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2nd International Conference on Frontiers of Intelligent manufacturing and Automation, CFIMA 2024

作者： Zhao, Tianchen Dong, Zhiming Wang, Xianpeng National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Ministry of Education Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University Shenyang China

ISBN: (纸本)9798400710681

When employing the network architecture search approach for designing a steel surface defect detector, there are issues with conflicting evaluation metrics and limited computational resources. To address this challenge, we propose a multiobjective evolutionary sharing strategy-based network architecture search method (MESNAS). MESNAS defines network architecture search as a bi-level multiobjective optimization problem combined with a weight sharing acceleration strategy designed to quickly and simultaneously optimize the accuracy and complexity of classification models. A refined search space suitable for different image classification and detection tasks is designed. Using the MOEA/D algorithm, which incorporates a variable-length encoding scheme, the search space is co-utilized and explored through targeted improvements to the crossover and mutation operators in the offspring generation process. The optimal network architecture obtained from the search is tested on the concrete surface defect classification task, and the steel continuous casting slab surface defect detection task. The experimental results show that the proposed algorithm has good model performance and portability in image classification as well as defect detection tasks with different data complexities and different categories. © 2024 Copyright held by the owner/author(s).

关键词： Continuous casting

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Structure-Based Robust Fractal Graph Neural Network with Molecular Fingerprint BERT for Molecular Property Prediction

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年

作者： Dong, Yaguo Xu, Meiling Tang, Lixin Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang110819 China

Accurate molecular property prediction is of great importance for AI-based drug design and bioinformatics. Despite recent promising progress, existing methods face challenges due to insufficient data labeling and imbalanced label distribution. Moreover, many current molecular property prediction models are based on 2D molecular graphs, neglecting the 3D characteristics of molecules. Therefore, in this paper, we propose the structure-based robust fractal graph neural network with Molecular Fingerprint Bidirectional Encoder Representations from Transformers (MFBERT) model for molecular property prediction. We construct the pre-trained MFBERT model to explore the use of unlabeled data and obtain representations from molecular extended connectivity fingerprints as the node features of molecular graphs. Then we design the fractal graph neural network that incorporates molecular 3D conformations while ensuring equivariance to molecular rotations and translations. A 3D feature extraction module and a coordinate update module are proposed to enhance the model's ability to capture molecular structural features. Additionally, weighted-gradient distributionally robust optimization is combined with the gradient descent algorithm to update model parameters, which can mitigate issues stemming from imbalanced label distribution and improve the robustness of our model. We conducted experiments on seven datasets from MoleculeNet, comprising three regression tasks and four classification tasks. Our method achieves the state-of-the-art performance compared to the existing models on all these datasets, which demonstrates its effectiveness for molecular property prediction. Additionally, we conducted ablation experiments on our designed modules, further validating the necessity of each component in our method. © 2025 IEEE.

关键词： Graph neural networks

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Evolutionary Direction Learning With Multivariate Gaussian Probabilistic Model for Multiobjective optimization

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IEEE Transactions on Evolutionary Computation 2025年

作者： Wang, Xianpeng Zhang, Jingchuan Tang, Lixin Liu, Yaxue Ministry of Education National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China

In recent years, utilizing data from the evolutionary process of multiobjective evolutionary algorithms (MOEAs) to learn knowledge and guide evolutionary search has become a popular research topic. However, existing knowledge learning (KL) frameworks often suffer from the low quality of collected datasets and the inefficiency of model construction, which significantly limits their effectiveness. To address this issue, this paper proposes a novel evolutionary direction learning (EDL) framework, which aims to learn the evolutionary direction (ED) knowledge for each objective to enhance the population generation of MOEAs. The proposed EDL incorporates an effective data collection method based on objective improvement to generate high-quality datasets, based on which a multivariate Gaussian probabilistic model is employed to learn ED knowledge for each objective through a data fusion modeling approach. Besides, a knowledge assignment method is designed to select the most suitable ED knowledge to guide the evolution of solutions. Experimental results on both synthetic and real-world problems demonstrate that the proposed EDL framework can accelerate the convergence of MOEAs and significantly improve their performance. A comparison of the proposed EDL with three state-of-the-art KL frameworks indicates that EDL is a highly competitive learning framework, achieving superior performance with larger datasets and impressive efficiency. © 1997-2012 IEEE.

