Developing composed simulation and optimization models using actual supply-demand network datasets

Soroosh Gholami, Hessam Sarjoughian, Gary W. Godding, Daniel R. Peters, Victor Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Large, fine-grain data collected from an actual semiconductor supply-demand system can help automated generation of its integrated simulation and optimization models. We describe how instances of Parallel DEVS and Linear Programming (LP) models can be semi-automatically generated from industry-scale relational databases. Despite requiring the atomic simulation models and the objective functions/constraints in the LP model to be available, it is advantageous to generate system-wide supply-demand models from actual data. Since the network changes over time, it is important for the data contained in the LP model to be automatically updated at execution intervals. Furthermore, as changes occur in the models, the interactions in the Knowledge Interchange Broker (KIB) model, which composes simulation and optimization models, are adjusted at run-time.

Original languageEnglish (US)
Title of host publicationProceedings of the 2014 Winter Simulation Conference, WSC 2014
EditorsAndreas Tolk, Levent Yilmaz, Saikou Y. Diallo, Ilya O. Ryzhov
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2510-2521
Number of pages12
ISBN (Electronic)9781479974863
DOIs
StatePublished - Jan 23 2015
Event2014 Winter Simulation Conference, WSC 2014 - Savannah, United States
Duration: Dec 7 2014Dec 10 2014

Publication series

NameProceedings - Winter Simulation Conference
Volume2015-January
ISSN (Print)0891-7736

Other

Other2014 Winter Simulation Conference, WSC 2014
CountryUnited States
CitySavannah
Period12/7/1412/10/14

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Developing composed simulation and optimization models using actual supply-demand network datasets'. Together they form a unique fingerprint.

Cite this