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Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty
Alternative TitleComput. Chem. Eng.
Ye, Yun1; Li, Jie2,3; Li, Zukui2; Tang, Qiuhua1; Xiao, Xin3; Floudas, Christodoulos A.2
2014-07-04
Source PublicationCOMPUTERS & CHEMICAL ENGINEERING
ISSN0098-1354
Volume66Issue:1Pages:165-185
AbstractScheduling of steelmaking-continuous casting (SCC) processes is of major importance in iron and steel operations since it is often a bottleneck in iron and steel production. In practice, uncertainties are unavoidable and include demand fluctuations, processing time uncertainty, and equipment malfunction. In the presence of these uncertainties, an optimal schedule generated using nominal parameter values may often be suboptimal or even become infeasible. In this paper, we introduce robust optimization and stochastic programming approaches for addressing demand uncertainty in steelmaking continuous casting operations. In the robust optimization framework, a deterministic robust counterpart optimization model is introduced to guarantee that the production schedule remains feasible for the varying demands. Also, a two-stage scenario based stochastic programming framework is investigated for the scheduling of steelmaking and continuous operations under demand uncertainty. To make the resulting stochastic programming problem computationally tractable, a scenario reduction method has been applied to reduce the number of scenarios to a small set of representative realizations. Results from both the robust optimization and stochastic programming methods demonstrate robustness under demand uncertainty and that the robust optimization-based solution is of comparable quality to the two-stage stochastic programming based solution. (C) 2014 Elsevier Ltd. All rights reserved.; Scheduling of steelmaking-continuous casting (SCC) processes is of major importance in iron and steel operations since it is often a bottleneck in iron and steel production. In practice, uncertainties are unavoidable and include demand fluctuations, processing time uncertainty, and equipment malfunction. In the presence of these uncertainties, an optimal schedule generated using nominal parameter values may often be suboptimal or even become infeasible. In this paper, we introduce robust optimization and stochastic programming approaches for addressing demand uncertainty in steelmaking continuous casting operations. In the robust optimization framework, a deterministic robust counterpart optimization model is introduced to guarantee that the production schedule remains feasible for the varying demands. Also, a two-stage scenario based stochastic programming framework is investigated for the scheduling of steelmaking and continuous operations under demand uncertainty. To make the resulting stochastic programming problem computationally tractable, a scenario reduction method has been applied to reduce the number of scenarios to a small set of representative realizations. Results from both the robust optimization and stochastic programming methods demonstrate robustness under demand uncertainty and that the robust optimization-based solution is of comparable quality to the two-stage stochastic programming based solution. (C) 2014 Elsevier Ltd. All rights reserved.
KeywordScheduling Steelmaking Continuous Casting Robust Optimization Two Stage Stochastic Programming Demand Uncertainty
SubtypeArticle
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.comphemeng.2014.02.028
URL查看原文
Indexed BySCI ; ISTP
Language英语
WOS KeywordMULTIPURPOSE BATCH PROCESSES ; CONTINUOUS-TIME FORMULATION ; STEEL PRODUCTION ; PLANT ; FRAMEWORK ; INDUSTRY
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Interdisciplinary Applications ; Engineering, Chemical
WOS IDWOS:000336373400014
Citation statistics
Cited Times:32[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Version出版稿
Identifierhttp://ir.ipe.ac.cn/handle/122111/11016
Collection研究所(批量导入)
Affiliation1.Wuhan Univ Sci & Technol, Dept Ind Engn, Wuhan, Hubei, Peoples R China
2.Princeton Univ, Dept Chem & Biol Engn, Princeton, NJ 08540 USA
3.Inst Proc Engn, State Key Lab Multiphase Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Ye, Yun,Li, Jie,Li, Zukui,et al. Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty[J]. COMPUTERS & CHEMICAL ENGINEERING,2014,66(1):165-185.
APA Ye, Yun,Li, Jie,Li, Zukui,Tang, Qiuhua,Xiao, Xin,&Floudas, Christodoulos A..(2014).Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty.COMPUTERS & CHEMICAL ENGINEERING,66(1),165-185.
MLA Ye, Yun,et al."Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty".COMPUTERS & CHEMICAL ENGINEERING 66.1(2014):165-185.
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