Solution Methods for Multi-Objective Robust Combinatorial Optimization

von Lisa Thom
Dissertation
Datum der mündl. Prüfung:2018-04-19
Erschienen:2018-05-02
Betreuer:Prof. Dr. Anita Schöbel
Gutachter:Prof. Dr. Anita Schöbel
Gutachter:Dr. Marie Schmidt
Zum Verlinken/Zitieren: http://dx.doi.org/10.53846/goediss-6852

 

 

Dateien

Name:Dissertation_Lisa_Thom_oL_weboptimiert.pdf
Size:6.49Mb
Format:PDF
Description:Dissertation

Lizenzbestimmungen:

Zusammenfassung

Englisch

This thesis addresses combinatorial optimization problems with several objectives containing uncertain parameters. A variety of robustness concepts for multi-objective optimization problems have been developed during the last years. This thesis provides methods to find so-called robust efficient solutions with respect to several of these concepts, assuming the uncertain parameters to be given via common uncertainty sets. Several solution approaches are presented, including extensions and combinations of algorithms from both robust and multi-objective optimization, using properties of particular uncertainty sets and robustness concepts. Beyond general combinatorial optimization problems, the shortest path problem is considered in particular. Solution algorithms are implemented and evaluated in numerical experiments.
Keywords: Multi-objective robust optimization; Multi-objective optimization; Robust optimization; Combinatorial optimization
 
Statistik