Engineering Applications of Artificial Intelligence最新文献

{"title":"Seeing the unseen: Semantic segmentation and uncertainty quantification for delamination detection in building facades","authors":"Yuebo Meng ,&nbsp;Guotong Yin ,&nbsp;Songtao Ye ,&nbsp;Qiaoqiao Wang ,&nbsp;Guanghui Liu ,&nbsp;Xiaohan Li ,&nbsp;Xiaojiao Geng","doi":"10.1016/j.engappai.2026.114129","DOIUrl":"10.1016/j.engappai.2026.114129","url":null,"abstract":"<div><div>Accurate detection of delamination in building facades is critical for prolonging service life and ensuring structural safety. Current inspection methodologies heavily rely on manual interpretation, lacking efficiency and intelligent robustness. While infrared thermography provides a non-destructive means for detecting subsurface delamination, its accuracy is often compromised by low thermal contrast under uncontrolled conditions and the absence of uncertainty quantification in deep learning models. To address these limitations, this paper proposes TIHSNet, a novel delamination detection framework based on semantic segmentation and uncertainty quantification. Specifically, a physics-informed thermal gradient attention module is introduced to emphasize thermodynamically meaningful gradients and enable accurate delamination boundary delineation. Subsequently, a dual output mechanism is proposed to simultaneously generate prediction and uncertainty maps, enabling quantitative assessment of predictive reliability and identification of regions requiring expert review. To further enhance spatial localization, visible light images are integrated to capture tile boundary information and support spatial classification of delamination. Experiments were conducted on a self constructed dataset comprising 2102 infrared thermography and visible light images collected from reinforced concrete and brick masonry walls. The results demonstrate that TIHSNet achieves a precision of 96.1%, surpassing traditional thresholding methods with a 27.9% gain, and further outperforming existing deep learning approaches by 10.5%. The uncertainty quantification results further validate the model’s robustness and its ability to support reliable decision making in real world inspection scenarios.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114129"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Intelligent control framework for Unmanned Aerial Vehicle autonomous docking based on Linear Active Disturbance Rejection Control and improved Particle Swarm Optimization","authors":"Mingzhi Shao ,&nbsp;Xin Liu ,&nbsp;Wenchao Cui ,&nbsp;Chengmeng Sun ,&nbsp;Haiwen Yuan","doi":"10.1016/j.engappai.2026.114070","DOIUrl":"10.1016/j.engappai.2026.114070","url":null,"abstract":"<div><div>Autonomous aerial docking of Unmanned Aerial Vehicle (UAV) is essential for aerial refueling, payload replacement, and cooperative operations, yet existing methods often exhibit low docking accuracy, weak disturbance rejection, and empirical parameter tuning. To overcome these limitations, this study proposes an intelligent control framework that integrates Linear Active Disturbance Rejection Control (LADRC) with an Improved Particle Swarm Optimization (IPSO) algorithm. First, a six degree of freedom dynamic model of the UAV and cone sleeve system is developed, incorporating wind disturbance, turbulence, and parameter perturbations. Second, the LADRC method realizes decoupled control of altitude, lateral, and velocity channels, ensuring robust dynamic compensation. Third, the IPSO algorithm, an Artificial Intelligence (AI) based optimization approach, is employed to adaptively tune the controller bandwidth and observer gains. This AI enhanced parameter learning process improves the generalization capability of LADRC under varying flight conditions. Simulation and scaled flight experiments demonstrate that the proposed AI driven LADRC achieves stable docking under fifty percent perturbations, with a trajectory root mean square error of 0.04 m and a relative velocity error of 0.03 m per second. Compared with conventional controllers, the tracking error is reduced by up to 38 percent. These results confirm that combining LADRC with AI based optimization offers a robust and precise solution for UAV autonomous aerial docking in complex and uncertain environments.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114070"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Gated Memory-Guided Multi-scale spatio–temporal–spectral feature fusion network for unsupervised Internet of Things time series anomaly detection","authors":"Peng You,&nbsp;Peng Chen,&nbsp;Xi Li,&nbsp;Ang Bian","doi":"10.1016/j.engappai.2026.114104","DOIUrl":"10.1016/j.engappai.2026.114104","url":null,"abstract":"<div><div>Internet of Things (IoT) devices generate huge time series data during operation, crucial for device monitoring, fault prediction, and system security. However, these data often contain noise interference and exhibit complex spatio–temporal characteristics, posing significant challenges to anomaly detection. To address these challenges, this paper proposes an unsupervised anomaly detection model <strong>G</strong>ated <strong>M</strong>emory-guided <strong>M</strong>ulti-scale spatio–temporal–spectral feature fusion <strong>net</strong>work (<strong>GMMnet</strong>). GMMnet firstly leverages Positional Multi-scale Temporal-convolution and Multi-scale Spatio-spectral Self-attention to efficiently learn the temporal and spatio-spectral features of time series data, with an adaptive threshold filtering employed to mitigate high-frequency noise interference. By introducing Gated Memory-guided Fusion, GMMnet can accurately fuse the normal spatio–temporal–spectral features within the data, effectively guiding the model training process and significantly enhancing its generalization capability. Additionally, a Radial Basis Functions based Enhanced Reconstruction module is proposed to further improve GMMnet’s capability in detecting subtle anomalies. Extensive experiments on five publicly available IoT time series datasets demonstrate that the proposed method outperformed existing thirteen state-of-the-art baselines on nine metrics, with an average F1 score improvement of 17.72%.