Abstract
In studying large-scale optimization models, numerical problems make it impossible to find an optimal solution. If one could find an optimal solution, the implementation of this solution would present other difficulties: the solution would be difficult to understand, while it would not be robust to changes in the state parameters. Therefore one wants to approximate the model.The two most important methods to do so are Aggregation and Decomposition. In this contribution we compare these two methods not only with one another but also with the optimal strategy in case of a one-machine, multi-product planning problem.
Zusammenfassung
Die Lösung großer, komplexer Modelle unter Unsicherheit ist in der Regel nur mit Hilfe von vereinfachenden Approximationsverfahren möglich. Die zwei wichtigsten Vorgehensweisen zur Vereinfachung derartiger Modelle stellen die Aggregation und die Dekomposition dar. In diesem Beitrag werden für ein Ein-Maschinen-Produkt Produktionsplanungsproblem unter Unsicherheit diese beiden Methoden miteinander und mit der optimalen Strategie verglichen.
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© 1983 Springer-Verlag Berlin Heidelberg
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Bemelmans, R. (1983). Aggregation and Decomposition in One-Machine, Multi-Product Planning Problems. In: Bühler, W., Fleischmann, B., Schuster, KP., Streitferdt, L., Zander, H. (eds) Operations Research Proceedings 1982. Operations Research Proceedings. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-38529-6_6
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DOI: https://doi.org/10.1007/978-3-662-38529-6_6
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