@article{schwarz_improving_2019,
  title = {Improving the computational efficiency of stochastic programs using automated algorithm configuration: an application to decentralized energy systems},
  issn = {1572-9338},
  url = {https://doi.org/10.1007/s10479-018-3122-6},
  doi = {10.1007/s10479-018-3122-6},
  abstract = {The optimization of decentralized energy systems is an important practical problem that can be modeled using stochastic programs and solved via their large-scale, deterministic-equivalent formulations. Unfortunately, using this approach, even when leveraging a high degree of parallelism on large high-performance computing systems, finding close-to-optimal solutions still requires substantial computational effort. In this work, we present a procedure to reduce this computational effort substantially, using a state-of-the-art automated algorithm configuration method. We apply this procedure to a well-known example of a residential quarter with photovoltaic systems and storage units, modeled as a two-stage stochastic mixed-integer linear program. We demonstrate that the computing time and costs can be substantially reduced by up to 50\% by use of our procedure. Our methodology can be applied to other, similarly-modeled energy systems.},
  journal = {Annals of Operations Research},
  author = {Schwarz, Hannes and Kotthoff, Lars and Hoos, Holger and Fichtner, Wolf and Bertsch, Valentin},
  month = jan,
  year = {2019},
  month_numeric = {1}
}