Journal of Industrial Information Integration最新文献

{"title":"A two stage-based approach for swarm UAVs landing in unknown 3D environments","authors":"Linjie Yang ,&nbsp;Hongtao He ,&nbsp;Yang Gao ,&nbsp;Luping Wang ,&nbsp;Shaolong Chen","doi":"10.1016/j.jii.2025.100928","DOIUrl":"10.1016/j.jii.2025.100928","url":null,"abstract":"<div><div>The ability to autonomously land in unknown environments is essential for achieving high intelligence of swarm unmanned aerial vehicles (UAVs). Such a capability requires that UAVs necessitate four functions: autonomous selection of landing sites, mutual coordination among the individuals, real-time trajectory planning, and global optimization decision-making. However, these modules are not sufficiently integrated into existing methods due to the complexity and uncertainty of swarm system. To address these challenges, this paper proposes a completed landing process framework for swarm UAVs using a two-stage approach. Specifically, the candidate landing sites are generated automatically from the complex environment in a coarse-to-fine manner. At the high-altitude flying stage, the swarm UAVs are considered as a whole and directed towards dynamic target points, which are updated by the designed optimization cost model. Additionally, a virtual force model is introduced to maintain the balance between the interior formation of the UAVs and the external obstacles. At the approaching landing stage, a novel landing model based on rapidly exploring random tree (RRT) is proposed to plan the final landing paths, which can effectively avoid collisions with other UAVs and ground obstacles. The proposed method is performed in real-world scenarios and estimated via different fly stages. The results of comprehensive experiments demonstrate that the proposed approach achieves good landing performance in multiple scenarios, including landing site selection and progressive swarm path planning. This also supports industrial information integration by enabling coordinated sensing, communication, and decision-making for swarm UAVs in complex environments.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 100928"},"PeriodicalIF":10.4,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898605","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":"Collaborative task scheduling in IIoT: A comparative study of evolutionary algorithms and deep reinforcement learning","authors":"Zhen Chen ,&nbsp;Xiaohan Wang ,&nbsp;Yuanjun Laili ,&nbsp;Lin Zhang ,&nbsp;Wentong Cai ,&nbsp;Lei Ren ,&nbsp;Zhihao Liu","doi":"10.1016/j.jii.2025.100930","DOIUrl":"10.1016/j.jii.2025.100930","url":null,"abstract":"<div><div>From the perspective of industrial information integration engineering (IIIE), the Industrial Internet-of-Things (IIoT) serves as a unified framework that integrates cloud, edge, and manufacturing resources through cloud–edge–device collaboration, enabling highly flexible and collaborative production processes. Collaborative task scheduling in IIoT refers to assigning manufacturing and computational tasks to heterogeneous resources to minimize the overall task makespan and energy consumption. However, the presence of complex task dependencies and the heterogeneity of resource configurations make the scheduling problem highly challenging. To address this, we conduct a comprehensive evaluation of seven evolutionary algorithms (EAs) and seven deep reinforcement learning (DRL) methods across three representative IIoT scheduling scenarios: manufacturing task scheduling (MTS), computational task scheduling (CTS), and hybrid task scheduling (HTS). To investigate the effect of algorithm design, we propose two types of algorithm formulations: explicit formulation (EF), where the algorithm outputs correspond directly to decision variables, and implicit formulation (IF), where outputs represent heuristic factors guiding task assignment. For each scenario, we construct scheduling instances of three scales and evaluate all 14 methods under both formulations. The results demonstrate that EAs offer more stable performance, while DRLs exhibit stronger generalization and faster inference, especially in large-scale or dynamic scenarios. Moreover, the implicit formulation often leads to better solution quality across both algorithm classes. These findings provide valuable insights for algorithm selection and design in IIoT environments and highlight the importance of formulation strategies in influencing optimization outcomes.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100930"},"PeriodicalIF":10.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895105","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":"Towards spatial-temporal meta-hypergraph learning for multimodal few-shot fault diagnosis","authors":"Jinze Wang ,&nbsp;Jiong Jin ,&nbsp;Lu Zhang ,&nbsp;Hong-Ning Dai ,&nbsp;Adriano Di Pietro ,&nbsp;Tiehua Zhang","doi":"10.1016/j.jii.2025.100924","DOIUrl":"10.1016/j.jii.2025.100924","url":null,"abstract":"<div><div>Fault diagnosis is essential for maintaining equipment safety and reliability in smart industrial environments. Early identification of issues through intelligent maintenance systems helps prevent downtime, enhance productivity, and mitigate hazards. However, two major challenges exist: first, when machines exhibit faults, they are typically deactivated for safety, resulting in scarce fault data; second, existing methods disregard high-order relationships between working conditions, while failing to simultaneously consider signal heterogeneity and spatial–temporal correlations. To address these challenges, we propose a spatial–temporal meta-hypergraph learning for multimodal few-shot fault diagnosis (MetaSTH-FD) by integrating dynamic spatial–temporal hypergraph construction into meta-learning. The framework first decomposes vibration signals into multimodal features, then constructs hypergraphs to capture complex relationships. Our approach enables quick adaptation to new conditions with limited samples, while the hypergraph structure models complex relationships in multimodal signal data. Experimental results demonstrate significant performance improvements across various working conditions and noise levels, thereby providing new insights for intelligent maintenance in smart manufacturing.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"48 ","pages":"Article 100924"},"PeriodicalIF":10.4,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144901448","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-Enabled Adaptive Criticality-Based IoT Firmware Update Distribution Framework for Sustainable Smart Cities","authors":"Collins Sey ,&nbsp;She Kun ,&nbsp;Obed Barnes ,&nbsp;Seth Larweh Kodjiku ,&nbsp;Kwame Omono Asamoah ,&nbsp;Chiagoziem Chima Ukwuoma ,&nbsp;Isaac Adjei-Mensah ,&nbsp;Linda Delali Fiasam ,&nbsp;Esther Stacy E.B. Aggrey ,&nbsp;Emmanuel S.A. Gyarteng","doi":"10.1016/j.jii.2025.100929","DOIUrl":"10.1016/j.jii.2025.100929","url":null,"abstract":"<div><div>The rapid growth of the Internet of Things (IoT) has significantly shaped the Smart City paradigm by enabling efficient data collection and resource management. Secure firmware updates and distribution mechanisms are crucial stages in the lifecycle of IoT device management. Traditional mechanisms, however, are vulnerable to unauthorized access, tampering, and single points of failure, which expose IoT devices to security threats. They also often fail to account for the dynamic nature of IoT environments and the varying criticality of devices for updates distribution, leading to inefficiencies and potential vulnerabilities. This work, proposes a blockchain-enabled firmware update framework that addresses these limitations by employing a Merkle tree-based chunking approach for firmware data integrity assurance, and the blockchain decentralization for a secure, tamper-proof update mechanism. It incorporates smart contracts to enable automatic validation and authorization of firmware updates, mitigating the risks of malicious attacks and unauthorized access. Additionally, it utilizes peer-to-peer storage for firmware update distribution, eliminating reliance on centralized servers and resolving the issue of author disappearance. It introduces a machine learning (ML)-based method, the Adaptive Criticality-Based Distribution (ACBD), which dynamically adjusts firmware update distribution priorities based on device criticality, defined by application domain, operational impact, and prevailing external conditions, a key gap in prior works. This ensures an optimized distribution strategy. Finally, it introduces a third-party creator delegation support which facilitates firmware updates delegation to multiple manufacturers, ensuring scalability and interoperability. Extensive experiments demonstrate robust security, high efficiency and reduces computational overhead, essential for sustainable smart cities.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100929"},"PeriodicalIF":10.4,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886231","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 comprehensive review of AI-driven plant stress monitoring and embedded sensor technology: Agriculture 5.0","authors":"Biplob Dey ,&nbsp;Romel Ahmed","doi":"10.1016/j.jii.2025.100931","DOIUrl":"10.1016/j.jii.2025.100931","url":null,"abstract":"<div><div>To address the future demand for food, fiber, and fuel, crop production must double by 2050. It requires reshaping Agriculture 5.0 by overcoming typical limitations in crop yields caused by biotic and abiotic stresses while integrating computational power, artificial intelligence (AI), and sensor technology. To date, many studies have explored AI applications in plant stress