Browsing by Author "Gorissen, B. L."

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    ArticlePublicationOpen Access
    A practical guide to robust optimization
    (Elsevier, 2015-06) Gorissen, B. L.; Yanıkoğlu, İhsan; Hertog, D. den; Industrial Engineering; YANIKOĞLU, Ihsan
    Robust optimization is a young and active research field that has been mainly developed in the last 15 years. Robust optimization is very useful for practice, since it is tailored to the information at hand, and it leads to computationally tractable formulations. It is therefore remarkable that real-life applications of robust optimization are still lagging behind; there is much more potential for real-life applications than has been exploited hitherto. The aim of this paper is to help practitioners to understand robust optimization and to successfully apply it in practice. We provide a brief introduction to robust optimization, and also describe important do׳s and don׳ts for using it in practice. We use many small examples to illustrate our discussions.
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    A survey of adjustable robust optimization
    (Elsevier, 2019-09) Yanıkoğlu, İhsan; Gorissen, B. L.; Hertog, D. den; Industrial Engineering; YANIKOĞLU, Ihsan
    Static robust optimization (RO) is a methodology to solve mathematical optimization problems with uncertain data. The objective of static RO is to find solutions that are immune to all perturbations of the data in a so-called uncertainty set. RO is popular because it is a computationally tractable methodology and has a wide range of applications in practice. Adjustable robust optimization (ARO), on the other hand, is a branch of RO where some of the decision variables can be adjusted after some portion of the uncertain data reveals itself. ARO generally yields a better objective function value than that in static robust optimization because it gives rise to more flexible adjustable (or wait-and-see) decisions. Additionally, ARO also has many real life applications and is a computationally tractable methodology for many parameterized adjustable decision variables and uncertainty sets. This paper surveys the state-of-the-art literature on applications and theoretical/methodological aspects of ARO. Moreover, it provides a tutorial and a road map to guide researchers and practitioners on how to apply ARO methods, as well as, the advantages and limitations of the associated methods.