TY - GEN
T1 - Solution approaches for the dynamic stacking problem
AU - Raggl, Sebastian
AU - Beham, Andreas
AU - Wagner, Stefan
AU - Affenzeller, Michael
N1 - Publisher Copyright:
© 2020 ACM.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/7/8
Y1 - 2020/7/8
N2 - In this paper we describe a dynamic stacking problem as it arises in a more complex form in the steel industry. We describe a simulation model and the simulated processes that are implemented. The model covers a gantry crane that performs relocations of blocks among three types of stacks. In the real world the crane operators and dispatchers solve complex stacking problems targeting to minimize relocation effort while adhering to many constraints, to various time windows, and to satisfy quality demands. While our model does not include all the real-world constraints, the challenges and benefits of solving this problem as a dynamic optimization problem in contrast to a static formulation become apparent. We adapt an existing solution approach from a static formulation that is based on the block relocation problem and compare it with a hand-written rule-based approach. Furthermore, we devise a hybrid approach in the form of an open-ended evolutionary algorithm.
AB - In this paper we describe a dynamic stacking problem as it arises in a more complex form in the steel industry. We describe a simulation model and the simulated processes that are implemented. The model covers a gantry crane that performs relocations of blocks among three types of stacks. In the real world the crane operators and dispatchers solve complex stacking problems targeting to minimize relocation effort while adhering to many constraints, to various time windows, and to satisfy quality demands. While our model does not include all the real-world constraints, the challenges and benefits of solving this problem as a dynamic optimization problem in contrast to a static formulation become apparent. We adapt an existing solution approach from a static formulation that is based on the block relocation problem and compare it with a hand-written rule-based approach. Furthermore, we devise a hybrid approach in the form of an open-ended evolutionary algorithm.
KW - Dynamic optimization problem
KW - Stacking
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85089741849&partnerID=8YFLogxK
U2 - 10.1145/3377929.3398111
DO - 10.1145/3377929.3398111
M3 - Conference contribution
AN - SCOPUS:85089741849
T3 - GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
SP - 1652
EP - 1660
BT - GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
T2 - 2020 Genetic and Evolutionary Computation Conference, GECCO 2020
Y2 - 8 July 2020 through 12 July 2020
ER -