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7th IEEE International Congress on Information Science and Technology, CiSt 2023

作者： Amellal, Asmae Amellal, MaIssam Seghiouer, Hamid Ech-Charrat, Mohammed Rida University Abdelmalek Essaadi Laboratory of Modeling Optimization of Industrial and Logistics Systems Ensa Tétouan Morocco

ISBN: (纸本)9781665461337

This study presents a novel approach that fuses the BERT (Bidirectional Encoder Representations from Transformers) model for sentiment analysis with Deep Gaussian Processes (DGP) for automotive spare parts price prediction, targeting applications in supply chain management. Leveraging the strengths of both models, our method enhances NLP (Natural Language Processing) and time series forecasting, promoting informed decision-making and improved business intelligence strategies in a data-driven world. © 2023 IEEE.

关键词： Decision making

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MOEA/D With Spatial-Temporal Topological Tensor Prediction for Evolutionary Dynamic Multiobjective optimization

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Wang, Xianpeng Zhao, Yumeng Tang, Lixin Yao, Xin Ministry of Education Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Lingnan University Hong Kong China

When solving dynamic multiobjective optimization problems, most evolutionary algorithms attempt to predict the initial population in a new environment by mining the relationships between solutions during historical environment changes. However, the complex relationships between solutions and the limited amount of available data often make it difficult to extract useful information efficiently, which may deteriorate the prediction accuracy. To address this problem, this paper proposes a spatial-temporal topological tensor-based prediction method to generate the initial population in a new environment under the decomposition framework of MOEA/D. The method relies on the idea that the population distribution in each environment has topological similarity along the time dimension in the objective space, which makes it efficient to represent the population distribution in terms of a tensor and predict new solutions along each decomposition axis in a new environment by an improved tensor-based multi-short time series prediction method. Experimental results on various benchmark problems and a real-world problem show that the proposed method is competitive or even superior to state-of-the-art dynamic multiobjective evolutionary algorithms based on prediction strategies. IEEE

关键词： Tensors

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Joint Entropy Enhanced Multi-objective Bayesian optimization for Graph Pruning

Joint Entropy Enhanced Multi-objective Bayesian Optimization...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Zhang, Weiwei Xu, Meiling Tang, Lixin National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University Shenyang China

ISBN: (纸本)9798400704956

Graph learning processes entire attributed graphs, posing computational and scalability challenges. This study proposes a joint entropy enhanced multi-objective Bayesian optimization approach (JE-MoBopt) for efficient graph pruning. By employing the non-dominated sorting genetic algorithm II (NSGA-II), JE-MoBopt can effectively identify the Pareto front calculated by the surrogate models. Furthermore, we introduce a joint entropy search (JES) acquisition function to select an appropriate point from the Pareto solution set, thereby refining the surrogate models. We evaluate the JE-MoBopt approach on brain networks to satisfy both efficiency and accuracy requirements while preserving the important edges. © 2024 Copyright held by the owner/author(s).

关键词： Genetic algorithms

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An Enhanced Surrogate-Assisted Multi-Objective Differential Evolution Algorithm with Self-Adaptive Strategies for Order Planning in Hot Rolling Process: Surrogate-Assisted MODE with Self-Adaptive Strategies for OPHR Problem

An Enhanced Surrogate-Assisted Multi-Objective Differential ...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Che, Gelegen Tang, Lixin Zhao, Guodong Guo, Qingxin Guo, Jianqiang National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Liaoning Shenyang China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang China Northeastern University Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang China

ISBN: (纸本)9798400704956

This paper studies a crucial decision-making problem of order planning for a hot rolling process (OPHR). The OPHR problem is to determine the optimal sequence of orders, with the goals of minimizing penalties for earliness/tardiness, maximizing profits from special orders, and minimizing transition penalties of adjacent orders. A multi-objective differential evolution (MODE) method with self-adaptive strategies (SaMODE) is developed to tackle it. Then, a radial basis function neural network is integrated into the SaMODE (RBFNN-SaMODE) as a surrogate model to reduce the computational burden of evaluating objectives throughout evolution. The proposed method is extensively tested on real production data, and the results demonstrate its superiority over the classical MODE, yielding highly effective solutions. © 2024 held by the owner/author(s).

关键词： Radial basis function networks

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Bi-objective approach for lot-sizing problem in cold rolling production planning

Bi-objective approach for lot-sizing problem in cold rolling...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Jia, Weiyan Tang, Lixin Yang, Yang Dong, Yun National Frontiers Science Center for Industrial Intelligence and Systems Optimization Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang China

ISBN: (纸本)9798400704956

Planning in the cold rolling is an increasingly important vital aspect in steel industry. In this study, we investigate a cold rolling planning problem derived from a steel company, which is characterized by multi-item considerations with sequence-dependent setups in a single period. To tackle this problem, we introduce a bi-objective modified lot-sizing model, aiming at minimizing both the capacity waste and setup cost. A dedicated multi-objective evolutionary algorithm is proposed to solve this problem. The experiment results demonstrate the effectiveness of the proposed algorithm in achieving near-optimal solutions within a reasonable time, which can facilitate reductions in both production costs and time. © 2024 Copyright held by the owner/author(s).

