Generating short-term observation schedules for space mission projects
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Space mission scheduling (SMS) has been an important research area for several years. The basic features of the space mission projects are the high investment and operational costs, and limited resource availability. Therefore, it is very important to justify the high investment on the space mission projects by generating good schedules. In this thesis, we have proposed several new solution algorithms for generating short term observation schedules of space mission projects and test their efficiencies on a good representative of SMS problem; Hubble Space Telescope (HST) scheduling problem. HST is an exceptional space observatory at low earth orbit among the others that are used for space exposures. The main features of generating short-term observations of HST are state dependent set up times, user specified due dates, priorities and the visibility windows assigned to the candidate observations. The objective of HST scheduling is to maximize the scientific return. We have proposed four new algorithms. The first one is a new dispatch rule that considers the basic features of the problem domain while scheduling the observations. The second one is a filtered beam search algorithm. We have introduced a new concept of childwidth, which is a parameter that restricts the number of beams that generates from the same parent. The third one is a Greedy Randomized Adaptive Search Procedure (GRASP) that needs to be tailored to be applicable to the problem domain. Finally, we proposed a simulated annealing algorithm with a new introduced concept of mutation. We have tested the relative performances of the proposed algorithms, as well as the nearest neighbor algorithm, both in objective function value and computational time aspects by utilizing a 2^ full-factorial experimental design.
KeywordsSpace mission scheduling
local search algorithms
Hubble Space Telescope
QB500.268 .K55 1997
Hubble space telescope.
Production scheduling--Mathematical models.
Industrial project management.