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contributor authorBi, Luning
contributor authorZuo, Ying
contributor authorTao, Fei
contributor authorLiao, T. W.
contributor authorLiu, Zhuqing
date accessioned2017-11-25T07:20:32Z
date available2017-11-25T07:20:32Z
date copyright2017/16/2
date issued2017
identifier issn1530-9827
identifier otherjcise_017_03_031007.pdf
identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4236523
description abstractEnergy consumption in manufacturing has risen to be a global concern. Material selection in the product design phase is of great significance to energy conservation and emission reduction. However, because of the limitation of the current life-cycle energy analysis and optimization method, such concerns have not been adequately addressed in material selection. To fill in this gap, a process to build a comprehensive multi-objective optimization model for automated multimaterial selection (MOO–MSS) on the basis of cloud manufacturing is developed in this paper. The optimizing method, named local search-differential group leader algorithm (LS-DGLA), is a hybrid of differential evolution and local search with the group leader algorithm (GLA), constructed for better flexibility to handle different needs for various product designs. Compared with a number of evolutionary algorithms and nonevolutionary algorithms, it is observed that LS-DGLA performs better in terms of speed, stability, and searching capability.
publisherThe American Society of Mechanical Engineers (ASME)
titleEnergy-Aware Material Selection for Product With Multicomponent Under Cloud Environment
typeJournal Paper
journal volume17
journal issue3
journal titleJournal of Computing and Information Science in Engineering
identifier doi10.1115/1.4035675
journal fristpage31007
journal lastpage031007-14
treeJournal of Computing and Information Science in Engineering:;2017:;volume( 017 ):;issue: 003
contenttypeFulltext


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