TY - GEN
T1 - Robust Predictive-Reactive Scheduling
T2 - 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020
AU - Portoleau, Tom
AU - Artigues, Christian
AU - Guillaume, Romain
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - In this paper we introduce a proactive-reactive approach to deal with uncertain scheduling problems. The method constructs a robust decision tree for a decision maker that is reusable as long as the problem parameters remain in the uncertainty set. At each node of the tree we assume that the scheduler has access to some knowledge about the ongoing scenario, reducing the level of uncertainty and allowing the computation of less conservative solutions with robustness guarantees. However, obtaining information on the uncertain parameters can be costly and frequent rescheduling can be disturbing. We first formally define the robust decision tree and the information refining concepts in the context of uncertainty scenarios. Then we propose algorithms to build such a tree. Finally, focusing on a simple single machine scheduling problem, we provide experimental comparisons highlighting the potential of the decision tree approach compared with reactive algorithms for obtaining more robust solutions with fewer information updates and schedule changes.
AB - In this paper we introduce a proactive-reactive approach to deal with uncertain scheduling problems. The method constructs a robust decision tree for a decision maker that is reusable as long as the problem parameters remain in the uncertainty set. At each node of the tree we assume that the scheduler has access to some knowledge about the ongoing scenario, reducing the level of uncertainty and allowing the computation of less conservative solutions with robustness guarantees. However, obtaining information on the uncertain parameters can be costly and frequent rescheduling can be disturbing. We first formally define the robust decision tree and the information refining concepts in the context of uncertainty scenarios. Then we propose algorithms to build such a tree. Finally, focusing on a simple single machine scheduling problem, we provide experimental comparisons highlighting the potential of the decision tree approach compared with reactive algorithms for obtaining more robust solutions with fewer information updates and schedule changes.
U2 - 10.1007/978-3-030-50153-2_36
DO - 10.1007/978-3-030-50153-2_36
M3 - Conference contribution
AN - SCOPUS:85086244818
SN - 9783030501525
T3 - Communications in Computer and Information Science
SP - 479
EP - 492
BT - Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings
A2 - Lesot, Marie-Jeanne
A2 - Vieira, Susana
A2 - Reformat, Marek Z.
A2 - Carvalho, João Paulo
A2 - Wilbik, Anna
A2 - Bouchon-Meunier, Bernadette
A2 - Yager, Ronald R.
PB - Springer
Y2 - 15 June 2020 through 19 June 2020
ER -