Proposing a Hybrid Model for Resource Leveling by Multi-Criteria Differential Evolution Algorithm under Uncertainty

The importance of resource leveling cannot be ignored throughout a project, especially in cases where a trivial fluctuation in using resources will lead into high financial risks or costs. Traditional approaches are usually insufficient in solving the problems of multi-objective constraint resource leveling. In this paper, we propose an optimized technique based on Differential Evolution (DE) algorithm and ELECTERE method in order to overcome the drawbacks of the traditional resource leveling algorithms. Furthermore, obviating the challenges of normalizing and weighting objective functions, this approach takes the uncertainty and ambiguity of the objective functions into account through application of pseudo-criterion. The structure of proposed model is designed based on real situations. Results obtained from the numerical example verify the efficiency of the proposed model in solving the problems of resource leveling, and in comparison with traditional models; they confirm the better performance of this model to overcome single and multi-objective problems.