Operational rationality through compilation of anytime algorithms
Operational rationality through compilation of anytime algorithms
Iterative Flattening: A Scalable Method for Solving Multi-Capacity Scheduling Problems
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Temporal constraint reasoning with preferences
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Journal of Scheduling
An overview of AI research in Italy
Artificial intelligence
Constraint-based methods for scheduling discretionary services
AI Communications
Schedule robustness through broader solve and robustify search for partial order schedules
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
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We consider a schedule optimization problem where each activity to be scheduled has a duration-dependent quality profile, and activity durations must be determined that maximize overall quality within given deadline and resource constraints. To solve this quality maximization problem, prior work has proposed a hybrid search scheme, where a linear programming solver for optimally setting the durations of temporally related activities is embedded within a larger search procedure that incrementally posts sequencing constraints to resolve resource conflicts. Under this approach, dual concerns of establishing feasibility and optimizing quality are addressed in an integrated fashion. In this paper, we propose an alternative approach, where feasibility and optimization concerns are treated separately: first, we establish a resource-feasible partial order schedule, assuming minimum durations for all activities; second, these fixed duration constraints are relaxed and quality optimal durations are determined, Experimental results indicate a tradeoff: when resource capacity constraints are loose, the integrated hybrid approach performs comparably to the separated scheme. However, in problems with tighter capacity constraints we find that separation of concerns enables both better solving capability and higher quality results. Following from these results, we discuss potential synergy between problem objectives of maintaining temporal flexibility and maximizing quality.