  The satellite image data downlink scheduling problem (SIDSP) is well studied
in literature for traditional satellites. With recent developments in satellite
technology, SIDSP for modern satellites became more complicated, adding new
dimensions of complexities and additional opportunities for the effective use
of the satellite. In this paper, we introduce the dynamic two-phase satellite
image data downlink scheduling problem (D-SIDSP) which combines two interlinked
operations of image data segmentation and image data downlink, in a dynamic
way, and thereby offering additional modelling flexibility and renewed
capabilities. D-SIDSP is formulated as a bi-objective problem of optimizing the
image data transmission rate and the service-balance degree. Harnessing the
power of an adaptive large neighborhood search algorithm (ALNS) with a
nondominated sorting genetic algorithm II (NSGA-II), an adaptive bi-objective
memetic algorithm, ALNS+NSGA-II, is developed to solve D-SIDSP. Results of
extensive computational experiments carried out using benchmark instances are
also presented. Our experimental results disclose that the algorithm
ALNS+NSGA-II is a viable alternative to solve D-SIDSP more efficiently and
demonstrates superior outcomes based on various performance metrics. The paper
also offers new benchmark instances for D-SIDSP that can be used in future
research works on the topic.
