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dc.contributor.author
Ehrenstein, Michael
dc.contributor.author
Wang, Chi-Hsiang
dc.contributor.author
Guillén Gosálbez, Gonzalo
dc.contributor.editor
Kiss, Anton A.
dc.contributor.editor
Zondervan, Edwin
dc.contributor.editor
Lakerveld, Richard
dc.contributor.editor
Özkan, Leyla
dc.date.accessioned
2020-07-22T12:55:49Z
dc.date.available
2020-01-30T15:08:04Z
dc.date.available
2020-02-12T08:44:37Z
dc.date.available
2020-07-22T12:55:49Z
dc.date.issued
2019
dc.identifier.isbn
978-0-12-819939-8
en_US
dc.identifier.isbn
978-0-12-819939-8
en_US
dc.identifier.issn
1570-7946
dc.identifier.other
10.1016/b978-0-12-818634-3.50140-5
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/395935
dc.description.abstract
Chemical supply chains are a crucial component in the ongoing supply of large population centres. Unfortunately, episodes of extreme weather have, in recent years, revealed vulnerabilities in global supply networks to high-impact events. With a possible increase in both frequency and intensity of these events due to climate change, supply chains are at risk of disruption now more than ever, with potentially dire economic, societal, and environmental consequences. Acknowledging that the direct application of stochastic programming can quickly lead to very large CPU times, we propose an algorithm that combines the sample average approximation method with a selection heuristic for extreme event scenarios. Our method allows to analyse the tradeoff between economic performance and disruption risk, identifying supply chain configurations which are more resilient against extreme events. We demonstrate the effectiveness of this methodology in multiple case studies, showing how it identifies near optimal solutions in short CPU times.
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
Supply chains
en_US
dc.subject
Optimization
en_US
dc.subject
Stochastic programming
en_US
dc.subject
Climate change
en_US
dc.subject
Extreme events
en_US
dc.title
Planning of Supply Chains Threatened by Extreme Events: Novel Heuristic and Application to Industry Case Studies
en_US
dc.type
Conference Paper
dc.date.published
2019-07-25
ethz.book.title
29th European Symposium on Computer Aided Process Engineering
en_US
ethz.journal.title
Computer Aided Chemical Engineering
ethz.journal.volume
46
en_US
ethz.journal.issue
Part A
en_US
ethz.pages.start
835
en_US
ethz.pages.end
840
en_US
ethz.event
29th European Symposium on Computer-Aided Process Engineering (ESCAPE-29)
en_US
ethz.event.location
Eindhoven, The Netherlands
en_US
ethz.event.date
June 16-19, 2019
en_US
ethz.publication.place
Amsterdam
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02516 - Inst. f. Chemie- und Bioingenieurwiss. / Inst. Chemical and Bioengineering::09655 - Guillén Gosálbez, Gonzalo / Guillén Gosálbez, Gonzalo
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02516 - Inst. f. Chemie- und Bioingenieurwiss. / Inst. Chemical and Bioengineering::09655 - Guillén Gosálbez, Gonzalo / Guillén Gosálbez, Gonzalo
en_US
ethz.date.deposited
2020-01-30T15:08:11Z
ethz.source
FORM
ethz.eth
no
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2020-02-12T08:44:52Z
ethz.rosetta.lastUpdated
2022-03-29T02:41:56Z
ethz.rosetta.versionExported
true
ethz.COinS
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