Modeling policy and resource dynamics in construction: a hybrid system dynamics–genetic algorithm framework for a fragile economy (Sudan case)

Construction sectors in fragile and least developed economies often experience persistent project delays, low productivity, and inefficient resource utilization due to interacting regulatory, labor, supply-chain, and macroeconomic constraints. Conventional policy analyses typically examine these challenges in isolation, limiting their ability to capture dynamic feedback effects and long-term system behavior. This study develops a hybrid System Dynamics–Genetic Algorithm (SD–GA) framework to analyze construction sector performance, using Sudan as an illustrative case. The methodology integrates structured expert elicitation (n = 50), importance-based factor ranking, causal loop and stock-and-flow modeling, and GA-based calibration to align simulated system behavior with expert-derived influence hierarchies. Policy scenarios combining varying levels of workforce training, regulatory streamlining, and material supply-chain improvement are simulated. Results indicate that baseline conditions lead to sustained stagnation, with average project delays exceeding 120 days. Regulatory reform alone can reduce delays by approximately 30%, while integrated, high-intensity interventions can halve delays and increase construction output by nearly 60% by Year 20. Sensitivity analysis confirms the robustness of scenario rankings and identifies regulatory efficiency and workforce development as the highest-leverage policy levers, with supply-chain reforms exhibiting nonlinear threshold effects. The proposed framework offers a potentially transferable decision-support approach, subject to contextual recalibration.