Journal of Industrial Information Integration最新文献_第10页

Analysis of causes of welding defects in bridge weathering steel based on large language models 基于大语言模型的桥梁耐候钢焊接缺陷原因分析

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-11-01 Epub Date: 2025-09-14 DOI: 10.1016/j.jii.2025.100954

Ji Wang, Weibin Zhuang, Tianyuan Liu, Jinsong Bao, Xinyu Li

The analysis of the causes of welding defects in bridge weathering steel necessitates a multifaceted approach integrating alloy element influences, crack control, and parameter optimization, as single-perspective methodologies inadequately address root causes and hinder effective solution development. To address these challenges, a large language model-based method for analyzing the causes of welding defects in bridge weathering steel is proposed. This method first integrates information from different welding perspectives through a multi-perspective associative memory mechanism and employs a hybrid retrieval strategy to retrieve factual memory and historical information, enabling precise recall of relevant content and providing comprehensive support for problem-solving. Second, a ”inhibition-cognition” task optimization strategy refines the problem-solving process by suppressing irrelevant information, decomposing tasks, and iteratively revising through cognitive simulation, thereby establishing a clear and efficient problem-solving pathway. Finally, the accuracy and consistency of sub-task processing are ensured by an expert-guided task verification meta-prompting method, where dynamic closed-loop validation is incorporated and expert knowledge is fused. Quantitative results demonstrate that the proposed method achieves consistent improvements in both ROUGE-L and BERTScore metrics across different models, while expert evaluations further confirm its exceptional performance in key dimensions such as rationality and comprehensiveness. This method provides a novel approach for analyzing the causes of welding defects in bridge weathering steel, playing a critical role in enhancing the accuracy and efficiency of defect analysis.

分析桥梁耐候钢焊接缺陷的原因需要多方面的方法，包括合金元素的影响、裂纹控制和参数优化，因为单一视角的方法不能充分解决根本原因，阻碍了有效的解决方案的制定。为了解决这些问题，提出了一种基于大型语言模型的桥梁耐候钢焊接缺陷原因分析方法。该方法首先通过多视角联想记忆机制整合不同焊接角度的信息，并采用混合检索策略检索事实记忆和历史信息，实现对相关内容的精确回忆，为问题解决提供全面支持。其次，“抑制-认知”任务优化策略通过抑制不相关信息、分解任务、通过认知模拟迭代修正来细化问题解决过程，从而建立清晰高效的问题解决路径。最后，通过引入动态闭环验证和融合专家知识的专家引导任务验证元提示方法，保证子任务处理的准确性和一致性。定量结果表明，该方法在不同模型的ROUGE-L和BERTScore指标上取得了一致的改进，而专家评价进一步证实了该方法在合理性和综合性等关键维度上的卓越表现。该方法为分析桥梁耐候钢焊接缺陷的原因提供了一种新的方法，对提高缺陷分析的准确性和效率具有重要意义。

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引用次数: 0

Interoperability of AI-enhanced digital twins 人工智能增强的数字孪生的互操作性

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-11-01 Epub Date: 2025-09-24 DOI: 10.1016/j.jii.2025.100961

Umar Memon, Wolfgang Mayer, Matt Selway, Markus Stumptner

Interoperability is one of the biggest challenges when multiple digital twins are used in collaboration. Although attempts to standardise and define interfaces have made significant progress, real interoperability is still difficult to achieve. It is due to unstated assumptions, contextual factors, and quality characteristics not covered by conventional methods. This paper presents a composition framework that uses a meta-model to capture contextual factors and quality characteristics in a structured manner that is required for compatibility between the models. It is achieved by developing a meta-model that explicitly represents the quality characteristics that can be used to decide whether digital twin models can be validly composed. Validation of the approach is illustrated by examples showing how our approach identifies the issues that are otherwise hidden compatibility issues. This paper also provides an algorithm to provide reasoning logic for requirements assessment by making implicit assumptions and contextual factors explicit and enabling the composition of digital twin models to be more effective.

