Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
Amplification of Search Performance through Randomization of Heuristics
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Simulation-based planning for planetary rover experiments
WSC '05 Proceedings of the 37th conference on Winter simulation
Introduction to Statistical Methods and Data Analysis (with CD-ROM)
Introduction to Statistical Methods and Data Analysis (with CD-ROM)
A comparison of techniques for scheduling earth observing satellites
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
Understanding algorithm performance on an oversubscribed scheduling application
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Heuristic-biased stochastic sampling
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Mixed discrete and continuous algorithms for scheduling airborne astronomy observations
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
IBM ILOG CP Optimizer for Detailed Scheduling Illustrated on Three Problems
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
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In recent years, planning and scheduling research has paid increasing attention to problems that involve resource oversubscription, where cumulative demand for resources outstrips their availability and some subset of goals or tasks must be excluded. Two basic classes of techniques to solve oversubscribed scheduling problems have emerged: searching directly in the space of possible schedules and searching in an alternative space of task permutations (by relying on a schedule builder to provide a mapping to schedule space). In some problem contexts, permutation-based search methods have been shown to outperform schedule-space search methods, while in others the opposite has been shown to be the case. We consider two techniques for which this behavior has been observed: TaskSwap (TS), a schedule-space repair search procedure, and Squeaky Wheel Optimization (SWO), a permutation-space scheduling procedure. We analyze the circumstances under which one can be expected to dominate the other. Starting from a real-world scheduling problem where SWO has been shown to outperform TS, we construct a series of problem instances that increasingly incorporate characteristics of a second real-world scheduling problem, where TS has been found to outperform SWO. Experimental results provide insights into when schedule-space methods and permutation-based methods may be most appropriate.