TY - GEN
T1 - Surrogate-Assisted Multi-Objective Parameter Optimization for Production Planning Systems
AU - Karder, Johannes
AU - Beham, Andreas
AU - Peirleitner, Andreas Josef
AU - Altendorfer, Klaus
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85083993763&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-45093-9_29
DO - 10.1007/978-3-030-45093-9_29
M3 - Conference contribution
SN - 9783030450922
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 239
EP - 246
BT - Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers
A2 - Moreno-Díaz, Roberto
A2 - Quesada-Arencibia, Alexis
A2 - Pichler, Franz
PB - Springer
T2 - 17th International Conference on Computer Aided Systems Theory, EUROCAST 2019
Y2 - 17 February 2019 through 22 February 2019
ER -