Optimal Charge Planning Model of Steelmaking Based on Multi-Objective Evolutionary Algorithm
暂无分享,去创建一个
Qing Liu | Xiang Li | Tieke Li | Bailin Wang | Jianping Yang | Caoyun Zou | Tieke Li | Xiang Li | Qing Liu | B. Wang | Jianping Yang | Caoyun Zou
[1] Sang-Woo Kim,et al. Recognition of Slab Identification Numbers using a Fully Convolutional Network , 2018 .
[2] Mohd Amran Mohd Radzi,et al. Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review , 2012 .
[3] Min Liu,et al. A multi-objective optimization approach for integrated production planning under interval uncertainties in the steel industry , 2016, Comput. Oper. Res..
[4] Fan Yang,et al. Improved Cross Entropy Algorithm for the Optimum of Charge Planning Problem , 2014 .
[5] Sergey Polyakovskiy,et al. A hybrid feasibility constraints-guided search to the two-dimensional bin packing problem with due dates , 2017, Eur. J. Oper. Res..
[6] Qiwen Yang,et al. Optimum Steelmaking Charge Plan with Unknown Charge Number Based on the Pseudo TSP Model , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.
[7] Baoxiang Wang,et al. Application of grey relational analysis and extreme learning machine method for predicting silicon content of molten iron in blast furnace , 2018, Ironmaking & Steelmaking.
[8] Xiao Liu,et al. A genetic algorithm heuristic approach to general outsourcing capacitated production planning problems , 2008 .
[9] Mohamed Elhoseny,et al. Optimizing K-coverage of mobile WSNs , 2018, Expert Syst. Appl..
[10] Mohamed Elhoseny,et al. A multi-objective transportation model under neutrosophic environment , 2018, Comput. Electr. Eng..
[11] Erik Hofmann,et al. Industry 4.0 and the current status as well as future prospects on logistics , 2017, Comput. Ind..
[12] Liu Qing,et al. Optimal charge plan model for steelmaking based on modified partheno-genetic algorithm , 2013 .
[13] L. Raslavičius,et al. New insights into algae factories of the future , 2018 .
[14] Weicheng Xie,et al. Convergence of multi-objective evolutionary algorithms to a uniformly distributed representation of the Pareto front , 2011, Inf. Sci..
[15] F. Richard Yu,et al. Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges , 2017, IEEE Communications Surveys & Tutorials.
[16] Lixin Tang,et al. The Charge Batching Planning Problem in Steelmaking Process Using Lagrangian Relaxation Algorithm , 2009 .
[17] Eduardo C. Xavier,et al. Heuristics for the strip packing problem with unloading constraints , 2013, Comput. Oper. Res..
[18] Mohammad Reza Yadollahpour,et al. A comprehensive solution for continuous casting production planning and scheduling , 2016 .
[19] D. E. Goldberg,et al. Genetic Algorithms in Search, Optimization & Machine Learning , 1989 .
[20] Liu Yi,et al. Unified modeling and intelligent algorithm of production planning for the process of steelmaking,continuous casting and hot rolling , 2013 .
[21] Toshiya Kaihara. New Systems Approach Towards The Realisation of Society 5.0 , 2017 .
[22] Mohamed Elhoseny,et al. Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm , 2019, Cluster Computing.
[23] Jianyu Long,et al. Production scheduling problems of steelmaking-continuous casting process in dynamic production environment , 2017 .
[24] Wei Liu,et al. Steel-Making and Continuous/Ingot Casting Scheduling of Mixed Charging Plan Based on Batch Splitting Policy , 2012 .
[25] Li Maojun,et al. A PARTHENO GENETIC ALGORITHM AND ANALYSIS ON ITS GLOBAL CONVERGENCE , 1999 .
[26] Min Huang,et al. On the integrated charge planning with flexible jobs in primary steelmaking processes , 2010 .
[27] Zhou Wang,et al. Fine Production in Steelmaking Plants , 2015 .