当在协作中使用多个数字孪生时，互操作性是最大的挑战之一。尽管标准化和定义接口的尝试取得了重大进展，但真正的互操作性仍然难以实现。这是由于未陈述的假设、背景因素和传统方法未涵盖的质量特征。本文提出了一个组合框架，该框架使用元模型以结构化的方式捕获上下文因素和质量特征，这是模型之间兼容性所必需的。这是通过开发一个元模型来实现的，该元模型明确表示可用于决定数字孪生模型是否可以有效组合的质量特征。通过示例来说明方法的验证，这些示例展示了我们的方法如何识别隐藏的兼容性问题。本文还提供了一种算法，通过将隐式假设和上下文因素明确化，从而为需求评估提供推理逻辑，从而使数字孪生模型的组合更加有效。

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引用次数: 0

A multimodal data fusion-based intelligent detection method for lump coal on underground conveyor belts in smart manufacturing 基于多模态数据融合的智能制造地下传送带块煤智能检测方法

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-11-01 Epub Date: 2025-10-26 DOI: 10.1016/j.jii.2025.100997

Le Chen , Ligang Wu , Qichao Ren

To address the challenges of low detection precision for lump coal on underground coal mine conveyor belts, this study proposes an intelligent detection method based on multimodal data fusion. The method is named YOLO DKH (YOLO Dynamic Snake Attention-KANA²-High-level Screening Feature Pyramid Network). This approach specifically targets the insufficient robustness of single-modal data under dust interference and varying lighting conditions in complex underground environments. First, a Deformable Spatial Attention (DSA) mechanism is designed, utilizing strip-shaped deformable convolution kernels along the x- and y-axes for feature extraction, which achieves adaptive geometric learning and reduces computational complexity simultaneously. Second, the KANA² dual-attention mechanism is proposed by combining regional attention with the KAN Conv module. Through B-spline smoothing and dual-branch processing, computational complexity is reduced, enhancing the fusion effect of RGB-infrared multimodal features. Then, a High-frequency Spatial Feature Pyramid Network (HSFPN) was constructed by integrating high-frequency perception modules and spatial dependency perception mechanisms to enhance multi-scale feature fusion by filtering out low-frequency background interference and capturing pixel-level spatial relationships. Finally, a comprehensive multi-modal RGB-infrared dataset comprising 9250 annotated images and 14,840 bounding boxes was constructed to provide a standardized benchmark for the development and validation of lump coal detection algorithms. The experimental results showed that the YOLO DKH model achieved 79.1 %, 74.3 %, and 77.2 % precision, recall, and [email protected], respectively, representing improvements of 6.03 %, 7.06 %, and 5.18 % compared to the baseline YOLOv11n model, while reducing the number of parameters by 2.71 %. and a 25.9 % reduction in single-image processing time to 6.1 milliseconds, providing an efficient and reliable technical solution for lump coal monitoring on underground conveyor belts in intelligent manufacturing.

针对煤矿井下传送带块煤检测精度低的问题，提出了一种基于多模态数据融合的块煤智能检测方法。该方法被命名为YOLO DKH （YOLO Dynamic Snake Attention-KANA2-High-level Screening Feature Pyramid Network）。该方法专门针对复杂地下环境中单模态数据在粉尘干扰和光照条件变化下鲁棒性不足的问题。首先，设计了一种可变形空间注意（DSA）机制，利用沿x轴和y轴的条形可变形卷积核进行特征提取，实现了自适应几何学习，同时降低了计算复杂度；其次，将区域注意与KAN转换模块相结合，提出KANA2双注意机制。通过b样条平滑和双分支处理，降低了计算复杂度，增强了红外多模态特征的融合效果。然后，结合高频感知模块和空间依赖感知机制构建高频空间特征金字塔网络（HSFPN），通过滤除低频背景干扰和捕获像素级空间关系增强多尺度特征融合；最后，构建了包含9250张带注释图像和14840个边界框的综合多模态rgb -红外数据集，为块煤检测算法的开发和验证提供了标准化基准。实验结果表明，YOLO DKH模型的准确率、召回率和[email protected]分别达到79.1%、74.3%和77.2%，与基线YOLOv11n模型相比分别提高了6.03%、7.06%和5.18%，同时减少了2.71%的参数数量。单幅图像处理时间缩短25.9%，达到6.1毫秒，为智能制造中块煤井下传送带监测提供了高效可靠的技术解决方案。

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引用次数: 0

Task-level failure diagnosis in process control systems under cyberattack based on multilevel business process models 基于多级业务流程模型的网络攻击下过程控制系统任务级故障诊断

