Article ID: | iaor20119142 |
Volume: | 61 |
Issue: | 2 |
Start Page Number: | 322 |
End Page Number: | 335 |
Publication Date: | Sep 2011 |
Journal: | Computers & Industrial Engineering |
Authors: | Reinelt Gerhard, Tan Yuejin, Wang Pei, Gao Peng |
Keywords: | vehicle routing & scheduling, artificial intelligence: decision support |
China plans to launch four small optical satellites and four small SAR satellites to form a natural disaster monitoring constellation. Data can be obtained by the constellation in all weather conditions for disaster alert and environmental damage analysis. The scheduling problem for the constellation consists of selecting and timetabling the observation activities to acquire the requested images of the earth surface and scheduling the download activities to transmit the image files to a set of ground stations. The scheduling problem is required to be solved every day in a typical 1‐day horizon and it must respect complex satellite operational constraints as well as request preferences, such as visibility time windows, transition time between consecutive observations or downloads, memory capacity, energy capacity, polygon target requests and priorities. The objective is to maximize the rewards of the images taken and transmitted. We present a nonlinear model of the scheduling problem, develop a priority‐based heuristic with conflict‐avoided, limited backtracking and download‐as‐needed features, which produces satisfactory feasible plans in a very short time. A decision support system based on the model and the heuristic is also provided. The system performance shows a significant improvement with respect to faster and better scheduling of an earth observing satellite constellation.