It is important, in practice, to find robust solutions to optimisation problems. This issue has been the subject of extensive research focusing on single-objective problems. Recently, researchers also acknowledged the need to find robust solutions to multi-objective problems and presented some first results on this topic. In this paper, we deal with bi-objective optimisation problems in which only one objective function is uncertain. The contribution of our paper is three-fold. Firstly, we introduce and analyse four different robustness concepts for bi-objective optimisation problems with one uncertain objective function, and we propose an approach for defining a meaningful robust Pareto front for these types of problems. Secondly, we develop an algorithm for computing robust solutions with respect to these four concepts for the case of discrete optimisation problems. This algorithm works for finite and for polyhedral uncertainty sets using a transformation to a multi-objective (deterministic) optimisation problem and the recently published concept of Pareto robust optimal solutions (Iancu & Trichakis, 2014). Finally, we apply our algorithm to two real-world examples, namely aircraft route guidance and the shipping of hazardous materials, illustrating the four robustness concepts and their solutions in practical applications.

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Keywords Multiple objective programming, OR in transportation, Robust optimisation
Persistent URL,
Series ERIM Top-Core Articles
Journal European Journal of Operational Research
Grant This work was funded by the European Commission 7th Framework Programme; grant id fp7/246647 - Optimization and its Applications in Learning and Industry (OPTALI)
Kuhn, K, Raith, A, Schmidt, M.E, & Schöbel, A. (2016). Bi-objective robust optimisation. European Journal of Operational Research, 252(2), 418–431. doi:10.1016/j.ejor.2016.01.015