Project scheduling to minimize the time and cost in large-scale construction projects with repulsion-based improved arithmetic optimization

Optimally balancing project duration and cost significantly enhances overall project value. Nevertheless, managing this dual-objective optimization becomes increasingly challenging as the number of activities and their associated execution modes expand in large-scale construction projects. Current optimization algorithms, although effective for smaller discrete time-cost trade-off problems (DTCTP), typically become entrapped in local optima, thus limiting their capability for exhaustive Pareto-front exploration when applied to larger DTCTP cases. The present study developed a repulsive arithmetic optimization algorithm (RAOA), explicitly formulated to overcome these constraints. RAOA capitalizes on a multiobjective optimization scheme integrated with a distinctive repulsion-based update mechanism to efficiently traverse extensive search spaces, avoid local optimal traps, and accelerate solution convergence. Empirical validation performed on scenarios involving 81, 146, 208, and 291 activities confirms RAOA's superior performance relative to current methodologies, revealing improvements of at most 0.20% closer proximity to known best solutions with computational efficiencies enhanced by at least 97%. Thus, RAOA provides an effective, scalable approach, furnishing construction managers with advanced optimization tools for proficient scheduling in large-scale project environments. This study contributes by extending recent DTCTP benchmarks through the introduction of RAOA, which outperforms prior heuristics like hybrid heuristic meta-heuristic (HHMH) and reduced computational demand, revealing RAOA's superior balance between exploitation and exploration. Collectively, the findings demonstrate that RAOA furnishes construction managers with a scalable, computationally efficient tool for generating better duration-cost trade-offs in large-scale scheduling environments.