Automating knowledge acquisition for constraint‐based product configuration

Abstract

PurposeProduct configuration is considered as one of the most successful applications of knowledge‐based approaches in the past decade. Knowledge‐based configurations can be classified into three different approaches, namely, rule‐based, model‐based and case‐based approaches. Past research has mainly focused on the development of reasoning techniques for mapping requirements to configurations. Despite the success of certain conventional approaches, the acquisition of configuration knowledge is usually done manually. This paper aims to explore fundamental issues in product configuration system, and propose a novel approach based on data mining techniques to automatically discover configuration knowledge in constraint‐based configurations.Design/methodology/approachGiven a set of product data comprising product requirements specification and configuration information, the paper adopted an association rule mining algorithm to discover useful patterns between requirement specification and product components, as well as the correlation among product components. A configuration was developed which takes XML‐based requirement specification as input and bases on a constraint knowledge base to produce product configuration as output consisting of a list of selected components and the structure and topology of the product. Three modules are developed, namely product data modelling, configuration knowledge generation and product configuration generation module. The proposed approach is implemented in the configuration knowledge generation module. The configuration generation module realizes a resolution of constraint satisfaction problem to generate the output configuration.FindingsThe significance and effectiveness of the proposed approach is demonstrated by its incorporation in our configuration system prototype. A case study was conducted and experimental results show that the approach is promising in finding constraints with given sufficient data.Originality/valueNovel knowledge generation approach is proposed to assist constraint generation for Constraint‐based product configuration system.