Multi-Objective Scheduling for Space Science Missions

Abstract

We have developed an architecture called MUSE (Multi-User Scheduling Environment) to enable the integration of multi-objective evolutionary algorithms with existing domain planning and scheduling tools. Our approach is intended to make it possible to reuse existing software, while obtaining the advantages of multi-objective optimization algorithms. This approach enables multiple participants to actively engage in the optimization process, each representing one or more objectives in the optimization problem. As initial applications, we apply our approach to scheduling the James Webb Space Telescope, where three objectives aremodeled: minimizing wasted time, minimizing the number of observations that miss their last planning opportunity in a year, and minimizing the (vector) build up of angularmomentumthat would necessitate the use of mission critical propellant to dump the momentum. As a second application area, we model aspects of the Cassini science planning process, including the trade-off between collecting data (subject to onboard recorder capacity) and transmitting saved data to Earth. A third mission application is that of scheduling the Cluster 4-spacecraft constellation plasma experiment. In this paper we describe our overall architecture and our adaptations for these different application domains. We also describe our plans for applying this approach to other science mission planning and scheduling problems in the future.

Automated Scheduling for NASA's Deep Space Network

“…The incorporation of multiobjective optimization (for example, Brown and Johnston [2013]; Johnston [2006]) into LAPS offers a new way to optimize DSN resource allocations, taking into account that there is no single objective that captures all of the disparate goals and objectives that are important. Multiobjective optimization has been employed in a wide variety of problem domains, including scheduling for science missions and generating some requirements inputs to the DSN midrange process (Johnston and Giuliano 2011).…”

“…Once an initial schedule has been generated, conflicts and/or violations may exist in the schedule due to the relaxation of constraints (Johnston and Giuliano 2011). The DSE provides schedule repair algorithms to reduce conflicts or violations.…”

Modeling and Simulation of Sky Survey

“…Table 2 summarizes some of the more representative software systems, according to Wang et al [32] and Amiaux et al [17]. Multi-User Scheduling Environment [29] No information available JWST/Cassini/Cluster WBD Spike planning and scheduling software [36], Sky Survey Planning Tool [17], and Multi-User Scheduling Environment [29] are applied in real satellite planning tasks with excellent optimization results. The Euclid telescope was able to achieve the science goal of covering an area of 15,500 deg 2 in 5.5 years, which also includes a 40 deg 2 deep-field observation area.…”

“…The complex constraints and wide sky area factors bring great challenges to the existing optimization algorithms to solve the sky survey observation models. The survey observation of large space astronomy telescopes is a complex task [29], and advanced planning and operation control technology is the basis for the efficient completion of the task. The space astronomy observation telescope will be constrained by various factors in the process of a sky survey observation.…”

A Simple and Effective Termination Condition for Both Single- and Multi-Objective Evolutionary Algorithms

“…The technique is based on a crowding estimation using the nearest neighbors of solutions in an Euclidean sense, and an efficient nearest neighbors search technique. GDE3 has performed well both in several academic studies [10], [19], [20] and in several practical applications [21]- [25], e.g., NASA has applied GDE3 for solving some space science optimization problems [26]. Therefore, GDE3 has been selected as an optimization method of this study.…”