, the novelty of TeSF lies in the utilization of the pre-trained LVM for streamlined data annotation coupled with the augmentation of YOLO-SH for a more cost-effective and precise detection of multiple defects within tunnels. The applicability of TeSF can extend to the analysis of 3D scanner images derived from in-service tunnel environments.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"44 ","pages":"Article 100790"},"PeriodicalIF":10.4,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379051","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":"Autonomous cycle of data analysis tasks for the determination of the coffee productive process for MSMEs","authors":"Jairo Fuentes ,&nbsp;Jose Aguilar ,&nbsp;Edwin Montoya","doi":"10.1016/j.jii.2025.100788","DOIUrl":"10.1016/j.jii.2025.100788","url":null,"abstract":"<div><div>Coffee production needs certain levels of efficiency to ensure that the quality of the bean, the roasting process, and in general, the coffee processing methods, achieve financial and environmental sustainability objectives. This requires tasks of monitoring and analyzing of features of the coffee bean, and the roasting process, among other aspects, so that stakeholders of the agro-industrial sector of MSMEs can know what happens in the coffee production and can make better decisions to improve it. In a previous article, three autonomous cycles of data analysis tasks are proposed for the automation of the production chains of the MSMEs. This work aims to instantiate the autonomous cycle responsible for identifying the type of input to transform in the production process, in the case of coffee production. This cycle analyzes the inputs of the production chain (quantity, quality, seasonality, durability, cost, etc.), based on information from the organization and the context, to establish the production process to be carried out. This autonomous cycle is instanced in the coffee production to identify the type of input to transform (bean quality), and to determine the transformation process (level of decrease of the bean during the roasting process and coffee processing method). The quality model is defined by the K-means technique with a performance in the Silhouette Index of 0.85, the predictive model of the level of decrease of beans in the roasting process is defined by Random Forest with a performance in the accuracy of 0.81, and finally, the identification model of the \"production method\" is carried out by the Logistic Regression technique with a quality performance in the accuracy of 0.72. Among the most important findings is that the autonomous cycle of data analysis tasks based on machine learning techniques is capable of studying the contextual data of coffee production to identify the type of input to be transformed and the coffee transformation process. Another important finding is that the autonomous cycle allows the automation of the production process, leading to improved times and coffee processing.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"44 ","pages":"Article 100788"},"PeriodicalIF":10.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349746","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":"Integrating digital transformation with human-centric factors strategies to enhance organisational process performance: The H.O.P.E. model","authors":"Camilla Buttura Chrusciak,&nbsp;Anderson Luis Szejka,&nbsp;Osiris Canciglieri Junior","doi":"10.1016/j.jii.2025.100785","DOIUrl":"10.1016/j.jii.2025.100785","url":null,"abstract":"<div><div>Digital transformation (DX) has driven significant company changes, restructuring products, processes, and services by integrating emerging technologies across all organisational levels. This change enhances workflows, decision-making, and operational efficiency, fostering innovation and competitive advantage. This research analyses how effective technology implementation, employee engagement, usability awareness, and strategic management practices can improve organisational processes. By analysing interconnections among DX, human factors, business processes, and emerging technologies, the research employs a systematic literature review and Structural Equation Modelling (SEM) to identify critical factors for success. The findings highlight that digital tools streamline operations and support data-driven decisions, reducing cognitive overload through user-centred design. This research proposes a Human-Oriented Process Enhancement (H.O.P.E.) model that integrates DX with human-centric factors to guide digital technology applications and improve organisational performance. The practical application of this model was carried out as part of a litigation management project in an automotive supplier manufacturing plant specialising in advanced solutions across seating, interiors, and clean mobility technologies. The project sought to streamline legal processes, enhance compliance, and mitigate risks through structured litigation management. In conclusion, the digital maturity and human factors (DMHF) index, the outcome of the H.O.P.E. model, has proven to be a comprehensive tool for aligning DX adoption with organisational strategic goals considering human-centric factors. Future research will focus on customising the index for industry-specific needs, particularly ergonomics, to ensure organisations achieve sustainable growth in a digital setting.