Constraint Satisfaction Approach on Product Configuration with Cost Estimation

Due to the dynamism and heterogeneity of today’s markets, mass customization has been widely applied in the manufacturing industry with the objective of satisfying individual customer needs while maintaining near mass production efficiency. Product family design is an effective approach for mass customization where problem modeling and solution are two significant concerns. As a large amount of data with diverse variety need to be represented (including production data, customer requirements and design knowledge), powerful knowledge representation models are necessary to capture the large variety and complexity of data. Given a configuration problem description and representation, efficient reasoning methods are also required for configuration solution search and optimization. Cost is a critical composition and criterion for product family design; however, the integration of cost estimation and product family design has not been successfully addressed. In this thesis, an extended Dynamic Constraint Satisfaction Problem (DCSP) based product configuration and cost estimation system is proposed to to effectively represent the large amount of data with great variety and complexity in product family design as DCSP terms (i.e., variables, domains and constraints). In the extended DCSP-based system, structural constraints are proposed to capture the inherent hierarchy among modules and components. Activation constraints are composed to represent derivation relationships among variables in the extended DCSP-based system. As both structural and activation constraints can activate variables into the DCSP according to triggering conditions (i.e., hierarchical and derivation relationships among variables), they are utilized to dynamically extend the search space of