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Conference Prof. José Manuel Giménez-Gómez


06/04/2017
12:30

Título: The CO2 conflicting-claims problem

Ponentes: José Manuel Giménez -Gómez (Universitat Rovira i Virgili)

Fecha: Jueves 6 de abril de 2017 a las 12:30 horas

Lugar: Sala de seminarios, Edificio Torretamarit, Universidad Miguel Hernández (Campus de Elche)

Resumen:

An effective climate agreement is urgently required, yet conflict between parties prevails over cooperation. Thanks to advances in science it is now possible to quantify the global carbon budget, the amount of available cumulative CO2 emissions before crossing the 2oC threshold (Meinshausen et al., 2009). Countries carbon claims, however, exceed this. Historically such situations have been tackled with bankruptcy division rules. We argue that framing climate negotiations as a classical conflicting claims problem (O’Neill, 1982) may provide for an effective climate policy. We analyze the allocation of the global carbon budget among parties claiming the maximum emissions rights possible. Based on the selection of some desirable principles, we propose an efficient and sustainable allocation of the available carbon budget for the period 2000 to 2050 taking into account different risk scenarios.


31 marzo 2017 Comments off

Conference Prof. Talbi


11/04/2017
12:30

Tittle: Combining metaheuristics with mathematical programming and machine learning

Speaker:  El-Ghazali Talbi  (University of Lille 1)

Date: 11/04/2017 12:30h

Location: Sala de Seminarios, Edificio Torretamarit

Abstract:
During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obtained by hybrid optimization algorithms. Combinations of optimization tools such as metaheuristics, mathematical programming, constraint programming and machine learning, have provided very efficient optimization algorithms. Four different types of combinations are considered in this talk: (i) Combining metaheuristics with complementary metaheuristics. (ii) Combining metaheuristics with exact methods from mathematical programming approaches which are mostly used in the operations research community. (iii) Combining metaheuristics with machine learning and data mining techniques.


9 marzo 2017 Comments off