monitoring and detection, and a critical synthesis of algorithm suitability, sensor integration, and critical assessment of industrial scalability is still lacking. Thus, this review systematically evaluates 175 peer-reviewed articles (from an initial pool of 687, published between 2010 and 2024) to identify trends, performance benchmarks, and integration challenges in AI-driven plant stress detection. The trend analysis revealed a substantial increase in AI and sensor applications for plant stress monitoring, specifically after COVID-19, although ∼67 % of studies limit classification tasks to 5 or fewer classes, often lacking field validation. CNN-based classification models (e.g., VGG16, VGG19, and ResNet50) consistently perform well across stress types, whereas detection-focused models such as YOLO and lightweight architectures such as MobileNet show greater variability, particularly in biotic stress identification tasks. Traditional machine learning methods, such as support vector machines, decision trees, and k-nearest neighbors remain relevant for structured, low-resolution data, especially under constrained conditions. Optimization algorithms such as stochastic gradient descent (biotic stress) and Adam (abiotic stress) are widely used. Advancements in sensor technologies, including hyperspectral imaging, volatile organic compound (VOC) detection via electronic noses, and real-time monitoring systems enable noninvasive and continuous stress detection. In parallel, the deployment of industrial robots equipped with embedded AI and multimodal sensors allows for automated, high-frequency stress surveillance and precision intervention in commercial-scale crop systems. Aligning AI models with specific sensor modalities is pivotal for developing scalable, interoperable, and industrial-grade monitoring systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100931"},"PeriodicalIF":10.4,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879418","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 hybrid priority-enabled deep Q-network for machine degradation-based dynamic scheduling of assembly line","authors":"Jinhao Du ,&nbsp;Yarong Chen ,&nbsp;Jabir Mumtaz ,&nbsp;Longlong Xu ,&nbsp;Pei Li ,&nbsp;Ripon K. Chakrabortty","doi":"10.1016/j.jii.2025.100927","DOIUrl":"10.1016/j.jii.2025.100927","url":null,"abstract":"<div><div>As production systems evolve, the performance degradation of machines can result in increased downtime and lower throughput; preventive maintenance is needed to fully or partially restore the initial performance of machines, making dynamic scheduling essential. This problem is modeled in this study as a Markov decision process, where each machine agent assigns components and their placement sequences based on the dynamic production status of the assembly line. This approach enables intelligent adaptation to changing machine conditions, enhances efficiency, and minimizes delays. An intelligent decision-making framework has been developed to handle agile planning and the scheduling of dynamic problems of an assembly line. This multi-layer framework is integrated with a virtual simulation model of the existing physical production system, enabling informed decision-making. An adaptive hybrid priority deep Q-network (AHP-DQN) algorithm is proposed, utilizing a distributed multi-agent system for decentralized decision-making among machines on the assembly line. To enhance the performance of the proposed AHP-DQN algorithm in environments characterized by complex state representations and uncertain machine performance degradation, two key components are customized: the neural network and experience replay buffer mechanism. First, a partially connected neural network incorporating noisy layers is employed to enhance exploration efficiency and robustness. Second, an adaptive hybrid prioritized experience replay buffer is introduced by combining line balancing with the rate to balance sampling quality and efficiency. The deep Q-network, enhanced with neural networks and experience priority mechanisms, enables the system to learn optimal balancing policies through interaction with its environment. Simulation results demonstrate that the adaptive hybrid model outperforms traditional balancing algorithms in terms of throughput and overall system performance. After investigating the performance of the AHP-DQN, it is validated that the proposed algorithm outperforms the existing approaches. Specifically, compared to the commonly used earliest completion time rule in industrial applications, the proposed method achieves a performance improvement of approximately 4–10%, highlighting its practical applicability and effectiveness.