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-11-01 Epub Date: 2025-10-31 DOI: 10.1016/j.jii.2025.101001

Xuqing Liang , Chunjie Zhou , Yu-Chu Tian , Peihang Xu , Minglu Wang , Xin Du

Ensuring the secure operation of process control systems (PCSs) is critical in the face of increasing cyberattacks. Diagnosing the causes of safety protection failures and predicting potential failure paths during cyberattacks can help maintain the system stability and security. However, cyberattacks often target not only assets but also control tasks or disrupt business processes, rendering existing asset-based failure diagnosis methods insufficient. To address this gap, this paper presents a novel task-level failure diagnosis approach based on multilevel business process models (BPMs) for PCSs. The model is constructed based on Business Process Model and Notation (BPMN) 2.0 by analyzing the interactions between devices, tasks, and business processes. It is then mapped to a multilevel Bayesian network (BN) for quantitative analysis under cyberattacks. Finally, the proposed approach is validated through a simulated distillation unit. Results show that cause diagnosis confirms sensing task failure (40.7%) as the main cause of business process failures, primarily due to data transmission failure (54.5%). The method effectively diagnoses failure causes at the task level and predicting potential system safety protection failure paths.

面对日益增多的网络攻击，确保过程控制系统（pcs）的安全运行至关重要。在网络攻击中，诊断安全防护故障的原因，预测潜在的故障路径，有助于维护系统的稳定性和安全性。然而，网络攻击往往不仅针对资产，还针对控制任务或破坏业务流程，使得现有的基于资产的故障诊断方法不足。为了解决这一问题，本文提出了一种基于多层次业务流程模型（bpm）的任务级故障诊断方法。通过分析设备、任务和业务流程之间的交互，该模型基于业务流程模型和符号（BPMN） 2.0构建。然后将其映射到多层次贝叶斯网络（BN）中进行网络攻击下的定量分析。最后，通过模拟蒸馏装置对该方法进行了验证。结果表明，原因诊断确认感知任务失败（40.7%）是业务流程失败的主要原因，主要原因是数据传输失败（54.5%）。该方法能有效地在任务级诊断故障原因，预测系统安全保护的潜在故障路径。

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引用次数: 0

F2P-Net: A Hybrid Prompt-Enhanced Dual-Branch Cooperative Network for Industrial Surface Defect Segmentation with Limited Data F2P-Net：用于有限数据下工业表面缺陷分割的混合快速增强双分支协作网络

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-11-01 Epub Date: 2025-10-19 DOI: 10.1016/j.jii.2025.100986

Kerong Yan , Shuai Chen , Min Xu , Peiye Sun , Rui Wang

Industrial surface defect detection is constrained by the scarcity of defective samples and by the insufficient capacity of current segmentation methods to precisely delineate defect boundaries. To address these challenges, we propose F2P-Net, a few-sample, highly precise industrial surface defect segmentation framework composed of three core modules. ViCNet (ViT and CNN collaborative encoder network) integrates a vision transformer backbone with an auxiliary convolutional branch to retain robust large-model priors while enhancing sensitivity to fine-scale textures and local irregularities. AFDec (automated geometric prompt and multi-scale feature fusion decoder) employs automated geometric prompts to localize potential defect regions and fuses hierarchical multi-scale features to improve boundary delineation and mask consistency. EVPT (edge-enhanced visual prompt tuning) is a fine-tuning module incorporating edge-explicit visual prompt to facilitate effective industrial domain adaptation of large vision models. The proposed method achieves considerable performance over existing full-data training approaches in metrics including mAP, Recall, and IoU using only 1.76 %∼3.06 % of training images across NEU_Seg, MT, KolektorSDD2, and DAGM2007 datasets. Under full-data training, it attains state-of-the-art segmentation accuracies with IoU scores of 86.03 %, 92.57 %, 78.77 %, and 82.55 %, respectively. The network provides a novel solution for industrial applications with few-sample, high-precision defect segmentation. Code is available at https://github.com/kerongYan/F2P-Net.

