Abstract
Efficient global optimization is, even after over two decades of research, still considered as one of the best approaches to surrogate-assisted optimization. In this paper, material requirements planning parameters are optimized and two different versions of EGO, implemented as optimization networks in HeuristicLab, are applied and compared. The first version resembles a more standardized version of EGO, where all steps of the algorithm, i.e. expensive evaluation, model building and optimizing expected improvement, are executed synchronously in sequential order. The second version differs in two aspects: (i) instead of a single objective, two objectives are optimized and (ii) all steps of the algorithm are executed asynchronously. The latter leads to faster algorithm execution, since model building and solution evaluations can be done in parallel and do not block each other. Comparisons are done in terms of achieved solution quality and consumed runtime. The results show that the multi-objective, asynchronous optimization network can compete with the single-objective, synchronous version and outperforms the latter in terms of runtime.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Altendorfer, K., Felberbauer, T., Jodlbauer, H.: Effects of forecast errors on optimal utilisation in aggregate production planning with stochastic customer demand. Int. J. Prod. Res. 54(12), 3718–3735 (2016)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Hauder, V.A., Karder, J., Beham, A., Wagner, S., Affenzeller, M.: A general solution approach for the location routing problem. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2017. LNCS, vol. 10671, pp. 257–265. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74718-7_31
Hopp, W.J., Spearman, M.L.: Factory Physics. Waveland Press, Long Grove (2011)
Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4), 455–492 (1998)
Karder, J., Wagner, S., Beham, A., Kommenda, M., Affenzeller, M.: Towards the design and implementation of optimization networks in HeuristicLab. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2017, pp. 1209–1214. ACM (2017)
Kommenda, M., et al.: Optimization networks for integrated machine learning. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2017. LNCS, vol. 10671, pp. 392–399. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74718-7_47
Rasmussen, C.E.: Gaussian processes in machine learning. In: Bousquet, O., von Luxburg, U., Rätsch, G. (eds.) ML 2003. LNCS (LNAI), vol. 3176, pp. 63–71. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28650-9_4
Acknowledgments
The work described in this paper was done within the Produktion der Zukunft Project Integrated Methods for Robust Production Planning and Control (SIMGENOPT2, #858642), funded by the Austrian Research Promotion Agency (FFG).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Karder, J., Beham, A., Peirleitner, A., Altendorfer, K. (2020). Surrogate-Assisted Multi-Objective Parameter Optimization for Production Planning Systems. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science(), vol 12013. Springer, Cham. https://doi.org/10.1007/978-3-030-45093-9_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-45093-9_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45092-2
Online ISBN: 978-3-030-45093-9
eBook Packages: Computer ScienceComputer Science (R0)