Expectation and SAA Models and Algorithms for Scheduling of Multiple Earth Observation Satellites Under the Impact of Clouds

Abstract

In the explosively increased number of applications of earth observation satellites (EOSs), scheduling is a significative issue to satisfy more requests and obtain a high observation efficiency. This paper investigates the scheduling of multiple EOSs under the impact of clouds. Firstly, we formulate the presences of clouds as stochastic events, and propose an expectation model. Afterwards, for the first time, a branch-and-price algorithm based on Dantzig-Wolfe decomposition is devised to solve the model optimally and efficiently. In order to obtain initial columns for column generation, a dynamic programming algorithm and a heuristic algorithm are suggested, respectively. Furthermore, we discuss the impact of clouds in the case of joint probabilities, and establish a sample average approximation (SAA) model accordingly. With respect to the expectation model, numerical experiment results demonstrate the relative dominance of the proposed branch-and-price algorithm in terms of solution time compared to CPLEX. In addition, the solution of the proposed SAA model is proven to be more robust than that of the expectation model.

Mission Replanning for Multiple Agile Earth Observation Satellites Based on Cloud Coverage Forecasting

“…Liao et al [21] described the uncertain cloud coverage as stochastic events and constructed a model containing the objective of maximizing the expected number of accomplished tasks. Wang et al [22] formulated a stochastic expectation model for CEOS scheduling problems under the impact of the cloud. The profit of a target is simplified depicted as a 0-1 distribution model corresponding to be observed successfully or not under the cloud coverage uncertainty.…”

“…The expectation model [22] and the CCP model [23] hold the assumption that one task can only be observed on a single orbit while scheduling one task on several orbits would improve the probability of successful observation. Wang et al [26] established a nonlinear robust mathematical model that could schedule each task on different orbits and proposed three heuristics to solve the large-scale problems.…”

Ensemble of Metaheuristic and Exact Algorithm Based on the Divide-and-Conquer Framework for Multisatellite Observation Scheduling

“…The task scheduling model aims to maximize the overall profits of all the scheduled tasks, while satisfying the constraints related to satellite operations, including satellite transfer time between two consecutive tasks, energy capacity, and memory capacity. The profit of a task represents the importance and value to the user of completing the observation task [22], [33], [38].…”

Multi Criteria Decision Making for the Multi-Satellite Image Acquisition Scheduling Problem

“…Barkaoui and Berger [16] developed a hybrid genetic algorithm to solve the multi-satellite collection scheduling problem for maximizing expected collection value. Wang et al [17] investigated the scheduling of multiple earth observation satellites by assuming stochastic behaviour of clouds. The authors employed branch-and-price algorithm to find the schedule that maximized the profits of the satellite operator.…”