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Didaktischer Zugang zur Theorie und Praxis moderner Softwarebibliotheken (Frameworks) für die Unternehmensforschung (OR)

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  • Mumm, Harald

Abstract

[Einleitung] Seit mehreren Jahren beschäftigt sich der Autor mit Tourenoptimierungsproblemen (siehe [MuRo2006], [Mu2011], [Mu2012], [Mu2017] und [Mu2018]). Dabei konnten Erfolge für bis zu zehn zu beliefernden Orten erzielt werden, aber nicht mehr, weil es sich um NP-schwierige Probleme handelt. (NP-Schwere bezeichnet eine Eigenschaft eines algorithmischen Problems. Dabei steht NP für nichtdeterministische Polynomialzeit.) Für derartige NP-schwierige Optimierungsprobleme gibt es im Operations Research (OR) gewisse Frameworks, die wenigstens für praxisrelevante Eingabedaten (z. B. 100 Orte) und Parameter diese Probleme lösen. Diese Frameworks arbeiten mit math. Verfahren, z. B. dem Branch-And-Price-Verfahren (kurz BaP), die für sehr große Lineare Programme (100000 Variablen und mehr) ausgedacht wurden. Das Ziel der Arbeit bestand darin, eine Arbeitsumgebung für das Brand-and-Price-Verfahren der Optimierung bereitzustellen und die Eignung der Arbeitsumgebung anhand eines selbst programmierten Beispiels nachzuweisen. Gleichzeitig soll eine Empfehlung für eines der am Markt verfügbaren Frameworks gegeben werden. Das Thema ist sehr komplex, deshalb sind Kennnisse und Fähigkeiten in math. Modellierung und Programmierung erforderlich. Die Programmpakete sind sehr umfangreich und schlecht dokumentiert. Durch diese Arbeit soll Licht ins Dunkel gebracht werden. Das Programmpaket ’SCIP’ (siehe [SCIP]) besteht z. B. aus 270.000 Quelltextzeilen, woran zehn Mitarbeiter 12 Jahre programmiert haben. Im Rahmen des Forschungssemesters sollte auch nach Alternativen gesucht werden, die für Master-Studenten besser handhabbar sind als SCIP. Im Sinne eines Tutoriums sollten auch didaktische Beispiele zur Theorie angefertigt werden, denn ohne theoretisches Hintergrundwissen ist die Software nicht zu bedienen. Ausserdem ist für die Software möglichst unter gängigen integrierten Entwicklungsumgebungen (Engl. Integrated Development Environment (IDE)), wie z. B. Netbeans , die Anwendbarkeit zu erreichen. Damit wird allen Masterstudenten ein leicht bedienbares Werkzeug für die Optimierung komplexer Problemstellungen zur Verfügung gestellt, das sie im Rahmen ihrer Masterthesis für geeignete Themen nutzen können. Zur Illustration werden Beispiele aus folgenden Problemklassen verwendet: Tourenoptimierung, eindimensionales Zuschneiden (Cutting Stock) sowie das Behälterproblem (Bin-Packing).

Suggested Citation

  • Mumm, Harald, 2018. "Didaktischer Zugang zur Theorie und Praxis moderner Softwarebibliotheken (Frameworks) für die Unternehmensforschung (OR)," Wismar Discussion Papers 07/2018, Hochschule Wismar, Wismar Business School.
    • Handle: RePEc:zbw:hswwdp:072018
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    References listed on IDEAS

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    1. Guy Desaulniers, 2010. "Branch-and-Price-and-Cut for the Split-Delivery Vehicle Routing Problem with Time Windows," Operations Research, INFORMS, vol. 58(1), pages 179-192, February.
    2. G. Clarke & J. W. Wright, 1964. "Scheduling of Vehicles from a Central Depot to a Number of Delivery Points," Operations Research, INFORMS, vol. 12(4), pages 568-581, August.
    3. Mumm, Harald, 2012. "Optimale Lösungen von Tourenoptimierungsproblemen mit geteilter Belieferung, Zeitfenstern, Servicezeiten und vier LKW-Typen," Wismar Discussion Papers 08/2012, Hochschule Wismar, Wismar Business School.
    4. Mumm, Harald, 2011. "Benchmark zur Tourenoptimierung," Wismar Discussion Papers 07/2011, Hochschule Wismar, Wismar Business School.
    5. Mumm, Harald, 2017. "Atlas optimaler Touren," Wismar Discussion Papers 03/2017, Hochschule Wismar, Wismar Business School.
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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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