An advanced metaheuristic algorithm for resource leveling optimization in project management

Abstract

Resource leveling is a critical method in construction project management, aimed at minimizing resource fluctuations and improving utilization efficiency across project schedules. However, addressing the complexity and high dimensionality of real-world scheduling problems remains a significant challenge. This study introduces an advanced nature-inspired metaheuristic algorithm that integrates mountain gazelle optimizer (MGO) with opposition-based learning (OBL) strategy to enhance resource leveling optimization. The novel approach leverages the demonstrated advantages of MGO and the population diversity benefits of OBL to prevent premature convergence, avoid entrapment in local optima, and improve solution quality. A case study is conducted to validate the effectiveness of the proposed model. Experimental results indicate that the hybrid algorithm outperforms benchmark algorithms in terms of convergence speed, solution accuracy, and overall stability. These findings underscore the potential of developed method to support efficient and reliable scheduling in construction project environments.