Hybrid meta-heuristic methods for the multi-resource leveling problem with activity splitting

In this paper, we consider the multi-resource leveling problem with the objective of minimizing the total costs resulting from the variation of the resource utilization and the cost of splitting non-critical activities. We propose hybrid meta-heuristic methods which combine particle swarm optimization (PSO) and simulated annealing (SA) search procedures to generate near-optimal project schedules in less computational time than the exact optimization procedure. The PSO algorithms are based on different update mechanisms for the particlesʹ velocities and positions. The cost and computation time performances of the combined PSO/SA search procedures are evaluated using a set of benchmark problems. Based on the results of the computational experiments, we suggest one of the proposed heuristic procedures to be used for solving the multi-resource leveling problem with activity splitting.