Yılmaz, Mehmet; Dede, Tayfun

The construction industry, a key contributor to global economic development, significantly impacts environmental pollution through greenhouse gas emissions. While conventional construction project management prioritizes cost and time reduction, environmental considerations are increasingly critical. This study addresses the time-cost-environmental impact trade-off problem by introducing a strength Pareto-based whale optimization algorithm (SP2WOA). The algorithm enhances solution diversity and achieves well-distributed Pareto fronts by integrating strength Pareto operators with the whale optimization algorithm. Its effectiveness is demonstrated through three diverse construction projects. Compared to previous algorithms (non-dominated sorting genetic algorithm-II, opposition-based multi-objective differential evolution, golden ratio-based opposite teaching-learning-based optimization and enhanced multi-objective grasshopper optimization algorithm), SP2WOA achieves superior balances between time, cost and environmental impact, using 33-60% fewer function evaluations. Statistical analysis using Wilcoxon rank tests confirms SP2WOA's superiority in both hypervolume and spread metrics. The results demonstrate that SP2WOA offers a valuable tool for sustainable construction project management.