Produktionspraxis. Maschinenbelegung optimieren mittels genetischem Algorithmus
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Produktionspraxis. Maschinenbelegung optimieren mittels genetischem Algorithmus

Produktionspraxis. Maschinenbelegung optimieren mittels genetischem Algorithmus


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About the Book

Diplomarbeit aus dem Jahr 2004 im Fachbereich BWL - Beschaffung, Produktion, Logistik, Note: 1,0, Universität Leipzig (Institut für Mathematik), Sprache: Deutsch, Abstract: Ziel dieser Arbeit ist es, für ein konkretes Beispiel aus der Produktionspraxis einen speziellen genetischen Algorithmus zu entwickeln, welcher die Maschinenbelegung bestmöglich plant. Markt- und Kundenorientierung stehen im Mittelpunkt moderner Unternehmensstrategien. Um die Wünsche nach Sachgütern und Dienstleistungen mit niedrigen Preisen, hoher Qualitäat und kurzen Lieferzeiten erfüllen zu können und dennoch trotz ständig sinkender Gewinnspannen profitabel zu arbeiten, sind die Unternehmen dazu angehalten, Möglichkeiten der Kostensenkung zu finden. Insbesondere im Produktionsbereich lassen sich durch einen verbesserten Ressourcenverbrauch sowie eine Verringerung der Durchlaufzeiten erhebliche Kosten einsparen, ohne dabei Abstriche bei der Qualität zu machen. Dazu ist eine rationelle Produktionsplanung und -steuerung notwendig. Die Maschinenbelegungsplanung ist dabei eines der zentralen Probleme. Die Aufgabe besteht darin, eine Zuordnung von Aufträgen zu Maschinen in einer gewissen Reihenfolge zu finden, so dass ein bestimmtes Ziel bestmöglich erfüllt wird. Seit den fünfziger Jahren werden die unterschiedlichsten Verfahren zur Lösung von Maschinenbelegungsproblemen vorgeschlagen. Zu den bekanntesten zählen Branch & Bound Verfahren sowie prioritätsregelbasierte Verfahren. Während Ersteres zwar eine Optimallösung garantiert, dessen Einsatz jedoch aufgrund immenser Rechenzeit bei größeren Problemen unmöglich ist, liefert das zweite Verfahren meist nur unbefriedigende Ergebnisse. Neuere Verfahren, wie Simulated Annealing, Tabu Search oder genetische Algorithmen liefern hingegen bei moderater Rechenzeit gute Lösungsqualitäten. Insbesondere genetische Algorithmen scheinen für die Lösung komplexer Optimierungsprobleme besonders gut geeignet zu sein. Zunächst wird die Maschinenbelegu


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Product Details
  • ISBN-13: 9783638721189
  • Publisher: Grin Verlag
  • Publisher Imprint: Grin Verlag
  • Height: 210 mm
  • No of Pages: 108
  • Series Title: German
  • Weight: 199 gr
  • ISBN-10: 3638721183
  • Publisher Date: 05 Aug 2007
  • Binding: Paperback
  • Language: German
  • Returnable: N
  • Spine Width: 7 mm
  • Width: 148 mm


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