We study simulation optimization methods for the stochastic economic lot scheduling problem. In contrast to
prior research, we focus on methods that treat this problem as a black box. Based on a large-scale numerical
study, we compare approximate dynamic programming with a global search for parameters of simple control
policies. We propose two value function approximation schemes based on linear combinations of piecewise-
constant functions as well as control policies that can be described by a small set of parameters. While
approximate value iteration worked well for small problems with three products, it was clearly outperformed
by the global policy search as soon as problem size increased. The most reliable choice in our study was a
globally optimized fixed-cycle policy. An additional analysis of the response surface of model parameters on
optimal average cost revealed that the cost effect of product diversity was negligible. (authors' abstract)
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:4042 |
| Date | 10 April 2013 |
| Creators | Löhndorf, Nils, Minner, Stefan |
| Publisher | Taylor and Francis |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Article, PeerReviewed |
| Format | application/pdf |
| Relation | http://dx.doi.org/10.1080/0740817X.2012.662310, http://www.tandfonline.com/, http://epub.wu.ac.at/4042/ |
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