Enhancing Aggregate Production Planning with an Integrated Stochastic Queuing Model

Gerd J. Hahn (), Chris Kaiser (), Heinrich Kuhn (), Lien Perdu () and Nico J. Vandaele ()
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Gerd J. Hahn: Catholic University of Eichstaett-Ingolstadt
Chris Kaiser: Catholic University of Eichstaett-Ingolstadt
Heinrich Kuhn: Catholic University of Eichstaett-Ingolstadt
Lien Perdu: Catholic University of Leuven
Nico J. Vandaele: Catholic University of Leuven

A chapter in Operations Research Proceedings 2011, 2012, pp 451-456 from Springer

Abstract: Abstract Mathematical models for Aggregate Production Planning (APP) typically omit the dynamics of the underlying production system due to variable workload levels since they assume fixed capacity buffers and predetermined lead times. Pertinent approaches to overcome these drawbacks are either restrictive in their modeling capabilities or prohibitive in their computational effort. In this paper, we introduce an Aggregate Stochastic Queuing (ASQ) model to anticipate capacity buffers and lead time offsets for each time bucket of the APP model. The ASQ model allows for flexible modeling of the underlying production system and the corresponding optimization algorithm is computationally very well tractable. The APP and the ASQ model are integrated into a hierarchical framework and are solved iteratively. A numerical example is used to highlight the benefits of this novel approach.

Date: 2012
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DOI: 10.1007/978-3-642-29210-1_72

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