Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
5-2010
Abstract
Resource Constrained Project Scheduling Problems with minimum and maximum time lags (RCPSP/max) have been studied extensively in the literature. However, the more realistic RCPSP/max problems — ones where durations of activities are not known with certainty – have received scant interest and hence are the main focus of the paper. Towards addressing the significant computational complexity involved in tackling RCPSP/max with durational uncertainty, we employ a local search mechanism to generate robust schedules. In this regard, we make two key contributions: (a) Introducing and studying the key properties of a new decision rule to specify start times of activities with respect to dynamic realizations of the duration uncertainty; and (b) Deriving the fitness function that is used to guide the local search towards robust schedules. Experimental results show that the performance of local search is improved with the new fitness evaluation over the best known existing approach.
Discipline
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering
Publication
ICAPS 2010: Proceedings of the Twentieth International Conference on Automated Planning and Scheduling: Toronto, Canada, May 12-16, 2010
First Page
73
Last Page
80
ISBN
9781577354499
Publisher
AAAI Press
City or Country
Menlo Park, CA
Citation
FU, Na; VARAKANTHAM, Pradeep; and LAU, Hoong Chuin.
Towards finding robust execution strategies for RCPSP/max with durational uncertainty. (2010). ICAPS 2010: Proceedings of the Twentieth International Conference on Automated Planning and Scheduling: Toronto, Canada, May 12-16, 2010. 73-80.
Available at: https://ink.library.smu.edu.sg/sis_research/304
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://www.aaai.org/ocs/index.php/ICAPS/ICAPS10/paper/view/1425
Included in
Artificial Intelligence and Robotics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons