By João Paulo Almeida, Jose Fernando Oliveira, Alberto Adrego Pinto

This quantity provides chosen contributions through best researchers within the box of operations examine, originating from the XVI Congress of APDIO. It offers fascinating findings and functions of operations learn tools and strategies in a large choice of difficulties. The contributions handle complicated real-world difficulties, together with stock administration with lateral transshipments, sectors and routes in solid-waste assortment and creation making plans for perishable meals items. It additionally discusses the newest suggestions, making the amount a precious instrument for researchers, scholars and practitioners who desire to find out about present developments. Of specific curiosity are the purposes of nonlinear and mixed-integer programming, information envelopment research, clustering suggestions, hybrid heuristics, provide chain administration and lot sizing, in addition to activity scheduling problems.

This biennial convention, equipped through APDIO, the Portuguese organization of Operational study, held in Bragança, Portugal, in June 2013, awarded an ideal chance to debate the most recent improvement during this box and to slender the distance among educational researchers and practitioners.

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Extra resources for Operational Research: IO 2013 - XVI Congress of APDIO, Bragança, Portugal, June 3-5, 2013

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Process. Syst. (NIPS) 14, 849–856 (2002) 11. : Catastrophic job destruction during the Portuguese economic crisis. J. Macroecon. 39, 444–457 (2014) 12. S. labor markets. Am. Econ. Rev. 91, 187–207 (2001) 13. : The European regional policy and the socioeconomic diversity of European regions: a multivariate analysis. Eur. J. Oper. Res. 187, 600–612 (2008) 14. : Classification of regional labour markets for purpose of labour market policies. Pap. Reg. Sci. 89(4), 859–881 (2009) 15. : Unemployment clusters across Europe’s regions and countries.

Given observation xi , the average dissimilarity to all other points in its own cluster is denoted as ai . xi ; c/. Finally, bi 26 C. Balsa et al. xi ; c/. bi ai / : max fbi ; ai g (5) The average silhouette width is obtained by averaging the si over all observations: 1X sN D si : m iD1 m (6) If the silhouette width of an observation is large it tends to be well clustered. Observations with small silhouette width values tend to be those that are scattered between clusters. The silhouette width si in Eq.

In other words, given a data matrix containing multivariate measurements on a large number of individuals (observations or points), the aim of the cluster analysis is to build up some natural groups (clusters) with homogeneous properties out of heterogeneous large samples [1]. C. pt A. pt E. P. Almeida et al. 1007/978-3-319-20328-7_2 19 20 C. Balsa et al. Groups are based on similarities. The similarity depends on the distance between data points and a reduced distance indicates that they are more similar.

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