Solving the Integrated-tasks satellite range scheduling problem: A surrogate-assisted variable neighborhood search approach

This study addresses the large-scale Satellite Range Scheduling Problem (SRSP) that integrates Tracking, Telemetry, and Command (TT&C) and Data Transmission (DT) tasks (ISRSP), while considering the capabilities of ground stations in supporting satellite operations. By combining scheduling segments into available satellite operating arcs, we first formulate a binary integer programming model to maximize the fulfillment of two kinds of tasks (TT&C and DT). A comprehensive surrogate-assisted variable neighborhood search (SVNS) algorithm is then proposed to solve the ISRSP. We prove that the SVNS algorithm converges to the set of local optimal solutions with probability 1. Large-scale numerical experiments where the numbers of schedule tasks range from 5,000 to 10,000 are conducted to evaluate the effectiveness of the developed model and gauge the efficiency of the proposed algorithm. The results are compared with several state-of-the-art algorithms and demonstrate that the SVNS algorithm outperforms others in terms of convergence time, accuracy and solution stability.