Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach
By C. Zhou, G. Yin, Xiao-Bing Hu. 2009
280 citations recorded by the source corpus.
Subfields: Industrial and Manufacturing Engineering, Materials Chemistry, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/44a914484104f0cb4c4d0168912592550b4dfd8f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Industrial and Manufacturing Engineering
- Ali Jahan
- Mehmet Ali Ilgin
- Yusof Ismail
- S. Sapuan
- Marjan Bahraminasab
- Md. Yusof B. Ismail
- Surendra Mohan Gupta
- Faizal Bin Mustapha
In Materials Chemistry
- Ali Jahan
- Yusof Ismail
- S. Sapuan
- Marjan Bahraminasab
- Mehmet Ali Ilgin
- Md. Yusof B. Ismail
- Surendra Mohan Gupta
- Faizal Bin Mustapha
In Mechanical Engineering
- Yusof Ismail
- S. Sapuan
- Marjan Bahraminasab
- Mehmet Ali Ilgin
- Surendra Mohan Gupta
- Ali Jahan
- Faizal Bin Mustapha
Citations in the corpus
- A comprehensive VIKOR method for material selection (2011, methodology): “Moreover, the other methods in material selection [18–25] do not have any provision for engineering design states in which satisfying the target value is desirable.”
- Development of a multi-criteria approach for the selection of sustainable materials for building projects (2011, background): “A large set of literature have made several attempt with measuring and assessing the performance of building systems with respect to different performance criteria like energy saving and thermal insulation, maintainability and so on (the literature here is extensive but a good review can be found in”
- Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art. (2010, background): “Zhou et al. (2006) carry out a similar”
- A framework for weighting of criteria in ranking stage of material selection process (2012, methodology): “Many of these methods require quantitative weights for the attributes [7–12].”