A two-stage flow-shop scheme for the multi-satellite observation and data- downlink scheduling problem considering weather uncertainties

摘要

An Earth observation satellite (EOS) is a sort of low earth orbit (LEO) satellite that is equipped with high resolution cameras for observing various types of target objects scattered across the surface of the Earth. The available time windows of multiple EOSs for observing a given target object and for downloading the acquired image/video data from satellites to ground receiver stations are scarce resources that should to be utilized efficiently. This problem is hereby named as the multi-satellite observation and data-downlink scheduling problem (MSODSP). We developed a two-stage flow shop scheme for the MSODSP in order to optimize the observation scheduling (at stage 1) and data-downlink scheduling (at stage 2) concurrently and get truly optimized results. A mixed-integer linear programming (MILP) model is developed for the MSODSP with three objective functions. The effects of weather uncertainties on the tasks' success are considered in the MILP model, which allow us to conduct a reliability-maximized task arrangement of EOSs. Computational experiments were conducted on the simulated data of a real LEO satellite to verify the proposed MILP model. The results showed that it was able to solve real-world instances of the MSODSP for up to 20 tasks over 8 days.