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  <doc>
    <id>7890</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferencepresentation</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-01-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Aggregate Production Planning under Risk of Disruption</title>
    <abstract language="eng">In recent years, large scale disruptions to global supply chains, like the Covid pandemic, a ship blocking the Suez canal or sanctions against Russia, have caused production to slow down or even to stand still, causing shortages and massive losses for affected businesses. Even if only specific companies were originally affected, shortages and delays rippled along the supply network.&#13;
The established approach to deal with disruptions is to utilize safety stock and capacity to compensate for fluctuations in uncertain quantities like customer demand. This approach is tried and tested for small fluctuations. To address larger disruptions, like the above given, very high safety stock and capacity would be needed, which would lead to unnecessarily high costs.&#13;
Resilience has often been viewed as an expensive capability that drives costs. Recent studies however advocate for the development of lean resilience concepts, creating new capabilities, which enable resilience and can deal with large fluctuations, reimagining resilience from the perspectives of efficiency and value creation.&#13;
This contribution identifies gaps in current research and establishes structural deficits of approaches discussed in the literature regarding supply chains. Firstly, the need for rigorous, quantitative definitions of resilience and relevant disruptions is justified.&#13;
Then, a stochastic model for aggregate production planning that includes capabilities to compensate for such large fluctuations along several dimensions is proposed. Lastly, this model is then applied to a case study pertaining to a realistic supply chain under the risk of large scale disruptions and the results of this approach are evaluated.</abstract>
    <parentTitle language="eng">OR 2024, International Conference on Operations Research 2024, September 3-6, 2024, Munich</parentTitle>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Frank Herrmann</author>
    <author>Maximilian Schoen</author>
    <author>Thorsten Claus</author>
    <author>Chenghao Dai</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Supply Chain Resiliance</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Aggregate Production Planning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Supply Chain Disruption</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16316">Produktion und Systeme</collection>
  </doc>
  <doc>
    <id>7882</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>345</pageFirst>
    <pageLast>351</pageLast>
    <pageNumber>7</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>ECMS</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Multi-Site Aggregate Production Planning With Resilience Consideration</title>
    <abstract language="eng">The recent years have shown a high frequency of disruptions like natural disasters or manmade disruptions, leading, for example, to transport routes or production facilities being unavailable for extended periods. Many manufacturing companies, which nowadays operate in a global production network, are heavily exposed to these disruptions. This results in both significant costs and substantial exceeding of promised deadlines. If it concerns the first company in a supply chain, it causes correspondingly significant delays in promised deadlines for subsequent companies in the supply chain, including a correspondingly significant increase in costs. Both are exemplified in this paper through a case study. This case study demonstrates that through resilience, both implications can be significantly reduced.</abstract>
    <parentTitle language="eng">Proceedings of the 38th ECMS International Conference on Modeling and Simulation (ECMS 2024): June 4th - June 7th, 2024, Cracow, Poland</parentTitle>
    <identifier type="isbn">978-3-937436-84-5</identifier>
    <identifier type="issn">2522-2422</identifier>
    <identifier type="doi">10.7148/2024-0345</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Chenghao Dai</author>
    <author>Maximilian Schön</author>
    <author>Thorsten Claus</author>
    <author>Frank Herrmann</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Production Planning and Control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Aggregate Production Planning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Linear Programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multi-Site Production Planning and Control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Supply Chain Resilience</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16316">Produktion und Systeme</collection>
  </doc>
</export-example>
