The second case was supported by the INTEGRAL (INTErnational Gamma-Ray Astrophysics Laboratory) long term planning team of the Integral Science Operations Centre (ISOC) based at ESAC in Madrid, Spain.
The main aim was to optimise the satisfaction of the scientific objectives expressed in the yearly announcements of opportunities. These announcements of opportunities, or AO’s as they are commonly called, are generated by the user community prior to the commencement of the next long term planning period which nominally covers one year. Not only do AO’s for the next planning period have to be considered but also AO’s from the previous planning period which were not scheduled are included, with increased priority.
To solve this optimisation problem, the APSI INTEGRAL MISSION Scheduler (AIMS) uses a local search algorithm that combines the best ideas from the state-of-the-art local search algorithms such as hill-climbing, tabu-search, and simulated annealing. This local search algorithm uses the underlying APSI framework to maintain flexible consistent schedules within each revolution and to determine the amount of observation time that can be added to the observations already scheduled within a revolution. In other words, the APSI framework is used to efficiently manage the basic scheduling constraints and the local search algorithm, built on top of it, is used to manage the optimisation criterion and specific constraints.
