Project portfolio selection is addressed here as a multi-objective optimization problem. This work introduces an interval-based method that takes into consideration imperfect knowledge of the contribution of projects to a portfolio, the project requirements, available resources and preference parameters in the model. The multi-objective optimization problem is solved using an evolutionary algorithm that is adapted to handle intervals. To direct the search toward the region of interest of the Pareto frontier, the preferences of the decision maker (DM) are incorporated using an interval-based outranking approach. This allows to address problems with many objective functions; intransitive preferences and incomparability situations can also be handled using this approach. In terms of analyzing robustness, the DM can obtain different solutions according to his/her level of conservatism. The effectiveness of this proposal was tested both on an example from the related literature and another example of a public project portfolio with nine objective functions and large number of applicant projects.

Project portfolio selection is one of the most important problems faced by any organization. The decision process involves multiple conflicting criteria, and has been commonly addressed by implementing a two-phase procedure. The first step identifies the efficient solution set; the second step supports the decision maker in selecting only one portfolio solution from the efficient set. However, several recent studies show the advantages gained by optimizing towards a region of interest (according to the decision maker's preferences) instead of approximating the complete Pareto set. However, these works have not faced synergism and its variants, such as cannibalization and redundancy. In this paper we introduce a new approach called Non-Outranked Ant Colony Optimization, which optimizes interdependent project portfolios with a priori articulation of decision-maker preferences based on an outranking model. Several experimental tests show the advantages of our proposal over the two-phase approach, providing reasonable evidence of its potential for solving real-world high-scale problems with many objectives.

Mitchell-Riley syndrome/Martinez-Frias syndrome (MRS/MFS) is a rare, autosomal recessive disorder with multisystem involvement and poor prognosis. Most reported cases have been associated with homozygous or compound heterozygous mutations in the RFX6 gene, a transcriptional regulatory factor for pancreatic morphogenesis. Given the limited number of reported cases, the syndrome may be under-recognized. When the particular phenotype of MFS includes a mutation on the RFX6 gene and neonatal diabetes, it has been called Mitchell-Riley syndrome. Because of this, we propose that MFS/MRS is a symptom continuum or an RFX6 malformation complex. We report an infant with all of the key clinical features of MRS/MFS without a definable mutation in RFX6 gene, supporting the consideration of these features as a symptom complex, and raising the question of genetic heterogeneity.

No abstract

No abstract

No abstract

No abstract

Selecting project portfolios Decision-Maker usually starts with limited information about projects and portfolios. One of the challenges involved in analyzing, searching and selecting the best portfolio is having a method to evaluate the impact of every project and portfolio in order to compare them.This paper develops a model for composing publicoriented project portfolios. Information concerning the quality of the projects is in the form of a project-ranking, which can be obtained by the application of a proper multi-criteria method; however the ranking does not assume an appropriate evaluation. A best portfolio is primarily found through a multi-objective optimization that regards the impact indicators that reflect the quality of the projects in the portfolio and competent portfolios' cardinalities. Overall good solutions are obtained by developing an evolutionary method, which is found to perform well in some test examples.