Case-based reasoning
Combinatorial algorithms: generation, enumeration, and search
ACM SIGACT News
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Towards a general ontology of configuration
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Product platform design and customization: Status and promise
Artificial Intelligence for Engineering Design, Analysis and Manufacturing - SPECIAL ISSUE: Platform product development for mass customization
A configuration design based method for platform commonization for product families
Artificial Intelligence for Engineering Design, Analysis and Manufacturing - SPECIAL ISSUE: Platform product development for mass customization
Artificial Intelligence for Engineering Design, Analysis and Manufacturing - SPECIAL ISSUE: Platform product development for mass customization
An agent-based approach for coordinating product design workflows
Computers in Industry
Platform-based product design and development: A knowledge-intensive support approach
Knowledge-Based Systems
Integrated Computer-Aided Engineering
Combinatorial optimization in system configuration design
Automation and Remote Control
Browser/server and client/server based dockside container crane CAD/CAE system
SEA '07 Proceedings of the 11th IASTED International Conference on Software Engineering and Applications
Integrated Vehicle Configuration System-Connecting the domains of mass customization
Computers in Industry
An agent-based approach for coordinating product design workflows
Computers in Industry
A fuzzy configuration multi-agent approach for product family modelling in conceptual design
Journal of Intelligent Manufacturing
Fuzzy agent-based approach for consensual design synthesis in product configuration
Integrated Computer-Aided Engineering
Hi-index | 0.00 |
For typical optimization problems, the design space of interest is well defined: It is a subset of Rn, where n is the number of (continuous) variables. Constraints are often introduced to eliminate infeasible regions of this space from consideration. Many engineering design problems can be formulated as search in such a design space. For configuration design problems, however, the design space is much more difficult to define precisely, particularly when constraints are present. Configuration design spaces are discrete and combinatorial in nature, but not necessarily purely combinatorial, as certain combinations represent infeasible designs. One of our primary design objectives is to drastically reduce the effort to explore large combinatorial design spaces. We believe it is imperative to develop methods for mathematically defining design spaces for configuration design. The purpose of this paper is to outline our approach to defining configuration design spaces for engineering design, with an emphasis on the mathematics of the spaces and their combinations into larger spaces that more completely capture design requirements. Specifically, we introduce design spaces that model physical connectivity, functionality, and assemblability considerations for a representative product family, a class of coffeemakers. Then, we show how these spaces can be combined into a “common” product variety design space. We demonstrate how constraints can be defined and applied to these spaces so that feasible design regions can be directly modeled. Additionally, we explore the topological and combinatorial properties of these spaces. The application of this design space modeling methodology is illustrated using the coffeemaker product family.