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A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with multiple time windows is presented. The problems' another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in-first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re-optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.