GA-based decision support system for housing condition assessment and refurbishment strategies

Refurbishment work involves improvement, adaptation, upgrading, renovation, modernization, retrofit, and repair of existing housing. With limited land usage and being aware of sustainability, the refurbishment market has faced increasing needs worldwide in the last decade. Refurbishment projects on existing housing, however, have complex and uncertain characteristics that are recognized to be more difficult to manage than those faced with new building projects. During the long life cycle period of housing, most residents are undoubtedly faced with refurbishment requirements. However, it is not easy for most of them to make assessment and refurbishment related decisions due to the lack of knowledge and experience. This study presents a genetic algorithm (GA) based on-line decision support system (DSS) to help residents easily conduct the housing condition assessment and offers optimal refurbishment actions considering the trade-off between cost and quality. Two refurbishment models, budget priority and quality priority, are developed to explore the relationship among the life cycle cost, restoration cost and improved quality. The result reveals that the proposed DSS solves the problems arising from asymmetric information and conflicting interests between residents and contractors, as well as improves traditional housing condition assessment to be more effective and efficient to users.