Decomposition of integrated scheduling and dynamic optimization problems using community detection

Ilias Mitrai, Prodromos Daoutidis

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper the decomposition of the integrated cyclic scheduling and dynamic optimization problem is analyzed using community detection. Different production systems are examined and based on the results of the community detection and the centrality of the constraint unipartite graph, a two level hierarchical structure is identified, with the scheduling problem in the first level and the dynamic optimization subproblems in the second level. The variables that link the two levels are continuous and obtained from the results of the community detection. Finally, Generalized Benders Decomposition is applied on the decomposed optimization problem obtained from the community detection and a solution is obtained faster than solving the problem monolithically.

Original languageEnglish (US)
Pages (from-to)63-74
Number of pages12
JournalJournal of Process Control
Volume90
DOIs
StatePublished - Jun 2020

Bibliographical note

Funding Information:
Financial support from NSF - CBET (grant nos. 1605549 , 1926303 ) is gratefully acknowledged. We would like to thank Qi Zhang (Department of Chemical Engineering and Materials Science, University of Minnesota) for the software access and Wentao Tang for his useful comments.

Funding Information:
Financial support from NSF-CBET (grant nos. 1605549, 1926303) is gratefully acknowledged. We would like to thank Qi Zhang (Department of Chemical Engineering and Materials Science, University of Minnesota) for the software access and Wentao Tang for his useful comments.

Publisher Copyright:
© 2020 Elsevier Ltd

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Community detection
  • Decomposition
  • Integrated scheduling and control

Fingerprint Dive into the research topics of 'Decomposition of integrated scheduling and dynamic optimization problems using community detection'. Together they form a unique fingerprint.

Cite this