Genetic Programming for Dynamic Resource Allocation in Construction Projects: A Simulation-Based Case Study

Construction projects require continuous allocation of heterogeneous resources such as workers, teams, and machinery. Traditional scheduling and assignment methods often assume static conditions and try to adapt when site conditions change or delays occur. This paper proposes a Genetic Programming (GP)–based framework to evolve resource allocation rules that adapt dynamically during project execution. Each GP individual represents a decision rule that maps the current construction-site state to resource–task assignments. The evolved rules are evaluated through simulation on a case study of a residential building and compared with the random resource allocation baseline approach. Results show that GP-generated rules can reduce project makespan, and improve resource utilization, as the experiments have shown an average cost of 1157.57 vs. 3085 for the baseline, which is about 62.5% lower, with a best of 813 vs. 2025 over 30 runs The findings highlight the potential of GP as a tool for dynamic resource allocation in construction management.