Value engineering, a powerful productivity tool
ICC&IE Selected papers from the 22nd ICC&IE conference on Computers & industrial engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A framework of collaborative design environment for injection molding
Computers in Industry
Product Configuration Frameworks-A Survey
IEEE Intelligent Systems
Change management in concurrent engineering from a parameter perspective
Computers in Industry
A framework of web-based conceptual design
Computers in Industry - Advanced web technologies for industrial applications
Architectural synthesis with possibilistic programming
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
Fuzzy multi-criteria selection of object-oriented simulation software for production system analysis
Computers and Operations Research
A genetic algorithm for the optimisation of assembly sequences
Computers and Industrial Engineering - Special issue: Sustainability and globalization: Selected papers from the 32 nd ICC&IE
An integrated model for supplier selection decisions in configuration changes
Expert Systems with Applications: An International Journal
Vendor selection in outsourcing
Computers and Operations Research
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
A hybrid approach for supplier cluster analysis
Computers & Mathematics with Applications
Managing logistics customer service under uncertainty: An integrative fuzzy Kano framework
Information Sciences: an International Journal
Hi-index | 12.06 |
Requirements of engineers or customers may result in product configuration change with product life cycle; effective management of product configuration can actually enhance productivity and customer satisfaction. This study aims to develop a three-phase evaluation model incorporating fuzzy theory, value engineering and multi-criterion to find optimal strategies for product configuration change, so as to select suitable combination of parts suppliers. Genetic algorithm was used to solve the issue concerned with part change in a short time with part quality, cost, time and reliability as evaluation parameters. Finally, as a case study, the display module of a notebook was analyzed, the results indicate that the evaluation can be effectively applied in a large-scale product configuration change.