Energy-aware material selection for product with multicomponent under cloud environment

Energy 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.