A variable order Runge-Kutta method for initial value problems with rapidly varying right-hand sides
ACM Transactions on Mathematical Software (TOMS)
Astronomical Algorithms
Leap before you look: an effective strategy in an oversubscribed scheduling problem
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
An effective algorithm for project scheduling with arbitrary temporal constraints
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
SOFIA's choice: an AI approach to scheduling airborne astronomy observations
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
A comparison of techniques for scheduling earth observing satellites
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
Journal of Artificial Intelligence Research
Heuristic-biased stochastic sampling
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Combining genetic algorithms with squeaky-wheel optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Improving genetic algorithm performance with intelligent mappings from chromosomes to solutions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Understanding performance tradeoffs in algorithms for solving oversubscribed scheduling
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
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We describe the problem of scheduling astronomy observations for the Stratospheric Observatory for Infrared Astronomy, an airborne telescope. The problem requires maximizing the number of requested observations scheduled subject to a mixture of discrete and continuous constraints relating the feasibility of an astronomical observation to the position and time at which the observation begins, telescope elevation limits, Special Use Airspace limitations, and available fuel. Solving the problem requires making discrete choices (e.g. selection and sequencing of observations) and continuous ones (e.g. takeoff time and setup actions for observations by repositioning the aircraft). Previously, we developed an incomplete algorithm called ForwardPlanner using a combination of AI and OR techniques including progression planning, lookahead heuristics, stochastic sampling and numerical optimization, to solve a simplified version of this problem. While initial results were promising, ForwardPlanner fails to scale when accounting for all relevant constraints. We describe a novel combination of Squeaky Wheel Optimization (SWO), an incomplete algorithm designed to solve scheduling problems, with previously devised numerical optimization methods and stochastic sampling approaches, as well as heuristics based on reformulations of the SFPP to traditional OR scheduling problems. We show that this new algorithm finds as good or better flight plans as the previous approaches, often with less computation time.