Resource-constrained discrete time-cost trade-off optimization in construction projects using nature-inspired algorithms

Efficient management of time and cost in construction projects is often hindered by limited resource availability and the discrete nature of execution alternatives. This study addresses the resource-constrained discrete time-cost trade-off problem (RC-DTCTP) by proposing a multi-algorithmic optimization framework using six nature-inspired algorithms: NSGA-III, MOPSO, MOACO, MOTLBO, MOWOA, and SPEA2. Each algorithm is rigorously evaluated based on 13 performance metrics, including convergence, diversity, and computational efficiency. A benchmark case study comprising 18 multi-mode construction activities is used for comparative validation. Among all, the multi-objective teaching-learning-based optimization (MOTLBO) algorithm demonstrated superior performance, achieving the most balanced trade-off between project duration and cost. Additionally, post-Pareto analysis using multi-criteria decision-making (MCDM) techniques—TOPSIS and the entropy weight method—was employed to identify the best compromise solution under varying stakeholder preferences. Sensitivity analysis further confirmed the robustness of MOTLBO across different resource availability scenarios. The proposed framework not only enhances algorithmic benchmarking for RC-DTCTP but also bridges the gap between computational optimization and practical decision-making in construction planning. This study provides a valuable decision-support tool for project managers seeking cost-effective and time-efficient scheduling solutions under realistic constraints.