Mixed Discrete and Continuous Algorithms for Scheduling Airborne Astronomy Observations

• Published in: Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems pp. 183 - 200
• Main Authors: Frank, Jeremy, Kürklü, Elif
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Feasibility Check; Ground Track; Learning and adaptive systems; Mathematical programming; Numerical Optimization Method; Operational research and scientific management; Operational research. Management science; Schedule Problem; Stochastic Sampling; Telescope; Flight; Optimization method; Astronomical observation; Scheduling; Stochastic method; Combinatorial optimization; Constrained optimization; Stochastic programming; Heuristic method; Problem solving; Setup time; Feasibility; Planning; Sampling; Aircraft; Artificial intelligence; Sequencing; Astronomy; Mathematical programming
• ISBN: 9783540261520; 3540261524
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11493853_15

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.