A hybrid approach using data envelopment analysis and case-based reasoning for housing refurbishment contractors selection and performance improvement

Abstract

The refurbishment market has grown greatly in the last decade. Relevant projects are becoming increasingly more demanding in the construction industry due to the emphasis on sustainability. Most refurbishment works, however, involve a higher level of risk and uncertainty, as well as more complex coordination than new buildings. These characteristics are likely to cause asymmetric information between contractors and tenants in a refurbishment process and thus affect customers’ satisfaction and project performance. This study proposes a systematic decision support approach to solve refurbishment asymmetric information problems by using case-based reasoning (CBR) and data envelopment analysis (DEA). The PZB model of the service quality and fuzzy sets are applied to support the DEA operation. With this intelligent approach, tenants can select an optimal refurbishment contractor according to their customization needs and contractors can find out their inefficient factors to improve their business competitiveness. The proposed hybrid decision support approach is expected to be useful for both tenants and contractors in those developed countries or regions with high refurbishment needs when they face similar problems.