Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721983
Ye Zhu, Shiwen Xie, Yongfang Xie, Xiaofang Chen
Superheat is the difference between the temperature of electrolyte and the temperature of primary crystal in aluminum electrolysis production, which is related to the physical field, current efficiency and electrolytic cell life and other important indicators in production. Therefore, by monitoring and identifying the degree of superheat, various parameters and blanking in the aluminum electrolysis process can be reasonably adjusted to keep the degree of superheat within a reasonable and stable range, which is of great significance to the efficient operation of the entire aluminum electrolysis cell. At present, many scholars have studied the identification of superheat and achieved a certain accuracy, but there are stiff few studies on the identification of the trend of superheat change. Therefore, in this paper, by mining the time sequence information of various data in the production process of aluminum electrolysis, the Long Short Term Memory (LSTM) algorithm with dual-stage attention mechanism (DA-LSTM) is used to classify and identify the superheat trend. The first stage of DA-LSTM introduces input feature attention to increase the weight of more relevant features. In the second stage, time step attention is introduced, and different time steps are weighted. Finally, the effectiveness of this method is verified by comparing with other methods, and it has higher accuracy.
{"title":"Recognition of aluminum electrolysis overheat trend based on DA-LSTM Neural Network","authors":"Ye Zhu, Shiwen Xie, Yongfang Xie, Xiaofang Chen","doi":"10.1109/ICCSS53909.2021.9721983","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721983","url":null,"abstract":"Superheat is the difference between the temperature of electrolyte and the temperature of primary crystal in aluminum electrolysis production, which is related to the physical field, current efficiency and electrolytic cell life and other important indicators in production. Therefore, by monitoring and identifying the degree of superheat, various parameters and blanking in the aluminum electrolysis process can be reasonably adjusted to keep the degree of superheat within a reasonable and stable range, which is of great significance to the efficient operation of the entire aluminum electrolysis cell. At present, many scholars have studied the identification of superheat and achieved a certain accuracy, but there are stiff few studies on the identification of the trend of superheat change. Therefore, in this paper, by mining the time sequence information of various data in the production process of aluminum electrolysis, the Long Short Term Memory (LSTM) algorithm with dual-stage attention mechanism (DA-LSTM) is used to classify and identify the superheat trend. The first stage of DA-LSTM introduces input feature attention to increase the weight of more relevant features. In the second stage, time step attention is introduced, and different time steps are weighted. Finally, the effectiveness of this method is verified by comparing with other methods, and it has higher accuracy.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129336311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9722001
Xiangzhu Zhang, Lijia Zhang, D. Xu, Hailong Pei
Estimation of the drone’s distance-to-collision is the key to the indoor autonomous obstacle avoidance and navigation of monocular UAVs. At present, the distance-to-collision model mainly uses regression loss or ordinal regression for training Regression loss utilizes the continuity of distance, and ordinal regression loss utilizes the order of distance. To improve the prediction performance of the model, this paper proposes a multi-loss function trained deep learning model based on the linear combination of ordinal regression loss and regression loss. The regression loss can be obtained by adding a distance decoder after the ordinal regression estimation, without changing the original structure of the model. Finally, we test the model performance in public datasets and obtain good results.
{"title":"Multi-Loss Function for Distance-to-collision Estimation","authors":"Xiangzhu Zhang, Lijia Zhang, D. Xu, Hailong Pei","doi":"10.1109/ICCSS53909.2021.9722001","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9722001","url":null,"abstract":"Estimation of the drone’s distance-to-collision is the key to the indoor autonomous obstacle avoidance and navigation of monocular UAVs. At present, the distance-to-collision model mainly uses regression loss or ordinal regression for training Regression loss utilizes the continuity of distance, and ordinal regression loss utilizes the order of distance. To improve the prediction performance of the model, this paper proposes a multi-loss function trained deep learning model based on the linear combination of ordinal regression loss and regression loss. The regression loss can be obtained by adding a distance decoder after the ordinal regression estimation, without changing the original structure of the model. Finally, we test the model performance in public datasets and obtain good results.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122338934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9722025
Liming Zhu, Xiaping Fan, Wei Wang, Ming Li
Product quality assessment (PQA) aims to detect the quality of products through real-time physical measurement statistics. This problem is essentially a multivariate statistical analysis problem, which is prevalent in complex industrial process. However, there are few researches focusing on this problem. Besides, most existing PQA methods depend on the sense organs of evaluation experts or consumers, which is too subjective and the accuracy of evaluation results is often difficult to guarantee. In this article, we propose an instantaneous PQA method which performs two-step evaluation on dual grains with the concurrent static and dynamic slow features of quality data obtained by slow feature analysis. First step is the coarse-grained evaluation step, which is the analysis with static slow features and then four quality levels can be achieved. For each quality level, to perform finer evaluation, the second step, which is fine-grained evaluation step, analyzes dynamic slow features to get the range of normal fluctuation characteristics. Finally, the online evaluation procedure and judging rules are designed for new products. To evaluate the feasibility of the proposed method, a case which is to assess the quality of a batch of cigarettes produced by an actual cigarette factory is studied and the result conforms to the experience of cigarette experts and demonstrates that the proposed method realizes instantaneous quality assessment.
