M. Leal Palazón, E. Conde Sánchez, J. Puerto Albandoz

Combinatorial optimization problems have been extensively studied in specialized literature since the mid-twentieth century. However, in recent decades, there has been a paradigm shift to the treatment of ever more realistic problems, which include sources of randomness and uncertainty in the data, multiple optimization criteria and multiple levels of decision. The thesis we present concerns the development of such concepts. Our objective was to study optimization models that incorporate uncertainty elements in the parameters defining the model, as well as the development of optimization models integrating multiple decision levels. In order to consider problems under uncertainty, we used Minmax Regret models from Robust Optimization; whereas the multiplicity and hierarchy in the decision levels were addressed using Bilevel Optimization. The models we propose have applications in areas such as Design, Transportation, Location, Planning or Portfolio Selection.

Keywords: Combinatorial Optimization, Bilevel Optimization, Robust Optimization


GT3-2 Multicritery Decision. Thesis Award
September 3, 2019  4:50 PM
I2L7. Georgina Blanes building

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