Management Science
A multiple criteria decision model for information system project selection
Computers and Operations Research
Decision support systems in the twenty-first century
Decision support systems in the twenty-first century
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Solving a comprehensive model for multiobjective project portfolio selection
Computers and Operations Research
Developing a projects evaluation system based on multiple attribute value theory
Computers and Operations Research
Large-scale public R&D portfolio selection by maximizing a biobjective impact measure
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Information Sciences: an International Journal
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This paper proposes the application of multi-criteria analysis to the problem of allocating public funds to competing programs, projects, or policies, with a subjective approach applied to define the concept of highest portfolio social return. This portfolio corresponds to an attainable non-strictly outranked state of the social object under consideration. Its existence requires a decision-maker (DM) to establish a relational preference system of minimal consistency. As the number of feasible portfolios increases exponentially, the DM's asymmetric preference relation should be computable to perform an exploration of the portfolio space. The complexity of many real situations requires evolutionary algorithms, but, in presence of many objectives, evolutionary algorithms are inefficient. We overcome this problem by using the extended non-outranked sorting genetic algorithm (NOSGA-II), which handles multi-criteria preferences through a robust model based on a binary fuzzy outranking relation expressing the truth value of the predicate ''portfolio x is at least as good as portfolio y''. The DM is assumed to be capable of assigning the parameters for constructing the outranking relation. In case of collective decision-making, we first assume that the possible differing values of the group members are not strongly conflicting, so that a consensus can be achieved on the model's parameters. Otherwise, we propose a method in which each member of the heterogeneous group gets his/her own best portfolio. These individual solutions are then aggregated in a group's best acceptable portfolio, which maximizes a measure of group satisfaction and minimizes regret. The proposal is examined through two real size problems, in which good solutions are reached; the first example is useful to illustrate the case of social-action program selection; the second illustrates the case of basic research project portfolios.