{"title":"Dual-Grained Clustering with Concurrent Evaluation of Static and Dynamic Slow Features for Instantaneous Product Quality Assessment","authors":"Liming Zhu, Xiaping Fan, Wei Wang, Ming Li","doi":"10.1109/ICCSS53909.2021.9722025","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9722025","url":null,"abstract":"Product quality assessment (PQA) aims to detect the quality of products through real-time physical measurement statistics. This problem is essentially a multivariate statistical analysis problem, which is prevalent in complex industrial process. However, there are few researches focusing on this problem. Besides, most existing PQA methods depend on the sense organs of evaluation experts or consumers, which is too subjective and the accuracy of evaluation results is often difficult to guarantee. In this article, we propose an instantaneous PQA method which performs two-step evaluation on dual grains with the concurrent static and dynamic slow features of quality data obtained by slow feature analysis. First step is the coarse-grained evaluation step, which is the analysis with static slow features and then four quality levels can be achieved. For each quality level, to perform finer evaluation, the second step, which is fine-grained evaluation step, analyzes dynamic slow features to get the range of normal fluctuation characteristics. Finally, the online evaluation procedure and judging rules are designed for new products. To evaluate the feasibility of the proposed method, a case which is to assess the quality of a batch of cigarettes produced by an actual cigarette factory is studied and the result conforms to the experience of cigarette experts and demonstrates that the proposed method realizes instantaneous quality assessment.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122875761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9722002
Rui Liang, Kai Wu, Sheng Xu, Tiantian Xu, Xiaoyi Ma, Lianyu Fu
Angle-of-arrival (AOA) target localization using the unmanned aerial vehicle (UAV) has been widely applied in many practical applications. To localize an invasive target quickly and accurately, both the estimation and UAV path optimization algorithms are required. This paper focuses on developing a path optimization method to improve the target estimation performance. Firstly, the problem formulation of AOA target localization is introduced. Secondly, the classical pseudolinear Kalman filter (PLKF) and the gradient-based path optimization are presented. Thirdly, we analyze the problems that existed in the previous methods and propose an improved gradient-descent path optimization algorithm combined with a simple grid search method. Finally, the simulation examples verify the effectiveness of the proposed methods.
{"title":"An Improved UAV Path Optimization Algorithm for Target Accurately and Quickly Localization","authors":"Rui Liang, Kai Wu, Sheng Xu, Tiantian Xu, Xiaoyi Ma, Lianyu Fu","doi":"10.1109/ICCSS53909.2021.9722002","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9722002","url":null,"abstract":"Angle-of-arrival (AOA) target localization using the unmanned aerial vehicle (UAV) has been widely applied in many practical applications. To localize an invasive target quickly and accurately, both the estimation and UAV path optimization algorithms are required. This paper focuses on developing a path optimization method to improve the target estimation performance. Firstly, the problem formulation of AOA target localization is introduced. Secondly, the classical pseudolinear Kalman filter (PLKF) and the gradient-based path optimization are presented. Thirdly, we analyze the problems that existed in the previous methods and propose an improved gradient-descent path optimization algorithm combined with a simple grid search method. Finally, the simulation examples verify the effectiveness of the proposed methods.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128842055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9722031
Jingjing Wang, Hongli Deng, Cun Wang, Xinliang Cao
For the problem of double robots working together in the welding process, this project proposes a path planning method based on Kmeans algorithm and ant colony algorithm. The Kmeans algorithm is used to classify all weld joint tasks and reasonably assign them to individual robots. The ant colony algorithm is used to sort the weld joints assigned to individual robots and also to plan the work route for each robot. The simulation results show that the task assignment to this method is more scientific and reasonable than the existing methods, and the path planning results are shorter than the existing ones.