关键词： Contrastive Learning

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Dynamic multiobjective operation optimization of blast furnace ironmaking process

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Advanced Engineering Informatics 2025年 66卷

作者： Yumeng Zhao Xianpeng Wang Xiangman Song National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 Liaoning China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Shenyang 110819 Liaoning China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang 110819 Liaoning China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University Shenyang 110819 Liaoning China

As the largest energy-consuming and carbon-emitting facility in the iron and steel industry, blast furnace needs to dynamically adjust its operating parameters according to the production conditions to minimize energy consumption and carbon emissions. Most of the previous studies focus only on the static operation optimization of blast furnace, which does not allow for the timely updating of operating parameters and the effective reduction of carbon emissions and production costs. Therefore, in this paper, a dynamic multiobjective operation optimization model for blast furnace, which integrates the identification approach of production condition variations, is developed and a tensor-based dynamic multiobjective evolutionary algorithm (T-DCMOEA/D) is devised to achieve cleaner production and lower cost of ironmaking process. The computational results based on real-world blast furnace operation optimization problems demonstrate that the proposed model and algorithm can effectively address the dynamic variations in the actual blast furnace production, which ensures that the blast furnace always operates in an optimized state. Furthermore, it is shown that our T-DCMOEA/D significantly outperforms some of the state-of-the-art dynamic multiobjective evolutionary algorithms in the literature, with regard to faster convergence, higher adaptivity, and better performance.

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Visualization and simulation of steel metallurgy processes

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International Journal of Minerals,Metallurgy and Materials 2021年第8期28卷 1387-1396页

作者： Te Xu Guang Song Yang Yang Pei-xin Ge Li-xin Tang Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang 110819China Liaoning Engineering Laboratory of Operations Analytics and Optimization for Smart Industry Northeastern UniversityShenyang 110819China Liaoning Key Laboratory of Manufacturing System and Logistics Shenyang 110819China Institute of Industrial&Systems Engineering Northeastern UniversityShenyang 110819China

Steel production involves the transfer and transformation of material and energy at different levels, structures, and scales, and this process incurs substantial information in the material and energy dimensions. Given the black-box feature of iron and steel production processes, process visualization plays an important role and inevitably benefits parameter correction, technical support decision-making, personnel training, and other aspects of the steel metallurgy industry. The technological characteristics of the entire process in the steel industry were analyzed in this study, a visualization technology route based on virtual reality(VR) was built, and the important components of visual simulation system for steel industry and the important technical points needed to realize the system were proposed. On the foundation, a visual simulation model for the process scheduling of the iron and steel enterprise raw materials' field, slab, and hot rolling processes was built, and a visualization simulation platform of the iron and steel metallurgy plant-wide process, including ironmaking, steelmaking, hot rolling, and cold rolling, was developed. Lastly, the effectiveness of platform was illustrated by practical application.

关键词： virtual reality steel metallurgy plant-wide visualization

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An optimal layout design for storage yard of container terminal

An optimal layout design for storage yard of container termi...

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19th International Conference on Industrial Engineering and Engineering Management

作者： Tang, Jian-Xun Tang, Li-Xin Liaoning Key Laboratory of Manufacturing System and Logistics Logistics Institute Northeastern University Shenyang China

ISBN: (纸本)9783642372698

This problem is to optimizeThis research is partly supported by State key Program of National Natural Science Foundation of China (71032004), the Fundamental Research Funds for the Central Universities (N090104002, N100704002) and by the 111 Project under Grant B08015. the layout design of blocks in new container yard. The yard cranes have restrictions on the width of blocks, and the layout of blocks and berths also have important impact on the driving route of yard trucks. Making use of bays, the lengths of blocks are discretized. An integer programming model is proposed for this problem, whose objective is to maximize the efficiency of trucks and the number of bays. In this model, there is also a lower bound on the minimum number of bays. Based on the characteristics of this model, a Filter-and-Fan algorithm is developed to solve this problem. The experimental results show the effectiveness of algorithm. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Trucks

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The simultaneous quay crane and truck scheduling problem in container terminals

The simultaneous quay crane and truck scheduling problem in ...