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"44 ","pages":"Article 100785"},"PeriodicalIF":10.4,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387879","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 outranking method with Dombi aggregation operators based on multi-polar fuzzy Z-numbers for selection of best rehabilitation center","authors":"Inayat Ullah ,&nbsp;Muhammad Akram ,&nbsp;Tofigh Allahviranloo","doi":"10.1016/j.jii.2025.100781","DOIUrl":"10.1016/j.jii.2025.100781","url":null,"abstract":"<div><div>Useful decisions are made based on reliable information. The concept of <span><math><mi>Z</mi></math></span>-number involves the issue of reliability of information. Multipolar information is particularly important in scenarios involving multiple attributes in a decision making process. There does not exist a study in the literature that conveys multipolar information with reliability. In this research article, the concept of multipolar fuzzy <span><math><mi>Z</mi></math></span>-Dombi aggregation operators is first introduced. An outranking method based on the proposed multipolar fuzzy <span><math><mi>Z</mi></math></span>-Dombi aggregation operators is then developed. The proposed method is applied to a case study related to the selection of the best rehabilitation centre for the treatment of teenage drug users. The proposed method is compared with four existing techniques in multipolar fuzzy and fuzzy environments to validate the approach. A sensitivity analysis is performed to test the credibility of the study. Further, the Spearman coefficient is calculated for ranking lists obtained by different methods to verify the method’s consistency. The study’s findings are presented in graphical illustrations for a clear understanding of the results. The method shows validity through consistent comparison with four established techniques. This alignment supports its robustness and relevance in practical applications. Moreover, a positive Spearman correlation coefficient confirms its reliability by aligning rankings with expected outcomes.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"44 ","pages":"Article 100781"},"PeriodicalIF":10.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160131","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 analysis of e-trade platforms in automotive spare part sector: A T-Spherical fuzzy perspective","authors":"Ömer Faruk Görçün ,&nbsp;Prasenjit Chatterjee ,&nbsp;Ahmet Aytekin ,&nbsp;Selçuk Korucuk ,&nbsp;Dragan Pamucar","doi":"10.1016/j.jii.2025.100782","DOIUrl":"10.1016/j.jii.2025.100782","url":null,"abstract":"<div><div>E-trade platforms are software applications that enable businesses to conduct online sales and manage their digital storefronts. These platforms provide a range of tools and features to facilitate the creation, operation, and management of an online business. This study comprehensively evaluates e-trade platforms within the automotive spare parts industry, examining various critical aspects to identify the optimal platform. The evaluation includes an in-depth analysis of the current state of the platforms, exploration of potential strategies and approaches for improvement, and identification and analysis of challenges and barriers. To address these issues, the study employs problem-solving within the framework of expert evaluations based on criteria defined by an extensive literature review. T-Spherical fuzzy (T-SF) subjective weighting approach and T-SF-weighted aggregated sum product assessment (WASPAS) method are used for this purpose. The analysis reveals that “security” is the most crucial criterion, with Amazon emerging as the most prominent e-trade platform. The findings indicate that prioritizing security, discounts, and delivery time will enable e-commerce platforms to gain a competitive edge. The study evaluates international e-commerce platforms, identifying weaknesses in critical business areas key competitive advantage factors, and offering forward-thinking recommendations. This research has significant implications for the rapid and effective development of logistical partnerships with e-trade platforms across various industries. Additionally, it serves as a foundational basis and template for future research in the e-commerce sector, particularly within the automotive spare parts industry.