{"title":"A Dual-Robot Welding Path Planning Method Based on Kmeans and Ant Colony Algorithms*","authors":"Jingjing Wang, Hongli Deng, Cun Wang, Xinliang Cao","doi":"10.1109/ICCSS53909.2021.9722031","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9722031","url":null,"abstract":"For the problem of double robots working together in the welding process, this project proposes a path planning method based on Kmeans algorithm and ant colony algorithm. The Kmeans algorithm is used to classify all weld joint tasks and reasonably assign them to individual robots. The ant colony algorithm is used to sort the weld joints assigned to individual robots and also to plan the work route for each robot. The simulation results show that the task assignment to this method is more scientific and reasonable than the existing methods, and the path planning results are shorter than the existing ones.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125259962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721965
Ting Sun, Cheng Liu, Xue-gang Wang
In this paper, a control strategy based on sliding mode control and radial basis function neural network is proposed for dynamic positioning vessels with nonlinearity, model uncertainty, time-varying disturbances, and input saturation. Sliding mode control is employed in the design of a novel nonlinear controller for dynamic positioning vessels to enhance the robustness. Radial basis function neural network is introduced to approximate model uncertainty and time-varying disturbances, which can mitigate the chattering problem of sliding mode control. Moreover, an auxiliary design system is applied to mitigate the effectiveness of input saturation, which is widely existed in the marine control actuators. The closedloop signals are proved to be stable by Lyapunov theory. In conclusion, the multiple simulations illustrate the feasibility and advantages of the presented anti-windup neural network-sliding mode controller.
{"title":"Anti-windup neural network-sliding mode control for dynamic positioning vessels","authors":"Ting Sun, Cheng Liu, Xue-gang Wang","doi":"10.1109/ICCSS53909.2021.9721965","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721965","url":null,"abstract":"In this paper, a control strategy based on sliding mode control and radial basis function neural network is proposed for dynamic positioning vessels with nonlinearity, model uncertainty, time-varying disturbances, and input saturation. Sliding mode control is employed in the design of a novel nonlinear controller for dynamic positioning vessels to enhance the robustness. Radial basis function neural network is introduced to approximate model uncertainty and time-varying disturbances, which can mitigate the chattering problem of sliding mode control. Moreover, an auxiliary design system is applied to mitigate the effectiveness of input saturation, which is widely existed in the marine control actuators. The closedloop signals are proved to be stable by Lyapunov theory. In conclusion, the multiple simulations illustrate the feasibility and advantages of the presented anti-windup neural network-sliding mode controller.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121663169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9722014
Yao Zou, Liang Zhong, Xiuyu He, W. He
A singularity-free robust adaptive trajectory tracking controller is designed for miniature helicopters with uncertain inertial parameters. Firstly, a position loop controller is developed with the saturation control scheme. Then, a novel attitude loop controller with initial condition constraint proposed for the attitude tracking to the command one. Further, adaptive laws with the projection algorithm are proposed to estimate the uncertain inertial parameters. It is demonstrated that, with the developed controller, the bounded trajectory tracking objective is accomplished.
{"title":"Singularity-Free Robust Adaptive Controller for Miniature Helicopters","authors":"Yao Zou, Liang Zhong, Xiuyu He, W. He","doi":"10.1109/ICCSS53909.2021.9722014","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9722014","url":null,"abstract":"A singularity-free robust adaptive trajectory tracking controller is designed for miniature helicopters with uncertain inertial parameters. Firstly, a position loop controller is developed with the saturation control scheme. Then, a novel attitude loop controller with initial condition constraint proposed for the attitude tracking to the command one. Further, adaptive laws with the projection algorithm are proposed to estimate the uncertain inertial parameters. It is demonstrated that, with the developed controller, the bounded trajectory tracking objective is accomplished.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130255937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9722033
Cheng Cheng, Jiejun Zhao, Xiaoli Luan, Li Mao, Fengdeng Guo
Since 2009, China has promulgated several laws and regulations to regulate the import of solid waste, but there has been a lack of supporting identification criteria. To provide detailed and feasible risk level identification criteria for imported appliances to guide the Customs identification of e-waste. This paper establishes a three-tier identification criterion which has 42 indicators covering: appearance, value of use, electrical safety risk, mechanical safety risk, toxic and hazardous substances risk. Using these indicators as input, an intelligent identification method constructed by support vector machine (SVM) algorithm could identify the risk level of imported appliances as low risk, medium risk, and high risk. To verify the effectiveness and practicality of this method, this paper uses the identification cases provided by Wuxi Customs. The results show that the identification method has high self-learning capability and accuracy.
