A hybrid grey system theory-based subcontractor selection model for high-stakes construction projects

Proper selection of subcontractors should consider multiple criteria such as cost, schedule, quality, and effective project management and control capabilities. Several multiple-criteria decision-making (MCDM) models that utilize a weighted score have been developed to help contractors select subcontractors. Although such models are capable of considering multiple evaluation criteria, they may unintentionally cover up some subcontractors' deficiencies that can significantly impact project success. Considering such deficiencies is particularly important for high-stakes construction projects. The paper proposes a hybrid grey entropy relational analysis (GERA) model for subcontractor selection that integrates grey relational analysis (GRA) and the grey entropy model (GEM). The proposed GERA model evaluates subcontractors' performance from two perspectives; proximity degree and equilibrium degree. The proximity degree is used to aggregate a subcontractor's attributes into a comprehensive score, while the equilibrium degree is used to expose subcontractors' deficiencies that should exclude them from selection. A case study is used to demonstrate the feasibility of the proposed model. The proposed model can be easily adapted to various decision-making scenarios with similar requirements.