Abstract
NASA’s Deep Space Network (DSN) is a globally-spanning communications network responsible for supporting the interplanetary spacecraft missions of NASA and other international users. The DSN is a highly utilized asset, and the large demand for its’ services makes the assignment of DSN resources a daunting computational problem. In this paper we study the DSN scheduling problem, which is the problem of assigning the DSN’s limited resources to its users within a given time horizon. The DSN scheduling problem is oversubscribed, meaning that only a subset of the activities can be scheduled, and network operators must decide which activities to exclude from the schedule. We first formulate this challenging scheduling task as a Mixed-Integer Linear Programming (MILP) optimization problem. Next, we develop a sequential algorithm which solves the resulting MILP formulation to produce valid schedules for large-scale instances of the DSN scheduling problem. We use real world DSN data from week 44 of 2016 in order to evaluate our algorithm’s performance. We find that given a fixed run time, our algorithm outperforms a simple implementation of our MILP model, generating a feasible schedule in which 17% more activities are scheduled by the algorithm than by the simple implementation. We design a non-MILP based heuristic to further validate our results. We find that our algorithm also outperforms this heuristic, scheduling 8% more activities and 20% more tracking time than the best results achieved by the non-MILP implementation.
Highlights
N ASA’s Deep Space Network (DSN) is a globallyspanning communications network responsible for supporting the interplanetary spacecraft missions of NASA and other international users [1]
This paper considers the process of DSN scheduling over a one week time horizon
We develop a mathematical model of the DSN scheduling process to elucidate its relationship to other scheduling problems, and to illustrate the nuances of DSN scheduling which justify the construction of our model
Summary
N ASA’s Deep Space Network (DSN) is a globallyspanning communications network responsible for supporting the interplanetary spacecraft missions of NASA and other international users [1]. NASA’s DSN is one of the largest and most sensitive telecommunications systems in the world, consisting of three large antenna facilities spaced approximately 120 degrees apart, allowing for constant communication between the DSN’s ground stations and the spacecraft they service [1], [2]. The DSN supports a multitude of users across a wide variety of scientific missions, including lunar, earth orbiting, ground-based, and deep space missions [3]. As of April 2019, the DSN supports 70 unique missions [4], offering a broad range of services including telemetry, spacecraft command, tracking, radio astronomy, and more [1]. The large variety of services provided by the DSN means that requirements from mission to mission can vary drastically, making the assignment of DSN resources a difficult logistical problem
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