In real-world applications of optimization, optimal solutions are often of limited value, because disturbances of or changes to input data may diminish the quality of an optimal solution or even render it infeasible. One way to deal with uncertain input data is robust optimization, the aim of...

In this paper we present new concepts of efficiency for uncertain multi-objective optimization problems. We analyze the connection between the concept of minmax robust efficiency presented by Ehrgott et al. (Eur J Oper Res, <CitationRef CitationID="CR14">2014</CitationRef>, doi:<ExternalRef> <RefSource>10.1016/j.ejor.2014.03.013</RefSource> <RefTarget Address="10.1016/j.ejor.2014.03.013" TargetType="DOI"/> </ExternalRef>) and the upper set less order...</refsource></externalref></citationref>

This paper finds a high correlation between the open to close returns for U.S. stocks in the previous trading day and the Japanese equity market performance in the current period. In contrast, the Japanese market has only a small impact on the U.S. return in the current period. High correlations...

The Indian Ocean region is arguably one of the world`s least understood, yet its importance in geopolitics and international relations is greater than ever before. This book explores the concepts of regime theory, regional orders and ocean governance to critically assess the effectiveness of...

Minmax regret optimization aims at finding robust solutions that perform best in the worst-case, compared to the respective optimum objective value in each scenario. Even for simple uncertainty sets like boxes, most polynomially solvable optimization problems have strongly NP-complete minmax...

We consider the problem of evacuating an urban area. Several planning aspects need to be considered in such a scenario, which are usually considered separately. We propose a macroscopic multi-criteria optimization model that includes several such questions simultaneously, and develop a genetic...