Review on Optimization of Construction Project Scheduling Using Metaheuristic Algorithms
Keywords:
Scheduling, Genetic algorithms, Delays, Project management, Optimization technique.Abstract
This review paper presents a comprehensive analysis of the application of metaheuristic algorithms for optimizing construction project scheduling. Various algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA), Grey Wolf Optimizer (GWO), Firefly Algorithm (FA), and hybrid metaheuristic approaches are critically reviewed. The study examines their effectiveness in minimizing project duration, reducing overall cost, balancing resource allocation, and managing project risks under deterministic and uncertain environments. Furthermore, this paper categorizes existing research based on problem formulation, objective functions, constraint handling techniques, and algorithm performance metrics. Comparative insights into single-objective and multi-objective scheduling models are discussed, highlighting strengths, limitations, and computational efficiency of each approach. Current research gaps, challenges in real-world implementation, and future research directions—such as hybrid intelligent models and integration with software are also identified. This review aims to provide researchers and practitioners with a structured understanding of metaheuristic-based scheduling optimization and a foundation for developing advanced decision-support tools in construction project management.
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