Genetic Algorithm for Personnel Assignment Problem with Multiple Objectives


This thesis introduces a multi-objective variation of the personnel assignment problem, by including additional hierarchical and team constraints, which put restrictions on possible matchings of the bipartite graph. Besides maximization of summation of weights that are assigned to the edges of the graph, these additional constraints are also treated as objectives which are subject to minimization. In this work, different genetic algorithm approaches to multi-objective optimization are considered to solve the problem. Weighted Sum - a classical approach, VEGA - a non-elitist multi-objective evolutionary algorithm, and SPEA - a popular elitist multi-objective evolutionary algorithm, are considered as means of solution to the problem, and their performances are compared with respect to a number of multi- objective optimization criteria.