Applying genetic algorithms for construction quality auditor assignment in public construction projects

Random third party quality audits are mandatory by the regulations for public construction projects in Taiwan. This project and auditor selection process is normally carried out based on personal experience and the quality of the selection outcome is hard to predict and control. This is a difficult work assignment problem because there are normally hundreds of projects and dozens of auditors to choose from. The purpose of this research is to establish a genetic algorithm-based model to assist with the project selection and auditor assignment process. The model is set up to find the optimal match between the project characteristics and auditor expertise from approximately 5.09E + 29 possible combinations. Information provided by the Kaohsiung County Government is used to validate the model. The results show that the model is not only valid but also able to produce a “much better match” between projects and auditors when comparing to manual assignment.

► A GA-based model is developed to solve complex assignment problems in quality audit.
► Data collected from public agencies are used to develop and validate the model.
► The model produces good match between auditor expertise and project characteristics.
► The model can be used to improve the quality audit process for public construction projects.