Expert systems for configuration at Digital: XCON and beyond
Communications of the ACM
Linear programming and network flows (2nd ed.)
Linear programming and network flows (2nd ed.)
Practical genetic algorithms
Analysis of assembly through product configuration
Computers in Industry
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Expert Systems and Applied Artificial Intelligence
Expert Systems and Applied Artificial Intelligence
Configuring Large Systems Using Generative Constraint Satisfaction
IEEE Intelligent Systems
Limits and opportunities in mass customization for "build to order" SMEs
Computers in Industry - Stimulating manufacturing excellence in small and medium enterprises
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Preference programming: Advanced problem solving for configuration
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Proof planning for maintainable configuration systems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A classification and constraint-based framework for configuration
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A constraint satisfaction approach to resolving product configuration conflicts
Advanced Engineering Informatics
Journal of Intelligent Manufacturing
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Customers can directly express their preferences on many options when ordering products today. Mass customization manufacturing thus has emerged as a new trend for its aiming to satisfy the needs of individual customers. This process of offering a wide product variety often induces an exponential growth in the volume of information and redundancy for data storage. Thus, a technique for managing product configuration is necessary, on the one hand, to provide customers faster configured and lower priced products, and on the other hand, to translate customers' needs into the product information needed for tendering and manufacturing. This paper presents a decision-making scheme through constructing a product family model (PFM) first, in which the relationship between product, modules, and components are defined. The PFM is then transformed into a product configuration network. A product configuration problem assuming that customers would like to have a minimum-cost and customized product can be easily solved by finding the shortest path in the corresponding product configuration network. Genetic algorithms (GAs), mathematical programming, and tree-searching methods such as uniform-cost search and iterative deepening A* are applied to obtain solutions to this problem. An empirical case is studied in this work as an example. Computational results show that the solution quality of GAs retains 93.89% for a complicated configuration problem. However, the running time of GAs outperforms the running time of other methods with a minimum speed factor of 25. This feature is very useful for a real-time system.