Formulations, Features of Solution Space, and Algorithms for Line-Pure Seru System Conversion
The line-seru conversion is usually used to improve productivity, especially in volatile business environment. Due to the simplicity, most researches focused on line-pure seru system conversion. We summarize the two existing models (i.e., a biobjective model and a single-objective model) of line-pur...
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Series: | Mathematical Problems in Engineering |
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doaj-92e03c2e432b4e059a52d600b3a62d9c2020-11-24T22:39:11ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472016-01-01201610.1155/2016/97483789748378Formulations, Features of Solution Space, and Algorithms for Line-Pure Seru System ConversionWei Sun0Qianqian Li1Chunhui Huo2Yang Yu3Ke Ma4Business School, Liaoning University, Shenyang 110316, ChinaBusiness School, Liaoning University, Shenyang 110316, ChinaBusiness School, Liaoning University, Shenyang 110316, ChinaInstitute of Systems Engineering, State Key Laboratory of Synthetic Automation for Process Industries, Northeastern University, Shenyang 110819, ChinaInstitute of Systems Engineering, State Key Laboratory of Synthetic Automation for Process Industries, Northeastern University, Shenyang 110819, ChinaThe line-seru conversion is usually used to improve productivity, especially in volatile business environment. Due to the simplicity, most researches focused on line-pure seru system conversion. We summarize the two existing models (i.e., a biobjective model and a single-objective model) of line-pure system conversion and formulate the three other usually used single-objective models in an integrated framework by combining evaluated performances and constraints. Subsequently, we analyze the solution space features of line-pure seru system conversion by dividing the whole solution space into several subspaces according to the number of serus. We focus on investigating the features between Cmax (and TLH) and subspaces. Thirdly, according to the distinct features between Cmax (and TLH) and subspaces, we propose four effective algorithms to solve the four single-objective models, respectively. Finally, we evaluate the computational performance of the developed algorithms by comparing with enumeration based on extensive experiments.http://dx.doi.org/10.1155/2016/9748378 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wei Sun Qianqian Li Chunhui Huo Yang Yu Ke Ma |
spellingShingle |
Wei Sun Qianqian Li Chunhui Huo Yang Yu Ke Ma Formulations, Features of Solution Space, and Algorithms for Line-Pure Seru System Conversion Mathematical Problems in Engineering |
author_facet |
Wei Sun Qianqian Li Chunhui Huo Yang Yu Ke Ma |
author_sort |
Wei Sun |
title |
Formulations, Features of Solution Space, and Algorithms for Line-Pure Seru System Conversion |
title_short |
Formulations, Features of Solution Space, and Algorithms for Line-Pure Seru System Conversion |
title_full |
Formulations, Features of Solution Space, and Algorithms for Line-Pure Seru System Conversion |
title_fullStr |
Formulations, Features of Solution Space, and Algorithms for Line-Pure Seru System Conversion |
title_full_unstemmed |
Formulations, Features of Solution Space, and Algorithms for Line-Pure Seru System Conversion |
title_sort |
formulations, features of solution space, and algorithms for line-pure seru system conversion |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2016-01-01 |
description |
The line-seru conversion is usually used to improve productivity, especially in volatile business environment. Due to the simplicity, most researches focused on line-pure seru system conversion. We summarize the two existing models (i.e., a biobjective model and a single-objective model) of line-pure system conversion and formulate the three other usually used single-objective models in an integrated framework by combining evaluated performances and constraints. Subsequently, we analyze the solution space features of line-pure seru system conversion by dividing the whole solution space into several subspaces according to the number of serus. We focus on investigating the features between Cmax (and TLH) and subspaces. Thirdly, according to the distinct features between Cmax (and TLH) and subspaces, we propose four effective algorithms to solve the four single-objective models, respectively. Finally, we evaluate the computational performance of the developed algorithms by comparing with enumeration based on extensive experiments. |
url |
http://dx.doi.org/10.1155/2016/9748378 |
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