Adjustable robust optimization with discrete uncertainty (AC)

Table of content

    Open Methodology

    We have open methodology on our experimental results thanks to rmarkdown.
    DisruptionFLP: Compiled version DisruptionFLP: R markdown source

    Open Source

    Nothing to report here.

    Open Data

    We have public instances and open raw experimental results. The columns in .csv files are explained in the corresponding public rmarkdown (see open methodology) while the instances format is explained in README.md files in the associated .zip.
    DisruptionFLP/instances.zip
    DisruptionFLP/results_ccg_DisruptionFLP.csv DisruptionFLP/results_benders_DisruptionFLP.csv

    Open Access

    We have open access preprint.
    preprint

    Open Educational Resources

    • We also have slides which were presented at EURO2022 and ROADEF2022 (EURO2022.pdf).
    • We have a scientific poster which was presented at the Mixed Integer Programming Workshop 2022, New York (USA) (MIP_poster.pdf).
    MIP_poster.pdf EURO2022.pdf