The integrated agile Earth observation satellite scheduling problem
研究了集成敏捷地球观测卫星调度问题，提出混合整数规划模型和启发式算法，以最大化观测收益和数据下载量，适用于卫星任务规划。
Satellites are widely used for different purposes, such as monitoring deforestation, rising sea levels, and ecological changes. As a scarce resource, scheduling the activities performed by these spacecrafts is of paramount importance. This paper addresses the integrated agile Earth observation satellite scheduling problem (IAEOSSP) that employs a constellation of agile satellites to observe the Earth. The objective of the IAEOSSP is to maximize the profits associated with observed targets and the amount of data downloaded to ground stations. Critical aspects of the problem are considered, such as the onboard storage capacity, time windows, setup times, and energy dynamics. We propose a mixed-integer programming (MIP) formulation for the problem, as well as an improved MIP model that is computationally more tractable at the expense of possibly reducing the feasible search space. Moreover, we implement a MIP-based heuristic (MBH) to solve the proposed model with an efficient heuristic strategy for decreasing the size of the graph, as well as a heuristic from the literature initially designed for the non-agile version of the problem. Computational experiments evaluate the performance of the MBH not only on the IAEOSSP but also on a particular case involving conventional satellites denoted as constellation mission scheduling problem (CMSP). The results show that the MBH compares favorably against the improved MIP model for the IAEOSSP and against two existing algorithms for the CMSP.