Multi-Objective Optimization for Resource Allocation in Large-Scale Construction Projects: A Case Study Approach

Abstract

Effective resource allocation is critical to the success of large-scale construction projects, where competing objectives such as minimizing costs, reducing delays, and maximizing resource utilization must be carefully balanced. This study employs a multi-objective optimization framework to address resource allocation challenges in large-scale construction projects. By using real-world case studies, the research demonstrates the application of advanced optimization techniques, such as Pareto optimization and genetic algorithms, to identify trade-offs and achieve balanced solutions. The methodology integrates practical constraints such as project deadlines, resource availability, and stakeholder priorities to ensure realistic and actionable results. The findings highlight the importance of adopting a systematic approach to resource allocation, achieving a 20% improvement in cost efficiency and a 15% reduction in project delays across case studies. This research provides practical insights for construction managers, offering a robust framework for resource optimization in complex construction environments.