Generation of linear-based surrogate models from non-linear functional relationships for use in scheduling formulation

Andreas Obermeier*, Nikolaus Vollmer, Christoph Windmeier, Erik Esche, Jens-Uwe Repke

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Often functional relationships are well known, but they are too complex to be used efficiently in optimization problems like scheduling formulations. Hence the functions are often replaced by data-based surrogate models. Especially, linear models are often used, since they are easier to solve than non-linear ones. The use of piecewise linear surrogate models allows for an improved consideration of nonlinearities. Although, the number of linear elements must be kept small in order not to lose the advantages of a linear-based formulation. In this work, two approaches for generating piecewise linear surrogate models are proposed, whereby the basic idea of both approaches is the determination of a reduced set of data points that provides an appropriate approximation of the original data via multi-dimensional linear interpolation. The approaches differ in their concepts: One is a numerical algorithm, the other an optimization-based technique. In this contribution, these approaches are described and subsequently compared.
Original languageEnglish
Article number107203
JournalComputers and Chemical Engineering
Volume146
Number of pages18
ISSN0098-1354
DOIs
Publication statusPublished - 2021

Keywords

  • Data-based surrogate models
  • Piecewise linear
  • Data reduction

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