Meta-heuristic approaches for solving Resource Constrained Project Scheduling Problem: A Comparative study

Meta-heuristics for solving Combinatorial Optimization Problems (COP) is a rapidly growing field of research. In this paper we have considered the Resource Constrained Project Scheduling Problem as a COP. The problem is highly constrained and is a common problem for many construction projects. The problem is NP-hard and deterministic methods are slow in execution. In our work, we use Simulated Annealing, Tabu Search, Genetic Algorithm, Particle Swarm Optimization and Elite Particle Swarm Optimization with Mutation for solving benchmark instances of this problem and compare their performances with each other. The results show that Simulated Annealing outperforms other methods in getting optimal results with minimum number of fluctuations.