Abstract
The authors investigate using genetic programming as a tool for finding good heuristics for supply chain restocking strategies. In this paper they outline their method that integrates a supply chain simulation with genetic programming. The simulation is used to score the population members for the evolutionary algorithm which is, in turn, used to search for members that might perform better on the simulation. The fitness of a population member reflects its relative performance in the simulation. This paper investigates both the effectiveness of this method and the parameter settings that make it more or less effective.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Box, George E. P., Hunter, William G., and Hunter, J. Stuart (1978). Statistics for Experimenters. Wiley-Interscience.
Casella, George and George, Edward I. (1992). Explaining the Gibbs sampler. American Statistician, 46(3):167–174.
Feldt, Robert and Nordin, Peter (2000). Using factorial experiments to evaluate the effect of genetic programming parameters. In Poli, Riccardo, Banzhaf, Wolfgang, Langdon, William B., Miller, Julian F., Nordin, Peter, and Fogarty, Terence C., editors, Genetic Programming, Proceedings of EuroGP’2000, LNCS 1802, pages 271–282, Edinburgh. Springer-Verlag.
Gelfand, Alan E, Hills, Susan E., Racine-Poon, Amy, and Smith, Adrian F. M. (1990). Illustration of Bayesian inference in normal data models using Gibbs sampling. Journal of the American Statistical Association, 88(421): 171–178.
Koza, John R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA.
Moore, Scott A. and DeMaagd, Kurt (2004). Beer game genetic program, http://sourceforge.net/projects/beergame/.
Parunak, H. Van Dyke, Savit, Robert, and Riolo, Rick L. (1998). Agent-based modeling vs. equation-based modeling: A case study and users’ guide. In Proceedings of Multi-agent Systems and Agent-based Simulation (MABS’ 98), LNAI 1534. Springer-verlag.
Shell Internationale Research Maatschappij (1998). Keyfinder.
Sterman, John D. (1989). Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management Science, 35(3):321–339.
Waclawiw, Myron A. and Liang, Kung-Yee (1993). Prediction of random effects in the generalized linear model. Journal of the American Statistical Association, 88(421): 171–178.
Weiss, Carol H. (1998). Evaluation. Prentice Hall, 2nd edition.
Wu, C. F. Jeff and Hamada, Michael (2000). Experiments: Planning, Analysis, and Parameter Design Optimization. Wiley-Interscience.
Zellner, Arnold (1971). An Introduction to Bayesian Inference in Econometrics. John Wiley & Sons, Inc.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer Science+Business Media, Inc.
About this chapter
Cite this chapter
Moore, S.A., DeMaagd, K. (2005). Using Genetic Programming to Search for Supply Chain Reordering Policies. In: O’Reilly, UM., Yu, T., Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice II. Genetic Programming, vol 8. Springer, Boston, MA. https://doi.org/10.1007/0-387-23254-0_13
Download citation
DOI: https://doi.org/10.1007/0-387-23254-0_13
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-23253-9
Online ISBN: 978-0-387-23254-6
eBook Packages: Computer ScienceComputer Science (R0)