关键词： evolutionary algorithm

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A Monte Carlo Simulation Approach for Estimating the Economic Impact of Sustainable Plantation Scenarios 11

A Monte Carlo Simulation Approach for Estimating the Economi...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Yaqin, Alvin Muhammad'ainul Huzaymah, Muhammad Yudistira Rahmatullah Rizalmi, Sigit Rahmat Wantira, Amanda Dwi Jamil, Ahmad Institut Teknologi Kalimantan Systems Modeling and Optimization Research Group Department of Industrial Engineering Balikpapan Indonesia Institut Teknologi Kalimantan Department of Industrial Engineering Balikpapan Indonesia Institut Teknologi Kalimantan Department of Logistics Engineering Balikpapan Indonesia

ISBN: (纸本)9798350389654

This paper investigates the economic impact of sustainable plantation scenarios for oil palm, rubber, cocoa, and coffee in East Kalimantan Province, Indonesia, using a Monte Carlo simulation approach. Currently dominated by oil palm, the region faces challenges regarding environmental sustainability and economic resilience. We propose a Monte Carlo simulation approach with a model to estimate the best scenarios of sustainable estate crop plantations based on net value over a year. Results indicate that Scenario 1, representing the existing plantation, has a high probability of incurring losses (17.3 %) and the lowest likelihood of achieving huge profits (32.8%). Scenario 3, which involves diversifying from oil palm and rubber into cocoa and coffee, offers moderate improvements over existing conditions. It shows a better outcome probability (35.5% huge profit and 12.4% loss) and has potential for significant maximum profits (325 trillion IDR). Scenario 3 is a viable option for short-term improvements. On the other hand, Scenario 2, which balances land distribution approximately equally for all commodities, demonstrates the highest probability of achieving huge profits (61.1 %) with minimal risk (0.01 % probability of loss) and also the highest average outcome in the simulation (113 trillion IDR), making it the most advantageous option. This study underscores the importance of diversifying plantation portfolios to enhance economic resilience and sustainability. Decision-makers can utilize these findings to optimize future plantation strategies, balancing economic growth with environmental stewardship. © 2024 IEEE.

关键词： Crops

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Steel Defect Detection Algorithm Based on Multiobjective ENAS Sharing Strategy 24

Steel Defect Detection Algorithm Based on Multiobjective ENA...

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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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A real-time temperature prediction method based on CNN-LSTM with MODE in steelmaking process

A real-time temperature prediction method based on CNN-LSTM ...

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IISE Annual Conference and Expo 2024

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

ISBN: (纸本)9781713877851

Prediction of molten steel quality is a key issue in the converter steelmaking process. Due to high temperature and physical-chemical reaction, it is difficult to predict the temperature of molten steel in real time through mechanism analysis. This is a challenging problem with black box characteristics. Here we propose a Convolutional neural networks (CNN) and long short-term memory (LSTM) networks model based on multi-objective differential evolution algorithm (MODE) for the real-time prediction problem in the steelmaking process. Specifically, the CNN is used to extract feature between multiple variables that affect the temperature of molten steel, and the LSTM is suitable for the time series model based on the irregular trends information. The multi-step output CNN-LSTM method is used to construct the temperature prediction model of molten steel, and a knee solution strategy is applied to MODE, which can balance the model accuracy and generalization ability. The experiments were conducted using real data collected from the converter steelmaking process. The results verify the effectiveness of the proposed model on multiple steel grades and outperform other artificial intelligence methods in multi-task prediction. © IISE Annual Conference and Expo *** rights reserved.

关键词： Convolutional neural networks

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BERT-Enhanced Deep Gaussian Regression: A Unified Approach for Sentiment Analysis and Price Prediction

BERT-Enhanced Deep Gaussian Regression: A Unified Approach f...

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International Conference on Information Science and Technology (ICIST)

作者： Asmae Amellal maIssam Amellal Hamid Seghiouer Mohammed Rida Ech-Charrat University Abdelmalek Essaadi Laboratory Of Modeling Optimization Of Industrial And Logistics Systems ENSA Tétouan Morocco

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A Multi-Objective Evolutionary Machine Learning Method for the Yield Strength Prediction of Strip Steel in Continuous Annealing

A Multi-Objective Evolutionary Machine Learning Method for t...

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IISE Annual Conference and Expo 2024

作者： Liu, Chang Tang, Lixin Zhang, Kainan Guo, Jiajing National Frontiers Science Center for Industrial Intelligence and Systems Optimization North-eastern University Shenyang110819 China Ministry of Education 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

ISBN: (纸本)9781713877851

In the continuous annealing process, the yield strength is the key property index to evaluate the quality of strip steel and guide the production. It is difficult to perform online quality measurement for numerous products in the steel industry, this brings significant challenges for process monitoring and operation optimization. In this paper, a multiobjective evolutionary machine learning method is proposed for the yield strength prediction of strip steel in continuous annealing. First, the factors affecting the yield strength of strip steel are analyzed and the prediction model based on a hybrid kernel support vector regression (HKSVR) is established. To improve the prediction accuracy, a multi-objective evolutionary optimization algorithm with a knee solution strategy is used to optimize the key parameters of the HKSVR. It is verified by the actual production data that the proposed method has high prediction accuracy and can meet the actual production demand. Moreover, the proposed method can help practitioners to construct quality analytics models for many other similar production lines, providing assurance for quality control and optimization with a good generalization. © IISE Annual Conference and Expo *** rights reserved.

关键词： Prediction models

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