A Framework for the Development of Personalized, Distributed Web-Based Configuration Systems

For the last two decades, configuration systems relying on AI techniques have successfully been applied in industrial environments. These systems support the configuration of complex products and services in shorter time with fewer errors and, therefore, reduce the costs of a mass-customization business model. We exploited the workbench based on the real-world business scenario of distributed configuration of services in the domain of information processing-based virtual private networks. Along with the growing complexity of configurable products, the required supporting software systems (configuration systems) have been improved and successfully applied in industrial environments (Fleischanderl et al. 1998; McGuiness and Wright 1998; Stumptner 1997). As a result, powerful tools based on different AI techniques such as constraint satisfaction (Junker 2001; Mailharro 1998) are available on the market.