Optimization and Computational Modeling for Sustainable Construction Supply Chains: An Analytical Approach

The construction industry, particularly in emerging economies, faces persistent challenges in managing complex supply chains while meeting sustainability targets. This study proposes an integrated analytical approach that combines Principal Component Analysis (PCA) and Mixed-Integer Linear Programming (MILP) to optimize sustainable construction supply chains. Drawing on survey responses from 487 industry professionals and supporting project records, 35 operational and sustainability-related variables were statistically analyzed. PCA reduced these variables to seven key factors such asprocurement timeliness, inventory management, transport reliability, supplier collaboration, emissions tracking, cost monitoring, and compliance—which then formed the core input parameters for the MILP model. The optimization framework was designed to minimize total cost and CO2 emissions while enhancing sustainability performance, subject to operational, capacity, and environmental constraints. Empirical application to Indian construction projects demonstrated notable gains: a 9.9% cost reduction, 11.7% decrease in emissions, 6.3% improvement in delivery time, and a 5.8-point increase in sustainability scores compared to baseline operations. Sensitivity analysis confirmed the model’s robustness under variations in demand, supplier capacity, and emission limits, with computation times under 15 seconds across all scenarios. By coupling multivariate statistical preprocessing with computational optimization, this research offers both methodological innovation and practical value. The resulting decision-support framework is adaptable to diverse civil and structural engineering contexts, providing a fast, data-driven, and sustainability-focused tool for improving supply chain performance.