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114104"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Predicting stress in two-phase random materials and super-resolution method for stress images by embedding physical information","authors":"Tengfei Xing ,&nbsp;Xiaodan Ren ,&nbsp;Jie Li","doi":"10.1016/j.engappai.2026.114087","DOIUrl":"10.1016/j.engappai.2026.114087","url":null,"abstract":"<div><div>Stress analysis is essential in material design. In materials with complex microstructures, such as two-phase random materials (TRMs), failure is typically associated with stress concentration at phase interfaces that govern mechanical performance. Existing Image Super-Resolution (ISR) methods are mainly data-driven and rely on supervised learning, where the achievable magnification of stress images is tightly constrained by the resolution of the training dataset. This limits their applicability for TRMs, where high-resolution stress information at phase interfaces is essential but often unavailable. In practical engineering, limited pixel resolution in microstructure images further constrains stress image clarity and hinders the observation of stress concentration regions. To address this research gap, we propose a stress prediction framework tailored for TRMs that combines microstructural information with physics-based constraints. First, the framework employs a Multiple Compositions U-net (MC U-net) to predict stress in low-resolution material microstructures. By incorporating phase interface information, the MC U-net effectively reduces prediction errors at phase interfaces. Secondly, a Mixed Physics-Informed Neural Network (MPINN)-based stress ISR method (SRMPINN) is introduced. Unlike conventional ISR methods, SRMPINN leverages physical constraints to achieve stress image super-resolution without requiring paired high-resolution training images, enabling stress images to be generated at substantially high magnification factors, including non-integer scales, with magnification ratios not restricted by the training dataset. Finally, transfer learning is applied to perform stress analysis on TRMs with different loading states and anisotropy. The results demonstrate that the proposed framework achieves high accuracy, generalization, and flexibility, particularly in resolving stress concentrations at phase interfaces.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114087"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Enhanced recognition of low-discernibility Railway Sleeper Serial Numbers via dual-stage adaptive image enhancement and position prior-guided detection","authors":"Peng Shi ,&nbsp;Jingjing Guo ,&nbsp;Lu Deng ,&nbsp;Yingkai Liu ,&nbsp;Lizhi Long ,&nbsp;Shaopeng Xu","doi":"10.1016/j.engappai.2026.114110","DOIUrl":"10.1016/j.engappai.2026.114110","url":null,"abstract":"<div><div>Automatic recognition of Railway Sleeper Serial Numbers (RSSNs) is essential for traceability, quality management, and lifecycle maintenance of railway infrastructure. In practice, embossed serial numbers on concrete surfaces exhibit extremely low discernibility with minimal height variations (&lt;1 mm) and negligible color differentiation. Traditional Red-Green-Blue-based (RGB-based) image enhancement and Optical Character Recognition (OCR) methods face a fundamental limitation: they cannot directly capture the three-dimensional geometric features distinguishing embossed characters from their surroundings. To address this challenge, this study proposes an integrated framework based on line laser height imaging and position prior-guided detection with three key innovations: (1) a cascaded processing framework leverages geometric height information to overcome RGB-based method limitations; (2) a Dual-stage Adaptive Image Enhancement (DAIE) strategy converts 16 binary-digit (bit) height images into optimized 8-bit visualizations by systematically selecting optimal methods: modulo truncation for global structure and Minimum-Maximum (Min-Max) normalization for local detail enhancement; and (3) a Position Prior-guided Spatial Attention (PPSA) Feature Pyramid Network (FPN) integrates statistically-derived position priors to enhance small target detection. Comprehensive validation on 2234 images demonstrates superior performance: 98.2% F1-score and 99.38% recognition accuracy at 27 Frames Per Second (FPS), achieving 2.4% improvement over state-of-the-art methods. Ablation experiments confirm the individual contributions of the PPSA module (4.0%), the Small Target Enhancement (STE) module (1.4%), and the DAIE strategy (3.08%). Field testing in a prefabricated factory validates industrial applicability, providing a scalable technical framework and valuable reference for low-discernibility embossed industrial character recognition. Code is publicly available at <span><span>https://github.com/shipeng38/RSSN-recognition</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114110"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Few-shot transfer learning for laser welding prediction","authors":"Luchen Wu ,&nbsp;Shijie Wu ,&nbsp;Hongxin Hu ,&nbsp;Hao Sun ,&nbsp;Shuang Ma ,&nbsp;Zhenya Wang","doi":"10.1016/j.engappai.2026.113983","DOIUrl":"10.1016/j.engappai.2026.113983","url":null,"abstract":"<div><div>Laser wire filling welding is a key joining technique in the manufacturing of aluminum battery packs for new energy vehicles, yet predictive modeling of melt pool geometry remains limited by scarce experimental data. A two-stage transfer learning framework is developed by integrating multiphysics numerical simulation, data augmentation, and Bayesian neural network (BNN). High-fidelity multiphysics simulations within the experimental process window are generated to expand parameter space coverage and to provide physics-informed data for model pretraining. Limited experimental samples are augmented using a Wasserstein Generative Adversarial Network with gradient penalty applied to laser power and wire feed speed. Gaussian perturbations on travel speed are introduced to represent measurement uncertainties. A shallow BNN is pretrained on simulated samples and fine-tuned on the augmented experimental dataset using physics-consistent regularization and partial layer-freezing strategies. The augmentation strategy is evaluated through leave-one-out cross-validation on eight experimental samples, and generalization is examined using a separate test under previously unobserved travel-speed conditions. After inverse normalization, the framework achieves root mean square errors of 0.027 mm for melt pool depth and 0.025 mm for width, with coefficients of determination of 0.788 and 0.741, respectively. Uncertainty-aware quantitative analysis based on Sobol sensitivity indices and reliability assessment is conducted after model validation to characterize dominant parameter influences and to identify high-confidence process windows under limited data conditions. The proposed framework provides a general simulation-informed and uncertainty-aware learning strategy for manufacturing processes with severely limited experimental data.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 113983"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Hesitant fuzzy three-way decision-making for large-scale data based on a new distance measure and behavioral theory","authors":"Jing Li ,&nbsp;Haidong Zhang ,&nbsp;Zhuoma Dawa ,&nbsp;Yanping He","doi":"10.1016/j.engappai.2026.114105","DOIUrl":"10.1016/j.engappai.2026.114105","url":null,"abstract":"<div><div>In highly uncertain real-world environments, making robust decisions amid incomplete information and the cognitive biases of decision-makers remain critical challenges in project management and other complex system decision-making-related domains. Although the three-way decision-making (3WD) method based on hesitant fuzzy (HF) environments provides an effective approach for managing uncertainty, the current research still has shortcomings in several key areas. On the one hand, the distance formulas that are commonly used for computing hesitant fuzzy elements (HFEs) generally suffer from insufficient sensitivity to the information captured by the score function and weak discriminative power. On the other hand, the process of determining loss functions is subjective and fails to consider the behavioral psychological factors of decision-makers, making it difficult to reflect the cognitive characteristics of humans during actual decision-making processes. These issues collectively limit the adaptability and practicality of the existing decision-making methods in complex real-world environments. To address the aforementioned issues, this study is aimed at constructing an HF 3WD framework that possesses both cognitive rationality and computational robustness. To this end, the core contributions of this work are as follows. First, a novel HF distance measure is developed, significantly improving the ability to distinguish fuzzy information differences. Second, a novel <span><math><mi>o</mi></math></span>-dominance relation is introduced, and the conditional probability is calculated using a data-driven approach, eliminating the reliance on expert scoring and thereby improving the objectivity and accuracy of the conditional probability. Finally, an objective loss function is established, effectively capturing the decision-maker’s nonlinear value perceptions and comparative psychology in gain and loss scenarios. Furthermore, comparative experiments and parameter analyses are conducted in big data scenarios to validate the fact that the proposed method outperforms the existing methods in terms of classification accuracy and decision stability, demonstrating superior effectiveness and robustness. We believe that by simulating human judgments made under uncertainty, this method opens up new avenues for implementing artificial intelligence-based decision-making systems in high-risk scenarios.