工业表面缺陷检测受到缺陷样本稀缺和当前分割方法精确描绘缺陷边界能力不足的限制。为了解决这些挑战，我们提出了F2P-Net，这是一个由三个核心模块组成的少数样本，高精度工业表面缺陷分割框架。ViCNet （ViT和CNN协同编码器网络）集成了视觉变换主干和辅助卷积分支，在保留鲁棒大模型先验的同时增强了对精细尺度纹理和局部不规则性的敏感性。AFDec （automated geometric prompt and multi-scale feature fusion decoder）采用自动几何提示定位潜在缺陷区域，并融合分层多尺度特征，提高边界划定和掩码一致性。EVPT（边缘增强视觉提示调整）是一种包含边缘显式视觉提示的微调模块，用于促进大型视觉模型的有效工业领域适应。该方法仅使用NEU_Seg、MT、KolektorSDD2和DAGM2007数据集上1.76% ~ 3.06%的训练图像，在mAP、Recall和IoU等指标上比现有的全数据训练方法取得了相当大的性能。在全数据训练下，IoU分数分别为86.03%、92.57%、78.77%和82.55%，达到了最先进的分割准确率。该网络为工业应用中少样本、高精度的缺陷分割提供了一种新的解决方案。代码可从https://github.com/kerongYan/F2P-Net获得。

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引用次数: 0

The next-generation digital twin: from advanced sensing towards artificial intelligence-assisted physical-virtual system 下一代数字孪生：从高级传感到人工智能辅助的物理虚拟系统

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-11-01 Epub Date: 2025-09-02 DOI: 10.1016/j.jii.2025.100942

Jianxiong Zhu , Yaxin Yang , Mingxuan Xi , Shanling Ji , Luyu Jia , Tao Hu

Due to the emerging technologies of the metaverse and the growth of the Internet of Things(IoTs), digital twin has became compelling research topics along with the field of industrial automation, robotics, etc. To understand the advancement of digital twin relating elements, three issues need to be mentioned. The first technology is the advanced sensing component mainly aiming to objects status identification, functional electronic materials to break detection limitation, and data-enhancement by virtual sensors. Among them, sensing with the ability of self-powered, high-sensitivity, and soft electronic dramatically facilitates digital twin in high-accuracy and fast response. Secondly, the physical-virtual model towards intelligent system in digital twin is summerized to utilize simulating real prototype and virtual reality, especially physical-virtual prototype, subsystems, and artificial intelligent-enhanced digital twin system. Finally, owing to the machine learning and artificial intelligence, the next-generation digital twin system with advnaced sensing, physical-virtual system, and artificial intelligent-enhanced in various applications in one system would be the future trend. This review not only systemly reports digital twin from sensing component, the fundamental theory to the physical-virtual prototype, and artificial intelligence-enhanced technologies, it also presnets the future trajectory of the next-generation of digital twin as well as the challenges for various potential applications.

由于新兴的超宇宙技术和物联网（iot）的发展，数字孪生与工业自动化、机器人等领域一起成为引人注目的研究课题。要了解数字孪生相关元素的进展，需要提到三个问题。第一种技术是先进传感组件，主要针对物体状态识别、突破检测限制的功能电子材料、虚拟传感器增强数据。其中，具有自供电、高灵敏度和软电子能力的传感极大地促进了数字孪生的高精度和快速响应。其次，总结了数字孪生智能系统的物理-虚拟模型，利用模拟真实样机和虚拟现实，特别是物理-虚拟样机、子系统和人工智能增强数字孪生系统。最后，由于机器学习和人工智能的发展，具有先进传感、物理虚拟系统和人工智能在一个系统中的各种应用增强的下一代数字孪生系统将是未来的趋势。本文不仅系统地介绍了数字孪生技术从传感元件、基础理论到物理虚拟样机、人工智能增强技术等方面的研究进展，还介绍了下一代数字孪生技术的发展轨迹以及各种潜在应用面临的挑战。

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引用次数: 0

Sustainable building material supplier assessment in Pythagorean neutrosophic setting using ITARA and MACONT methods 使用ITARA和MACONT方法对毕达哥拉斯中性环境下的可持续建筑材料供应商进行评估

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-11-01 Epub Date: 2025-11-04 DOI: 10.1016/j.jii.2025.101000

