Multi-Objective, Multi-User 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 re-use 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 are modeled: minimizing wasted time, minimizing the number of observations that miss their last planning opportunity in a year, and minimizing the (vector) build up of angular momentum that 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.…”

Analyzing Assessment Practices for Indigenous Students

“…Information obtained using (or misusing) that standardized test will likely yield information that is unreliable and invalid about those individuals not represented in the test's norming samples during the development process. Johnston and Claypool (2010) explained that standardized tests can be problematic when attempting to effectively and fairly measure learning and academic success of Indigenous students. Notably, the majority of norm-referenced standardized tests predominately rely on Western knowledge paying little attention to cultural and linguistic barriers that have the potential to disadvantage Indigenous students performance while ensuring more positive outcomes for non-Indigenous test takers.…”

“…For example, a final assessment could be a dance performance that encapsulates physical, emotional, spiritual, as well as intellectual knowledge and wellbeing. Johnston and Claypool (2010) suggested that assessment for Indigenous students include student interviews, behavioral observations, peer-generated assessment, talking/discussion circles to share views and ideas, experiential assessment, and parents, Elders, and community members also serving as evaluators.…”