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  <doc>
    <id>2481</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>287</pageFirst>
    <pageLast>293</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>European Council for Modelling and Simulation</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Influence of Company Sizes in adapted Master Production Scheduling for Improving Human Working Conditions</title>
    <abstract language="eng">Sustainability is an important topic in production plan-ning and control. This article contributes in particular the to further research on the social dimension. It pre-sents a linear optimisation model for Master Produc-tion Scheduling in order to improve human working conditions. Existing approaches have already identified a considerable potential for improvements. Further-more, this article analyses the influence of the compa-ny size on workload and costs using an application with a high proportion of manual activities. It is demonstrat-ed that human working conditions can be improved independently from the company size without increas-ing costs. In addition, smaller companies tend to have a higher exhaustion and the workload affects the total costs more in smaller companies. Therefore, smaller companies might benefit more from an improvement in human working conditions.</abstract>
    <parentTitle language="deu">Proceedings of the 34th International ECMS Conference on Modelling and Simulation, ECMS 2020</parentTitle>
    <identifier type="isbn">978-3-937436-68-5</identifier>
    <identifier type="doi">10.7148/2020-0287</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-24817</identifier>
    <enrichment key="opus.import.date">2022-01-21T12:31:59+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Marco Trost</author>
    <author>Thorsten Claus</author>
    <author>Frank Herrmann</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>company size</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cost reduction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>exhaustion</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>linear optimisation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>master production scheduling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>workload</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16316">Produktion und Systeme</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/2481/0287_simo_ecms2020_0018.pdf</file>
  </doc>
</export-example>