Tapas Kumar Paul , Madhumangal Pal

Supplier selection plays a pivotal role in the construction industry, particularly for companies that seek to harmonize sustainability, innovation, and quality in their operations. This paper introduces a novel multi-criteria decision-making (MCDM) framework that combines the Indifference Threshold-Based Attribute Ratio Analysis (ITARA) and the Mixed Aggregation by Comprehensive Normalization Technique (MACONT) within a Pythagorean neutrosophic environment. The model is designed to assess suppliers using four critical dimensions: product performance, innovation in the supply chain, service effectiveness, and environmental sustainability, tailored to meet the strategic needs of a prominent Indian company. By utilizing Pythagorean neutrosophic sets, the proposed approach effectively addresses uncertainties and ambiguities in expert evaluations, resulting in a more adaptable and dependable decision-making process. The application of this hybrid ITARA-MACONT method shows its effectiveness in selecting top-performing suppliers who align with these multifaceted requirements, thereby enabling companies to make decisions that prioritize both sustainability and innovation. The practical application of this method illustrates the model’s feasibility and robustness. Moreover, the proposed framework supports industrial information integration by enabling the structured fusion of heterogeneous expert judgments and quantitative performance metrics, critical for informed decision-making in digital supply chains.

供应商选择在建筑行业中起着关键作用，特别是对于那些寻求在运营中协调可持续性、创新和质量的公司。提出了一种基于无差异阈值的属性比分析（ITARA）和综合归一化混合聚合技术（MACONT）的多准则决策框架。该模型旨在通过四个关键维度来评估供应商：产品性能、供应链创新、服务效率和环境可持续性，以满足一家知名印度公司的战略需求。通过利用毕达哥拉斯中性集，该方法有效地解决了专家评估中的不确定性和模糊性，从而使决策过程更具适应性和可靠性。这种ITARA-MACONT混合方法的应用显示了其在选择符合这些多方面要求的最佳供应商方面的有效性，从而使公司能够做出优先考虑可持续性和创新的决策。该方法的实际应用证明了该模型的可行性和鲁棒性。此外，该框架通过实现异构专家判断和定量绩效指标的结构化融合来支持工业信息集成，这对数字供应链中的知情决策至关重要。

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引用次数: 0

Towards spatial-temporal meta-hypergraph learning for multimodal few-shot fault diagnosis 面向多模态小故障诊断的时空元超图学习

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-11-01 Epub Date: 2025-08-15 DOI: 10.1016/j.jii.2025.100924

Jinze Wang , Jiong Jin , Lu Zhang , Hong-Ning Dai , Adriano Di Pietro , Tiehua Zhang

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.

在智能工业环境中，故障诊断对于维护设备的安全性和可靠性至关重要。通过智能维护系统早期识别问题有助于防止停机，提高生产力并减轻危害。然而，存在两个主要挑战：首先，当机器出现故障时，通常会出于安全考虑停用它们，导致故障数据稀缺；其次，现有的方法忽略了工作条件之间的高阶关系，而未能同时考虑信号异质性和时空相关性。为了解决这些挑战，我们将动态时空超图构建集成到元学习中，提出了一种用于多模态少次故障诊断的时空元超图学习（MetaSTH-FD）。该框架首先将振动信号分解为多模态特征，然后构建超图来捕获复杂关系。我们的方法可以快速适应有限样本的新条件，而超图结构可以模拟多模态信号数据中的复杂关系。实验结果表明，在各种工作条件和噪音水平下，性能都有了显著提高，从而为智能制造中的智能维护提供了新的见解。

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引用次数: 0

Integrated lot-sizing and scheduling for parallel mixed-model automotive production lines with transportation resource constraints 考虑运输资源约束的混合汽车生产线集成批量与调度

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-11-01 Epub Date: 2025-09-15 DOI: 10.1016/j.jii.2025.100945

Lei Yue , Jietao Huang , Linshan Ding , Xiwen Cai , Yanming Sun , Tao Zou

The automotive industry is undergoing rapid transformation, accompanied by intensifying market competition. As a result, component manufacturers face mounting pressure to improve production efficiency and control costs, particularly in high-mix, low-volume manufacturing environments. This paper investigates the integrated lot-sizing and scheduling problem in a mixed-flow parallel production system for automotive components, with a particular constraint related to the availability and management of transportation resources during production. The connection between lot-size scheduling and transportation resource allocation is crucial to the overall efficiency of the system. Therefore, a novel mixed integer programming model is presented for the multi-objective problem to minimize the makespan, total tardiness, total setup cost, and inventory levels. The min–max normalization approach is applied to normalize the objectives with different dimensions. A hybrid sparrow search algorithm (HSSA) is proposed with multi-strategy fusion, i.e., integrated mutation strategy, particle swarm velocity position update strategy, Gaussian Cauchy perturbation strategy, and multi-point crossover strategy. Two instances with varying scales have been designed to assess the performance of the proposed algorithm in the context of a practical automotive components production environment. The parameters of the HSSA for different sizes of problems are tuned by Taguchi method. Performance evaluation of the proposed HSSA is carried out by conducting extensive experiments compared to three well-established algorithms. The results demonstrate the effectiveness and superiority of the proposed HSSA in solving the simultaneous lot-sizing and scheduling problem with transportation resource constraints.

