Optimal selection and management of human resources in the construction industry are effective in improving productivity, reducing lost labor output and achieving project goals. This research aims to present a combined multi-objective intelligent model for optimizing the number of workers in workgroups at construction sites.
Criteria are scored by stakeholders based on project objective’s importance. Subsequently, optimal worker allocation in workgroups is prioritized and presented based on these criteria and project conditions to achieve the highest labor management efficiency and project needs at minimum cost. This combined model utilizes the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method and the Best Worst Method (BWM). BWM is employed for criteria weighting by project stakeholders, and its output is integrated into TOPSIS calculations.
In the case study, criteria were established based on the number of labor forces, productivity items, and cost, and project stakeholders rated them according to their significance. The presented model offers optimal choices for the number of workers in active work teams. Additionally, calculations demonstrated the significant role of criteria weighting and importance in prioritization.
This model provides a flexible and knowledge-based approach to addressing dynamic project environments, improving productivity, minimizing costs and aligning managerial decisions with project objectives.
