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Supplementary Materials: A comprehensive approach for an approximative integration of nonlinear-bivariate functions in mixed-integer linear programming (MILP) models

dc.contributor.author Roth, Maximilian
dc.date.accessioned 2022-04-08T14:35:08Z
dc.date.available 2022-04-08T14:35:08Z
dc.date.created 2022
dc.date.issued 2022-04-08
dc.description The architecture consists of 3 modules: Master Module, Approximation Module and MILP Module. In the master module, the nonlinear bivariate functions are defined and the input data for the optimization module is managed. It should be mentioned that univariate functions can also be defined in addition to the bivariate functions, but this should not be considered further, since the data flows are analogously. The functions are passed from the master module to the approximation module, where the functions are transformed from a continuous surface to a piecewise-constant-linear mesh. In a next step, the meshes are passed back to the master module and stored in the input data. The complete input data – one-dimensional and multi-dimensional parameters – are transferred to the optimization program. In the MILP, the constraints for the meshes are stored, which are independent of the specific, initially defined function. Note: For running the code, an application scenario needs to be integrated. de_DE
dc.identifier.uri https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3430
dc.identifier.uri https://doi.org/10.48328/tudatalib-830
dc.language.iso en de_DE
dc.rights.licenseODC-BY-1.0 (https://opendatacommons.org/licenses/by/1.0/)
dc.subject MILP de_DE
dc.subject Bivariate de_DE
dc.subject Non-linear de_DE
dc.subject Approximation de_DE
dc.subject Big-M de_DE
dc.subject.classification 3.31-01
dc.subject.classification 4.43-01
dc.subject.ddc 004
dc.subject.ddc 510
dc.title Supplementary Materials: A comprehensive approach for an approximative integration of nonlinear-bivariate functions in mixed-integer linear programming (MILP) models de_DE
dc.type Software de_DE
dcterms.accessRights openAccess
person.identifier.orcid 0000-0003-1353-9446
tuda.history.classification Version=2020-2024;312-01 Mathematik
tuda.history.classification Version=2020-2024;409-01 Theoretische Informatik
tuda.unit TUDa

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Approximation.py3.87 KB Download
Master.py2.87 KB Download
MILP_MeshConstraints.py8.44 KB Download