Solution Methods for Multi-Objective Robust Combinatorial Optimization

by Lisa Thom
Doctoral thesis
Date of Examination:2018-04-19
Date of issue:2018-05-02
Advisor:Prof. Dr. Anita Schöbel
Referee:Prof. Dr. Anita Schöbel
Referee:Dr. Marie Schmidt
Persistent Address: http://dx.doi.org/10.53846/goediss-6852

 

 

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Abstract

English

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
 
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