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114105"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Assessment and evaluation of machine learning algorithms for recommendation system of E-commerce based on bipolar fuzzy- method based on the removal effects of criteria-elimination and choice translating Reality-I approach","authors":"Muhammad Arsal,&nbsp;Ubaid ur Rehman,&nbsp;Abaid ur Rehman Virk","doi":"10.1016/j.engappai.2026.114107","DOIUrl":"10.1016/j.engappai.2026.114107","url":null,"abstract":"<div><div>E-commerce websites are increasingly depending on machine learning (ML) algorithms to improve their recommendation systems to deliver personalized user experiences and enhance customer satisfaction. ML models provide useful solutions, but choosing the best ML model is still difficult, especially for e-commerce platforms' recommendation systems. This selection challenge involves dual aspects (bipolarity), which means that both the positive and negative aspects of the evaluation criteria for ML models must be taken into consideration. It is a multi-criteria decision-making (MCDM) problem with uncertainty. However, previous research has failed to consider the bipolar character of these model selection criteria. Finding the right weights for the evaluation criteria also becomes important in MCDM, particularly when handling the bipolarity and uncertainty present in actual e-commerce situations. To overcome this limitation, we introduce a novel bipolar fuzzy (BF)-method based on the removal effects of criteria (MEREC)-elimination and choice translating reality (ELECTRE-I) methodology that combines the method based on the removal effects of criteria (MEREC) method's objective weighting capability with the elimination and choice translating reality (ELECTRE-I) technique's decision-ranking power. The methodology is used in a real-world case study that focuses on choosing the best ML model for an e-commerce platform's recommendation system. The proposed structure provides a strong decision-support tool that tackles the difficulties associated with choosing an ML algorithm and captures the dual nature of evaluation criteria. Comparative findings show how well the BF-MEREC-ELECTRE-I method works to provide data-driven, interpretable, and useful recommendations for e-commerce applications.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114107"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Biologically inspired vision fusion: Central-peripheral synergy for medical image classification","authors":"Rui Lu ,&nbsp;Long Yu ,&nbsp;Shengwei Tian ,&nbsp;Yukun Xiao","doi":"10.1016/j.engappai.2026.114026","DOIUrl":"10.1016/j.engappai.2026.114026","url":null,"abstract":"<div><div>The synergy between foveal and peripheral processing is fundamental to the efficiency of biological vision. While hybrid Convolutional Neural Network (CNN)-Transformer architectures aim to capture both local and global features, they often rely on static, predefined structures that struggle to dynamically align information and adaptively allocate computational resources, ultimately limiting their performance. To address this limitation, we introduce the Central-Peripheral Vision Transformer (CPVT), a novel architecture that explicitly and hierarchically mimics this biological dichotomy. CPVT employs fine-grained, convolutionally modulated attention in its shallow layers to emulate foveal vision, while seamlessly transitioning to a coarse-grained, global attention mechanism in deeper layers to emulate peripheral vision. This design is enhanced by two specialized Feed-Forward Networks that facilitate synergistic information interaction. Rigorously validated on diverse medical imaging benchmarks, CPVT achieves state-of-the-art performance, attaining classification accuracies of 87.98% on the International Skin Imaging Collaboration (ISIC) 2018 challenge dataset and 90.41% on the Kvasir dataset. These results demonstrate that an adaptive, hierarchical integration of biological vision principles can significantly enhance machine perception for medical image analysis.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114026"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 sustainable multi-objective economic production quantity model with learning–forgetting and green technology investment","authors":"Tanmay Halder,&nbsp;Bijoy Krishna Debnath","doi":"10.1016/j.engappai.2026.114064","DOIUrl":"10.1016/j.engappai.2026.114064","url":null,"abstract":"<div><div>The economic production quantity (EPQ) model enables production managers to determine optimal production lot sizes. Traditional EPQ models often overlook practical factors such as workforce learning and forgetting (LaF), imperfect production processes, and carbon emissions, which are increasingly critical for economically and environmentally conscious decision making. This study develops a multi-objective EPQ (MOEPQ) model that integrates LaF dynamics and green technology (GT) investment within a sustainable manufacturing framework. The model incorporates both non-rework and rework cycles, capturing realistic production behavior affected by labor efficiency variations and environmentally conscious investment decisions. The problem is formulated as a multi-objective optimization under a carbon cap-and-trade policy, aiming to minimize total cost and carbon emissions. To solve the problem, the non-dominated sorting genetic algorithm II (NSGA-II) and the strength pareto evolutionary algorithm 2 (SPEA2) are implemented with control parameters tuned using response surface methodology (RSM) to ensure convergence accuracy and solution diversity. Pareto fronts are generated, and statistical evaluation includes normality assessment using the Shapiro–Wilk test, followed by performance comparison through the Wilcoxon signed-rank test. The best solution is selected using the multi-criteria decision analysis (MCDA) approach through the technique for order preference by similarity to an ideal solution (TOPSIS). Numerical experiments demonstrate that integrating LaF and GT significantly reduces total cost and carbon emissions. Sensitivity analysis identifies the key economic and emission related parameters, offering practical insights for managerial decision making.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"168 ","pages":"Article 114064"},"PeriodicalIF":8.0,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}