汽车行业正在经历快速转型，市场竞争也在加剧。因此，零部件制造商面临着提高生产效率和控制成本的巨大压力，特别是在高混合、小批量的制造环境中。本文研究了汽车零部件混合流并行生产系统中的集成批量和调度问题，该问题与生产过程中运输资源的可用性和管理有关。批量调度与运输资源分配之间的联系对系统的整体效率至关重要。因此，提出了一种新的混合整数规划模型，用于求解最大完工时间、总延迟时间、总安装成本和库存水平的多目标问题。采用最小-最大归一化方法对不同维数的目标进行归一化。提出了一种多策略融合的混合麻雀搜索算法（HSSA），即综合突变策略、粒子群速度位置更新策略、高斯柯西摄动策略和多点交叉策略。设计了两个不同规模的实例，以评估所提出的算法在实际汽车零部件生产环境中的性能。采用田口法对不同规模问题的HSSA参数进行了整定。通过与三种成熟算法进行广泛的实验，对所提出的HSSA进行了性能评估。结果表明，所提出的HSSA算法在解决具有运输资源约束的同时分批和调度问题方面具有有效性和优越性。

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引用次数: 0

Self-adaptive solution for industrial integration of AI-based decision-making systems for industrial flows management 基于人工智能的工业流程管理决策系统集成自适应解决方案

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-11-01 Epub Date: 2025-10-14 DOI: 10.1016/j.jii.2025.100971

Ivo Perez Colo , Carolina Saavedra Sueldo , Luis Avila , Geraldina Roark , Gerardo G. Acosta , Mariano De Paula

From the perspective of systems theory, a production process can be conceptualized as an organized set of operations that primarily involves managing the flow of materials, goods, energy, and information. Optimal management of industrial flows is a complex decision-making problem that has been addressed for decades, from modeling and optimization theory to today’s artificial intelligence (AI) techniques. However, although many modern AI-based proposals have been successfully tested in various and diverse flow optimization problems, their performance and transferability to industrial plants are strongly dependent on their high-dimensional hyper-parameter settings. Typically, hyper-parameter tuning is still performed by human experts who spend a considerable amount of time conducting trial-and-error heuristic searches for optimal hyper-parameter configurations. This fact, in addition to being inefficient, makes democratization, integration, and scalability towards industrial systems inconvenient, as they commonly have limited qualified expert human resources. Keeping in mind this fact, in this work, we propose a simulation-based Bayesian optimization approach for autonomous optimal hyper-parameter adjustment of black-box AI-based decision-making techniques. Our proposal was tested on two flow optimization problems of very different nature and behavior, and each of them was addressed with different modern AI-based decision-making techniques.

从系统理论的角度来看，生产过程可以被概念化为一组有组织的操作，主要涉及管理材料、货物、能源和信息的流动。工业流程的优化管理是一个复杂的决策问题，从建模和优化理论到今天的人工智能（AI）技术，已经解决了几十年。然而，尽管许多现代基于人工智能的建议已经成功地在各种各样的流动优化问题中进行了测试，但它们的性能和可转移性在很大程度上依赖于它们的高维超参数设置。通常，超参数调优仍然由人类专家执行，他们花费相当多的时间进行试错启发式搜索，以获得最优的超参数配置。除了效率低下之外，这一事实还不利于工业系统的民主化、集成和可扩展性，因为它们通常只有有限的合格专家人力资源。考虑到这一事实，在本工作中，我们提出了一种基于模拟的贝叶斯优化方法，用于基于黑盒人工智能的决策技术的自主最优超参数调整。我们的建议在两个性质和行为非常不同的流程优化问题上进行了测试，每个问题都使用不同的现代基于人工智能的决策技术来解决。

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引用次数: 0