A Hybrid Machine Learning and Metaheuristic Framework for Optimizing Time and Cost in Hospital Construction Projects

Abstract

The rapid aging and functional deterioration of Iran's hospital infrastructure—where over 60% of the 1,100 existing hospitals with 160,000 beds are considered obsolete—pose a critical challenge to achieving national healthcare goals. Moreover, bridging the gap to meet the target of 2.3 hospital beds per 1,000 people requires the addition of approximately 40,000 new beds, amid serious fiscal constraints. This study presents a data-driven decision-support framework to optimize construction time and cost in hospital projects, using actual data from 270 existing facilities. The proposed methodology integrates machine learning models—specifically MLP, SVR, and Random Forest—for predictive analysis, with metaheuristic algorithms including Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Artificial Bee Colony (ABC) for multi-objective optimization. Among the predictive models, SVR achieved the highest accuracy in estimating both cost and duration. Optimization results indicated that GWO outperformed the other algorithms, achieving the lowest normalized objective value. In the most efficient scenario, a 108-bed hospital at an optimal location minimized both cost (596 billion Rials) and time (4.45 years), while a fixed-capacity scenario of 300 beds increased both metrics but offered higher service output. The results provide a scalable, evidence-based tool for policymakers and infrastructure planners to evaluate trade-offs between time, cost, and capacity. The approach is particularly useful for strategic healthcare planning under limited resources.