<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>202</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>6192</pageFirst>
    <pageLast>6215</pageLast>
    <pageNumber/>
    <edition/>
    <issue>20</issue>
    <volume>54</volume>
    <type>article</type>
    <publisherName>Taylor &amp; Francis</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-01-21</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment</title>
    <abstract language="eng">This paper proposes a scenario-based two-stage stochastic programming model with recourse for master production scheduling under demand uncertainty. We integrate the model into a hierarchical production planning and control system that is common in industrial practice. To reduce the problem of the disaggregation of the master production schedule, we use a relatively low aggregation level (compared to other work on stochastic programming for production planning). Consequently, we must consider many more scenarios to model demand uncertainty. Additionally, we modify standard modelling approaches for stochastic programming because they lead to the occurrence of many infeasible problems due to rolling planning horizons and interdependencies between master production scheduling and successive planning levels. To evaluate the performance of the proposed models, we generate a customer order arrival process, execute production planning in a rolling horizon environment and simulate the realisation of the planning results. In our experiments, the tardiness of customer orders can be nearly eliminated by the use of the proposed stochastic programming model at the cost of increasing inventory levels and using additional capacity.</abstract>
    <parentTitle language="eng">International Journal of Production Research</parentTitle>
    <identifier type="doi">10.1080/00207543.2016.1162917</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Julian Englberger</author>
    <author>Frank Herrmann</author>
    <author>Michael Manitz</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Hierarchische Produktionsplanung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fertigungsprogrammplanung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Nachfrageverhalten</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Stochastische Optimierung</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Master production scheduling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Demand uncertainty</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Two-stage stochastic programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Scenario-based stochastic programming with recourse</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Hierarchical production planning</value>
    </subject>
    <collection role="ddc" number="67">Industrielle Fertigung</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16316">Produktion und Systeme</collection>
  </doc>
  <doc>
    <id>5612</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>16</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>15</volume>
    <type>article</type>
    <publisherName>Bundesvereinigung Logistik (BVL) e.V.</publisherName>
    <publisherPlace>Bremen</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Master production scheduling with scenario-based capacity-load factors in a rolling planning environment</title>
    <abstract language="eng">This paper proposes two stochastic programming models for master production scheduling with capacity-load factor scenarios. In contrast to other work on production planning with load-dependent lead times or dynamic capacity loads, we iteratively build a set of realistic capacity-load factor scenarios by simulating the realization of the master production schedules in a rolling horizon environment. Therefore, we integrate the models into a hierarchical production planning and control system that is common in industrial practice and measure the effective capacity-load factors. With these factors, we resolve the master production scheduling problem. Toa evaluate the performance of the proposed models, we compare the stochastic models with the common approach to reduce the nominally available capacity for master production scheduling. In our experiments, the stochastic models signiﬁcantly reduce the tardiness of production orders caused by capacity bottlenecks.</abstract>
    <parentTitle language="eng">Logistics Research, Special Issue “Supply Chain Analytics in the 2020s”</parentTitle>
    <identifier type="doi">10.23773/2022_12</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Julian Englberger</author>
    <author>Frank Herrmann</author>
    <author>Michael Manitz</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16316">Produktion und Systeme</collection>
  </doc>
</export-example>
