This paper presents a multiobjective methodology for optimal zoning design (OZ), based on the grouping of geographic data with characteristics of territorial aggregation. The two objectives considered are the minimization of the geometric compactness on the geographical location of the data and the homogeneity of any of the descriptive variables. Since this problem is NP hard [1], our proposal provides an approximate solution taking into account properties of partitioning algorithms and design restrictions for territorial space. Approximate solutions are generated through the set of optimum values (Maxima) and the corresponding minimals (dual Minima) [2] of the bi-objective function using Variable Neighborhood Search (VNS) [3] and the Pareto order defined over this set of values. The results obtained by our proposed approach constitute good solutions and are generated in a reasonably low computational time.

The zones design occurs when small areas or basic geographic units (BGU) must be grouped into acceptable zones under the requirements imposed by the case study. These requirements can be the generation of intra-connected and/or compact zones or with the same amount of habitants, clients, communication means, public services, etc. In this second point to design a territory, the selection and adaptation of a clustering method capable of generating compact groups while keeping balance in the number of objects that form each group is required.The classic partitioning stands out (also known as classification by partition among the clustering or classification methods [1]). Its properties are very useful to create compact groups.An interesting property of the classification by partitions resides in its capability to group different kinds of data. When working with geographical data, such as the BGU, the partitioning around medoids algorithms have given satisfactory results when the instances are small and only the objective of distances minimization is optimized. In the presence of additional restrictions, the K-medoids algorithms, present weaknesses in regard to the optimality and feasibility of the solutions.In this work we expose 2 variants of partitioning around medoids for geographical data with balance restrictions over the number of objects within each group keeping the optimality and feasibility of the solution. The first algorithm considers the ideas of k-meoids and extends it with a recursive constructive function to find balanced solutions. The second algorithm searches for solutions taking into account a balance between compactness and the cardinality of the groups (multiobjective). Different tests are presented for different numbers of groups and they are compared with some results obtained with Lagrange Relaxation. This kind of grouping is needed to solve aggregation for Territorial Design problems

ResumenDesde principios del siglo XX, cuando comenzaron los manifiestos por los derechos de las mujeres, el acceso de las mujeres a diversos medios ha sido periódico y creciente, pero no considerable. Actualmente, observando solo el área de ciencia e ingeniería, todavía hay una baja presencia de liderazgo. Esta ausencia se acentúa más en la investigación científica con un porcentaje inferior al 10%. En este trabajo se presenta una revisión de la literatura sobre la presencia de las mujeres y su participación en la academia e investigación. A partir de una metodología cualitativa basada en una técnica documental se realiza el