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100927"},"PeriodicalIF":10.4,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860640","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":"Monitoring corn growth with disease identification and yield prediction via advanced intelligent architecture","authors":"Mustafa Mhamed ,&nbsp;Zhao Zhang ,&nbsp;Man Zhang","doi":"10.1016/j.jii.2025.100922","DOIUrl":"10.1016/j.jii.2025.100922","url":null,"abstract":"<div><div>Corn is a vital food plant globally due to its significant financial value, beneficial wellness, and nutritional effects on humanity. The implementation of artificial intelligence (AI) in agriculture has progressed, but the vision systems, which promote machine efficiency, speed, and productivity, remain to be raised. Managing corn growth from planting to harvest and early disease diagnosis impacts yield, quality, and profitability. This research assists in anticipating growth and estimating overall damage and loss by providing New Comprehensive Corn Leaf Diseases (CCLD) data via Corn Growth Stages (CGS) with initial benchmark evaluations. Secondly, it proposed a Swin Vision Transformer with a novel Advanced Pooling Layer and PO-GELU function (PS-VT+ APL). This eliminates noise, lowers the computational burden, minimizes dimensions, captures the most beneficial local features, helps to optimize the loss functions, and optimizes efficiency. Thirdly, PS-VT+ APL successfully predicts the best time for Corn Diseased Daytime (CDDT) detection with an accuracy of 99.21%. It also efficiently identifies disease types using the Corn Leaf Diseases Types (CLST) set with an efficiency of 96.12%. In addition, it is essential to determine the best moment to identify each kind of corn disease. Finally, PS-VT+APL promptly and thoroughly recognizes and distinguishes complicated symptoms of corn diseases during growth, with the highest score (99.82%). Furthermore, it works more effectively and requires less time than the baseline methods. The recommended approach boosts the automation of corn systems while simultaneously achieving excellent effectiveness through a variety of operations. It can work in conjunction with real-time systems such as self-spraying systems and unmanned aerial vehicles (UAVs) and is flexible enough to manage a wide range of corn-related jobs. This study's findings benefit various areas, including decision-making, disease management, time-sensitive harvesting schedules, development methods, and corn robotic vision.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100922"},"PeriodicalIF":10.4,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867408","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":"Performance study of two-ways gradient octagonal hierarchical honeycomb with excellent energy absorption and crashworthiness properties","authors":"Shen Xu, Quanping Fu, Xinlong Guang, Huilan Huang, Xiaolin Deng","doi":"10.1016/j.jii.2025.100925","DOIUrl":"https://doi.org/10.1016/j.jii.2025.100925","url":null,"abstract":"The demand for lightweight structures with outstanding energy absorption capabilities is increasingly critical. Inspired by the stability inherent in triangular configurations, this study introduces an Octagonal Self-similar Hierarchical Honeycomb (OSHH), designed by integrating multiple triangles into a hierarchical system within an octagonal framework. To further enhance its performance, gradient strategies and hierarchical strategies are applied. This research systematically evaluates the energy absorption capacity and crashworthiness of the Two-Way Gradient Octagonal Hierarchical Honeycomb (TWGOHH) through experimental and numerical simulation methods. The study explores the effects of various gradient distribution strategies, angular gradient coefficients, length gradient coefficients, dual-parameter gradients (angle and length), and hierarchical strategies on structural performance. Results from numerical simulations reveal significant performance improvements driven by both gradient and hierarchical strategies. The gradient strategy enhances the energy absorption (EA), specific energy absorption (SEA), and crush force efficiency (CFE) by approximately 15 %, while the hierarchical strategy achieves increases exceeding 35 %. Additionally, the hierarchical strategy substantially reduces the Poisson's ratio under impact, which is crucial for performance enhancement. By integrating gradient and hierarchical strategies, the honeycomb structures exhibit notable improvements, offering fresh perspectives for advanced honeycomb structure design.","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"107 1","pages":"100925"},"PeriodicalIF":15.7,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898641","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":"Embodied intelligence-based hybrid edge computing networks for scalable task execution in Industrial IoT","authors":"Yiwen Wu, Jianhua He, Ke Zhang, Xiaoyan Huang, Fan Wu, Yin