multi-objectives particle swarm algorithm

The Multi-Mode Resource Constrains Project Scheduling Problem (MRCPSP) tries to find the best sequence of activities in a manner that involves more than one type of operating mode and in the presence of resource constraints, project’s precedence constraints must be satisfied. In each execution mode, the amount of resources and execution time are specified and different. In The Preemptive multi-mode Resource Constraints Project Scheduling Problem (P-MRCPSP), each operating mode activity can be interrupted and restarted at any time without any extra cost. In this paper, minimizing the completion time along with maximizing the current net value of the project in the P-MRCPSP are considered. After solving the problem by using Epsilon limits method, according to NP-hard problem and multi-objective model, multi-objective particle swarm optimization (MOPSO) has been developed to achieve optimum scheduling. In order to evaluate the proposed method’s efficiency, results have been compared to non-dominance genetic algorithm sorting (NSGAII) based on designed indicators. The Taguchi method has been used in experimental design, to adjust these two algorithms’ parameters. The results of the model solution show the strength of MOPSO algorithm.