Mission scheduling in space network with antenna dynamic setup times
IEEE Transactions on Aerospace and Electronic Systems, 2018•ieeexplore.ieee.org
Due to the constantly increasing mission demands for the space network (SN), the technical
issue of high-efficient scheduling has attracted continuous attention in recent years. Most of
the previous scheduling algorithms have generally ignored two critical factors, ie, the
dynamic property of single access (SA) antenna's setup times and the time-space
distribution characteristics of users' requested missions. In this paper, we build a
mathematical model for the heterogeneous intersatellite link antenna (ILA) pointing route …
issue of high-efficient scheduling has attracted continuous attention in recent years. Most of
the previous scheduling algorithms have generally ignored two critical factors, ie, the
dynamic property of single access (SA) antenna's setup times and the time-space
distribution characteristics of users' requested missions. In this paper, we build a
mathematical model for the heterogeneous intersatellite link antenna (ILA) pointing route …
Due to the constantly increasing mission demands for the space network (SN), the technical issue of high-efficient scheduling has attracted continuous attention in recent years. Most of the previous scheduling algorithms have generally ignored two critical factors, i.e., the dynamic property of single access (SA) antenna's setup times and the time-space distribution characteristics of users' requested missions. In this paper, we build a mathematical model for the heterogeneous intersatellite link antenna (ILA) pointing route problem with the consideration of constraints on visibility windows and dynamic setup times. Specifically, the ILA's pointing mechanics cost and the missions' time-space distribution are first taken into account into the scheduling model. Meanwhile, a new two-stage heuristic algorithm with hierarchical scheduling strategies is proposed to solve this optimization model. Finally, we employ the SN dataset to verify our proposed model by comparing three algorithms, the existing greedy randomized adaptive search procedure (GRASP) algorithm with static setup times, our improved GRASP algorithm with dynamic setup times, and our proposed two-stage heuristic algorithm. Experimental results show that our improved GRASP can schedule 3.72%, 10.36%, 12.51% more missions and can consume 53.18%, 49.95%, and 49.32% less setup times of SA antennas than the original Rojanasoonthon's GRASP for the mission scale of 200, 400, and 600, respectively. Moreover, compared with the improved GRASP algorithm, our proposed two-stage heuristic algorithm is more effective, which can further enhance the total number of scheduled missions by 2.41%, 4.43%, and 6.02% and reduce the setup times of SA antennas by 11.84%, 10.38%, and 9.54%.
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