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"44 ","pages":"Article 100782"},"PeriodicalIF":10.4,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387878","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-contrast self-supervised learning framework for enhancing multi-label classification in Industrial Internet of Things","authors":"Xin Hu ,&nbsp;Yifan Chen ,&nbsp;Jichao Leng ,&nbsp;Yuhua Yao ,&nbsp;Xiaoming Hu ,&nbsp;Zhuo Zou","doi":"10.1016/j.jii.2025.100777","DOIUrl":"10.1016/j.jii.2025.100777","url":null,"abstract":"<div><div>In the Industrial Internet of Things (IIoT), multi-label classification is challenging due to limited labeled data, class imbalance, and the necessity to consider temporal and spatial dependencies. We propose BiConED, a bi-contrast encoder–decoder self-supervised model integrating two contrasting methods: RAC employs an encoder–decoder with augmented data to capture temporal dependencies and boost information entropy, enhancing generalization under label scarcity. QuadC captures spatial dependencies across channels through convolutions on hidden vectors. Evaluated on the real-world industrial benchmark SKAB, BiConED improves feature extraction for underrepresented classes, achieving a 26% increase in F1 score, a 67.72% reduction in False Alarm Rate (FAR), and a 57.25% decrease in Missed Alarm Rate (MAR) compared to models without the proposed contrasts. Even with limited labeled data, BiConED maintains a FAR below 1% and recovers up to 85% of the F1 score without resampling, demonstrating its robustness in imbalanced IIoT environments.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"44 ","pages":"Article 100777"},"PeriodicalIF":10.4,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049837","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":"Backpropagation neural network model with statistical inference in manufacturing processes","authors":"Homero de León-Delgado ,&nbsp;Rolando J. Praga-Alejo ,&nbsp;David S. González-González","doi":"10.1016/j.jii.2025.100783","DOIUrl":"10.1016/j.jii.2025.100783","url":null,"abstract":"<div><div>Nowadays, there is a growing need for tools to model the complex characteristics of manufacturing processes to support decision-making and optimize production and quality. This study proposes using a Backpropagation neural network (BPNN) to model manufacturing processes, leveraging its ability to capture complex and nonlinear relationships. Additionally, integrating statistical inference techniques from Generalized Linear Models (GLM) with the neural network is suggested. This integration combines the predictive capabilities of the BPNN with the statistical tools of GLMs, enhancing result interpretability and analysis accuracy. The proposed approach was applied to two manufacturing processes. In the die-casting process, the BPNN with a logit function showed a lower deviance (0.0399) compared to the probit model (0.0875) and a greater deviance difference (6.1280) with a <em>p</em>-value of 0.0201. Confidence intervals confirmed the significance of these results. Metal temperature and solidification time were significant predictors, with weights of -1.0375 and -0.9880, respectively. In the machining process, the BPNN model with the probit function had a lower deviance (0.0140) compared to the logit model (0.0175) and a slight precision advantage with a deviance difference of 3.9325 and a p-value of 0.0473. Parameters S1 and S2 had significant effects with weights of 72.671 and -54.397, respectively. This approach allows for selecting optimal activation functions for each process, improving efficiency and quality control in manufacturing.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"44 ","pages":"Article 100783"},"PeriodicalIF":10.4,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049838","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":"Realization of the physical to virtual connection for digital twin of construction crane","authors":"Enliu Yuan ,&nbsp;Jian Yang ,&nbsp;Mohamed Saafi ,&nbsp;Fei Wang ,&nbsp;Jianqiao YE","doi":"10.1016/j.jii.2025.100779","DOIUrl":"10.1016/j.jii.2025.100779","url":null,"abstract":"<div><div>A digital twin is an integrated multi-physics representation of a complex physical entity. This article develops the physical-to-virtual connection of the digital twin and proposes a framework for the construction of a tower crane digital twin. The main contributions of this paper include development of tower crane monitoring dataset, tower crane detection and tower crane operation mode recognition. By annotating &gt;20,000 tower crane images in 583 tower crane videos, a tower crane image recognition dataset and a tower crane operating mode dataset are established. Yolov5x algorithm is used in the tower crane detection, and the test set detection accuracy is 93.85 %. After comparing the LSTM and CNN algorithms, 3DResNet algorithm is selected for tower crane operational mode recognition. The dataset is augmented by rotating the image and the final recognition accuracy reaches 87 %. These models can be installed on CCTV to monitor operational status of tower crane in real time and transfer relevant information to the virtual model. The tower crane in the virtual space completes the action of the physical tower crane, thereby realizing the physical-to-virtual mapping in the digital twin.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"44 ","pages":"Article 100779"},"PeriodicalIF":10.4,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990512","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}