Application of Rao optimization algorithms and archive control mechanism for resource leveling problems in construction projects

Purpose This study aims to address the complexities of resource leveling in construction projects, particularly under conditions of fluctuating resource demands and limited availability. It introduces a novel approach combining Rao algorithms with an archive control mechanism to enhance the effectiveness of resource management. Design/methodology/approach The proposed method integrates the simplicity and robustness of Rao algorithms with an archive control mechanism to solve the Resource Leveling Problem (RLP). The archive control mechanism maintains a diverse pool of high-quality solutions, enabling a broader and more effective exploration of the solution space. Comprehensive simulations and comparative analyses with established techniques were conducted to evaluate the performance of the proposed approach. Findings Results demonstrate that the proposed method significantly improves resource allocation balance, reduces peak resource requirements, and minimizes overall project costs. The archive control mechanism plays a key role in preventing premature convergence and enhancing solution diversity, leading to more optimized project schedules. Originality/value This research introduces a novel hybrid framework for resource leveling that leverages the efficiency of Rao algorithms and the diversity-preserving capabilities of an archive control mechanism. The approach is particularly valuable for more complex construction projects where traditional methods often face limitations in handling resource fluctuations and optimization complexity.