A novel modified mountain gazelle optimizer for enhancing resource scheduling problem in multi-project environment

Efficient resource allocation and leveling are critical to successful construction project scheduling, yet conventional methods often fail to balance project duration with stable resource utilization. This study introduces a modified mountain gazelle optimizer (mMGO), which extends the original single-objective MGO into a multi-objective optimization approach to address the complex trade-offs inherent in construction scheduling. In addition, the algorithm integrates opposition-based learning and dynamically controlled chaotic mapping to enhance global search capability and avoid premature convergence. The mMGO is embedded within a two-phase scheduling framework, in which the first phase generates resource-feasible baseline schedules, while the second phase redistributes activities to minimize fluctuations in multi-resource demand. To validate its performance, mMGO was verified through two case studies. Case study 1 consisted of five projects, each containing six activities, whereas case study 2 included three projects, each involving 60 activities. The results show that mMGO consistently achieved the highest hypervolume values, indicating closer convergence to the true Pareto front and greater diversity of non-dominated solutions. Moreover, mMGO produced schedules that simultaneously reduced project makespan, resource intensity, and resource utilization instability metrics. These findings indicate that mMGO has potential as a computational decision-support approach for multi-objective resource scheduling in benchmark multi-project environments.