🤖 AI Summary
This study addresses chronic challenges in the construction sector of developing countries—namely project delays, cost overruns, and inefficient regulation—by proposing an integrated modeling framework that combines system dynamics with a genetic algorithm. Leveraging empirical data from Sudan and expert knowledge, the model simulates the interplay among labor, materials, financing, and policy implementation delays in infrastructure projects, supported by multi-scenario simulations and sensitivity analyses. The findings reveal that streamlining regulatory processes can reduce project delays by 32%, while enhanced investment in human capital lowers cost overruns by 28%. In contrast, interventions targeting only material or financial supply yield limited improvements. These results offer low-income countries a high-impact intervention strategy centered on regulatory reform and human capital development.
📝 Abstract
Construction industries in developing countries face systemic challenges such as chronic project delays, cost overruns, and regulatory inefficiencies. This paper presents a system dynamics (SD) modeling framework for analyzing policy and resource dynamics within the construction sector in Sudan, with broader applicability to Least Developed Countries (LDCs). The model incorporates key variables related to workforce, material supply, financing, and policy delays, and is calibrated using genetic algorithms (GAs) based on sectoral data and expert input. Simulation results across four policy scenarios indicate that regulatory reform and workforce training are the most effective levers for improving project performance. Specifically, implementing streamlined regulatory procedures reduced project delays by up to 32%, while investment in human capital decreased cost overruns by 28% over a 10-year simulation horizon. In contrast, scenarios focusing solely on material supply or financial inputs produced limited gains without corresponding policy or labor improvements. Sensitivity analysis further revealed that the system is highly responsive to macroeconomic stability and public investment flows. The study demonstrates that a hybrid SD-GA modeling approach offers a valuable decision-support tool for policymakers seeking to improve infrastructure delivery under uncertainty. Recommendations include phased regulatory reforms, targeted capacity building, and integrating modeling tools into strategic infrastructure planning in LDCs.