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The growth of project management as international discipline was encouraged by the increasing of projects complexity all over the world. Despite of the effort made by the principal standards of project management, the number of failed and renegotiated projects still very high. Decision making process is one of the root causes of projects failure. During project development, decisions are made according to the evaluations performed in projects cut. In each cut, experts make, sequentially, diagnosis, decision, and prediction processes. This paper aims at creating a new model that integrate the diagnosis, decision, and prediction processes in one platform for decision making during projects development. The new model is based on Neutrosophic Cognitive Maps (NCMs) with a triangular neutrosophic number in map's connections. The suggested model deals efficiently with the uncertainty and vagueness, since it considers the truth, indeterminacy, and falsity degrees. Finally, the proposed model has been compared, in terms of efficacy, with a traditional FCM-based model for decision making in projects. [ABSTRACT FROM AUTHOR]

This paper aims at proposing a Constraint-based Learning Estimation of Distribution Algorithm (EDA) to solve the Multi-Mode Resource Constrained Project Scheduling Problem (MMRCPSP). This problem is considered a combinatorial optimization problem and consists of a set of tasks to be carried out in an ordered way, limited availability of resources, and an execution mode for each task. Generally, traditional EDAs handle the constraints of the problem using strategies such as penalty, Pareto dominance, or others, without taking into account these constraints during the learning process. These traditional approaches usually cause the generation of infeasible solutions in each iteration. In this paper, an approach based on the constraints handling during the learning process of the probabilistic model is proposed. To validate the performance of the algorithms, the new approach in two scenarios is compared. First, in the solution of a set of classical optimization problems, where different EDAs are compared in terms of efficiency and efficacy. The second group of experiments is performed on a set of instances of the PSPLIB database. In this case, the proposed algorithm is compared with a traditional EDA and with other results reported in the bibliography. The experimental results demonstrate that the proposal outperforms both the results of traditional EDA and other EDA reported in the literature. [ABSTRACT FROM AUTHOR]