Cost Control Management of Construction Projects Based on Fuzzy Logic and Auction Theory

In this study, an innovative method has been proposed for resource allocation among contractors in large construction projects. This method is designed based on a combination of machine learning techniques, fuzzy theory, and auction modeling. Resource allocation in the context of large construction projects, where multiple contractors work simultaneously, can pose a complex problem. Developing an efficient method to address this issue can contribute to improving project performance in terms of cost and construction delays. We have presented a three-stage method for resource allocation in large construction projects. In the first stage, machine learning techniques are utilized to develop two distinct neural network models for predicting costs and delays for each contractor. These models utilize the Genetic Algorithm (GA) to optimize their parameters. In the second stage, a fuzzy model is used, which takes inputs from the neural network models and other contractor-specific features. This model prioritizes the needs of the contractors. Finally, an auction model is employed to fairly distribute the limited project resources between the contractors in need. The implementation results indicate that the proposed method for allocating resources among contractors in large construction projects have achieved MAE and RMSE values of 18.88 and 22.98, respectively, demonstrating a significant performance improvement compared to other proposed methods.