{"title":"Imported Appliance Risk Level Identification Based on Support Vector Machine Algorithm","authors":"Cheng Cheng, Jiejun Zhao, Xiaoli Luan, Li Mao, Fengdeng Guo","doi":"10.1109/ICCSS53909.2021.9722033","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9722033","url":null,"abstract":"Since 2009, China has promulgated several laws and regulations to regulate the import of solid waste, but there has been a lack of supporting identification criteria. To provide detailed and feasible risk level identification criteria for imported appliances to guide the Customs identification of e-waste. This paper establishes a three-tier identification criterion which has 42 indicators covering: appearance, value of use, electrical safety risk, mechanical safety risk, toxic and hazardous substances risk. Using these indicators as input, an intelligent identification method constructed by support vector machine (SVM) algorithm could identify the risk level of imported appliances as low risk, medium risk, and high risk. To verify the effectiveness and practicality of this method, this paper uses the identification cases provided by Wuxi Customs. The results show that the identification method has high self-learning capability and accuracy.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130400184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721972
Heping Gu, Jun Mei
In this paper, a sampling control method based on adaptive dynamic programming is proposed. The general form and cost function of nonlinear systems are given, the famous Hamilton-Jacobi-Bellman (HJB) equation is derived, and the sampling controller is designed via the optimal control input. The neural network control is used to approximate the optimal cost function, and it is proved that the closed-loop system is uniformly ultimately bounded. Finally, numerical simulation is presented to show the feasibility of the proposed method.
{"title":"Optimal sampling control of nonlinear systems based on adaptive dynamic programming","authors":"Heping Gu, Jun Mei","doi":"10.1109/ICCSS53909.2021.9721972","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721972","url":null,"abstract":"In this paper, a sampling control method based on adaptive dynamic programming is proposed. The general form and cost function of nonlinear systems are given, the famous Hamilton-Jacobi-Bellman (HJB) equation is derived, and the sampling controller is designed via the optimal control input. The neural network control is used to approximate the optimal cost function, and it is proved that the closed-loop system is uniformly ultimately bounded. Finally, numerical simulation is presented to show the feasibility of the proposed method.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130933150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9722019
Jiangtao Xu, Jinliang Ding, Qing-da Chen, Ling Yi
Slab scheduling of hot rolling plays an important role in smart manufactory of heavy plate production. It faces the challenges of multiple specifications, small batch and characteristic mode of production. The wide-range fluctuation of product specifications leads the existing approaches difficult to used. To solve this problem, a novel slab scheduling approach based on heuristic and ant colony algorithms is proposed. The schedule problem is formulated in two stages, namely slab allocation and slab rolling sequence optimization. In the slab allocation stage, the strategy of selecting appropriate slabs from forward delivery is used. Then, an ant colony algorithm combined with a constraints handling strategy based on specification jump penalties is designed to solve the slab rolling sequence optimization problem. The computational experiments are carried out and the results demonstrate the effectiveness by the actual production data.
{"title":"Hot Rolling Scheduling of Heavy Plate Production Based on Heuristic and Ant Colony Algorithms","authors":"Jiangtao Xu, Jinliang Ding, Qing-da Chen, Ling Yi","doi":"10.1109/ICCSS53909.2021.9722019","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9722019","url":null,"abstract":"Slab scheduling of hot rolling plays an important role in smart manufactory of heavy plate production. It faces the challenges of multiple specifications, small batch and characteristic mode of production. The wide-range fluctuation of product specifications leads the existing approaches difficult to used. To solve this problem, a novel slab scheduling approach based on heuristic and ant colony algorithms is proposed. The schedule problem is formulated in two stages, namely slab allocation and slab rolling sequence optimization. In the slab allocation stage, the strategy of selecting appropriate slabs from forward delivery is used. Then, an ant colony algorithm combined with a constraints handling strategy based on specification jump penalties is designed to solve the slab rolling sequence optimization problem. The computational experiments are carried out and the results demonstrate the effectiveness by the actual production data.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132065219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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