Zhang","doi":"10.1016/j.jii.2025.100916","DOIUrl":"https://doi.org/10.1016/j.jii.2025.100916","url":null,"abstract":"As Industry 5.0 advances, the increasing data volumes generated by end devices and the diverse and strict application requirements present significant challenges to traditional computing and communication infrastructures. There is a strong demand for more scalable and efficient computing and communication infrastructures. In this paper, we investigate a Hybrid Fog Computing Network (HFCN) architecture to enhance computing and data analytics capabilities in Industrial Internet of Things (IIoT) systems. In this architecture, Ad-hoc Fogs (A-Fogs) and Dedicated Fogs (D-Fogs) are formed to utilize the computing power of end devices and are integrated with cloud computing to create a seamless and scalable computing system. We propose a resource management framework and a novel Admission Control and Resource Allocation (ACRA) algorithm, which incorporates iterative optimization and Quality of Service (QoS)-awareness. The algorithm jointly considers computing and communication resources to maximize system utility while satisfying the QoS requirements of IIoT applications. The proposed ACRA algorithm is evaluated and compared with two baseline non-cooperative algorithms via a system-level simulator. Experimental results demonstrate the feasibility and scalability of large-scale task processing with HFCN. The cooperative ACRA algorithm achieves significant improvements in resource utilization, QoS, and processing capacity.","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"23 1","pages":"100916"},"PeriodicalIF":15.7,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144901450","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":"Hybrid neural network-based metaheuristics in designing robust supply chains under pre-disaster: A case study of blood supply chain","authors":"Reyhaneh Eslami, Negin Faraji, Mobina Mousapour Mamoudan, Fariborz Jolai, Amir Aghsami","doi":"10.1016/j.jii.2025.100923","DOIUrl":"https://doi.org/10.1016/j.jii.2025.100923","url":null,"abstract":"Effective disaster relief requires efficient supply chain management, particularly for perishable and non-perishable goods under uncertain conditions. This study aims to address the challenges of disaster supply chains by proposing a hybrid optimization model that minimizes response times and operational costs, while ensuring the rapid and reliable delivery of essential relief items to affected areas. The model combines predictive analytics and robust optimization techniques in a two-phase approach: pre-disaster planning and post-disaster response. Demand for essential goods is predicted using a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, optimized with Ant Colony Optimization (ACO) to improve accuracy. The ACO-optimized CNN-RNN model achieved a Mean Squared Error (MSE) of 0.028, Root Mean Square Error (RMSE) of 0.167, and a Coefficient of Determination (R²) of 0.93, demonstrating a 20% improvement in MSE and an 11% reduction in RMSE compared to the unoptimized baseline model. The optimization phase employs Aghezzaf’s robust optimization framework to handle uncertainties in supply, demand, and potential disruptions across the supply chain. The proposed mathematical model categorizes goods into perishable and non-perishable items, incorporates real-world constraints such as inventory expiration and transportation delays, and evaluates performance under various disaster scenarios. The model demonstrates superior predictive performance, achieving significant improvements in accuracy and robustness compared to baseline models. Validation was conducted using statistical tests and real-world scenarios, confirming the reliability of the model. Data authenticity was ensured by sourcing from validated databases and employing cross-referencing techniques for consistency checks. Sensitivity analysis further highlighted the model’s adaptability to different disaster conditions, demonstrating resilience and operational efficiency in minimizing response times and costs. Using the blood supply chain as a case study, the proposed model significantly enhances disaster management by providing a flexible and reliable framework for resource allocation and decision-making. By integrating advanced machine learning techniques with robust optimization, the model bridges the gap between theoretical approaches and practical disaster relief applications. This innovation offers decision-makers a strategic tool for pre-crisis planning, dynamic response strategies, and efficient supply chain operations, contributing to improved outcomes in disaster scenarios.","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"30 1","pages":"100923"},"PeriodicalIF":15.7,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144901451","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}