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International Conference on Intelligent Computation Technology and Automation

作者： Zhao, Jiao Tang, Lixin Liaoning Key Laboratory of Manufacturing System and Logistics Logistics Institute Northeastern University Shenyang China

ISBN: (纸本)9780769543536

This paper addresses the simultaneous quay crane and truck scheduling problem (QC&TSP) at a container terminal. This paper considers one quay crane and several trucks to unload containers from the vessel, and every truck needs some time to come back to the quay crane after transports one container to the yard, then it can transport another container continually. In this paper, every container is arranged as a task to unload separately. Based on the features of the scheduling problem, an improved PSO (Particle Swarms optimization) algorithm is presented to solve the scheduling problem, which develops a new formulation for updating velocity, besides, the decode method of the particles is designed in particular. Experiments are carried out especially to evaluate the performance of the proposed PSO algorithm, and compare with the solutions obtained by CPLEX software. The results show that the proposed PSO algorithm is efficient for solving the problem in the solution quality and computation time. © 2011 IEEE.

关键词： Scheduling

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Modeling and an ILP-based algorithm framework for the slab stack shuffling problem considering crane scheduling

Modeling and an ILP-based algorithm framework for the slab s...

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1st International Conference on Computing Control and Industrial Engineering, CCIE 2010

作者： Ren, Huizhi Tang, Lixin Liaoning Key Laboratory of Manufacturing System and Logistics Logistics Institute Northeastern University Shenyang China

ISBN: (纸本)9780769540269

This paper studies the slab stack shuffling problem considering crane scheduling in the slab yard of iron and steel industries. An ILP-based algorithm framework is proposed for the problem, in which an ILP model is proposed to solve the slab selecting subproblem and a specified procedure is developed to detect whether there is a feasible crane schedule for the chosen slabs. Through a number of iterations, the optimum solution can be obtained. The experiment results show that the algorithm framework is effective and efficient. © 2010 IEEE.

关键词： Steelmaking

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A new scater search approach for the singe machine total weighted tardiness scheduling problem with sequence-dependent setup times

A new scater search approach for the singe machine total wei...

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4th International Workshop on Advanced Computational Intelligence, IWACI 2011

作者： Guo, Qingxin Tang, Lixin Liaoning Key Laboratory of Manufacturing System and Logistics Logistics Institute Northeastern University Shenyang 110819 China

ISBN: (纸本)9781612843735

In this paper, we propose a scatter search based meta-heuristic algorithm to solve the single machine total weighted tardiness problem with sequence-dependent setup times. Both construction heuristics and random strategy based heuristics are adopted to generate the diversified population. A variable neighborhood search based local search method is adopted to improve the trial solutions and the combined solutions. To measure the diversification value between two solutions, we count the number of edges by which the two solutions differ from each other. We also propose a new strategy that the length of the reference set could be adjusted adaptively to balance the computing time and solving ability. Three different combination methods are presented to generate the new trial solutions with the solutions in the subsets. Computational results of the benchmark problems indicate that the proposed method is stable and can find 19 better solutions among the total 120 test problems than the best known solutions. The yielded average relative percentage deviations from the best known solutions and a recent scatter search algorithm were 1.98% and -3.14%. © 2011 IEEE.

关键词： optimization

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Dynamic Topic Analysis in Academic Journals using Convex Non-negative Matrix Factorization Method

arXiv

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arXiv 2025年

作者： Yang, Yang Zhang, Tong Wu, Jian Su, Lijie National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China China Criminal Police University Shenyang China Center for Advanced Process Decision-making Carnegie Mellon University PittsburghPA United States Beijing Institute for General Artificial Intelligence Beijing China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University ChinaShenyang Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang China

With the rapid advancement of large language models, academic topic identification and topic evolution analysis are crucial for enhancing AI’s understanding capabilities. Dynamic topic analysis provides a powerful approach to capturing and understanding the temporal evolution of topics in large-scale datasets. This paper presents a two-stage dynamic topic analysis framework that incorporates convex optimization to improve topic consistency, sparsity, and interpretability. In Stage 1, a two-layer non-negative matrix factorization (NMF) model is employed to extract annual topics and identify key terms. In Stage 2, a convex optimization algorithm refines the dynamic topic structure using the convex NMF (cNMF) model, further enhancing topic integration and stability. Applying the proposed method to IEEE journal abstracts from 2004 to 2022 effectively identifies and quantifies emerging research topics, such as COVID-19 and digital twins. By optimizing sparsity differences in the clustering feature space between traditional and emerging research topics, the framework provides deeper insights into topic evolution and ranking analysis. Moreover, the NMF-cNMF model demonstrates superior stability in topic consistency. At sparsity levels of 0.4, 0.6, and 0.9, the proposed approach improves topic ranking stability by 24.51%, 56.60%, and 36.93%, respectively. The source code is available at https://***/meetyangyang/CDNMF. © 2025, CC BY.

关键词： Non-negative matrix factorization

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