Abstract
The problem of selecting a portfolio of research and development projects from a set of proposed projects subject to resource constraints is formulated as a multiobjective optimization problem. Three categories of objectives are identified, namely quantitative, qualitative and balance/distributional. The first two are linear in the binary decision variables, but the third involves nonlinear measures of discrepancy between desired and actual distributions of activity amongst defined categories. The result is a nonlinear combinatorial multiobjective optimization problem. A form of reference point approach is motivated and developed as a solution method, and a special purpose genetic algorithm is implemented for obtaining the solution numerically. Extensions to interactive approaches for exploration of the efficient frontier are presented and discussed.
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