Automated Scheduling for NASA's Deep Space Network

Johnston, Mark; Tran, Daniel

doi:10.1609/aimag.v35i4.2552

Cited by 14 publications

(21 citation statements)

References 15 publications

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“…The DSN has undertaken an overall unification and simplification of the scheduling software systems 6,7,[11][12][13] , of which the first increment has been operational since June 2011. This is called the Service Scheduling Software (SSS, or S 3 ) and has initially been applied only to the mid-range process.…”

Section: B Status Of Dsn Scheduling Softwarementioning

confidence: 99%

“…Underlying the web application and database is a scheduling automation component, the DSN Scheduling Engine 13 (DSE). The DSE provides a range of functions based on the semantics of the DSN scheduling domain, including:…”

Section: Dsn Service Scheduling Softwarementioning

confidence: 99%

“…Multi-objective optimization has been employed in a wide variety of problem domains, including scheduling for science missions [17][18][19][20] and generating some requirements inputs to the DSN mid-range process 21 .…”

Section: B Leveraged Development Of Long-range Planning and Forecastmentioning

confidence: 99%

“…• visibility constraints • flexibilities Further discussion of the nature of these requirements and flexibilities is included below in Section III.C; more detail is provided elsewhere 6,7 .…”

Section: Introductionmentioning

confidence: 99%

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Automating Mid- and Long-Range Scheduling for NASA's Deep Space Network

Johnston¹,

Tran²,

Arroyo³

et al. 2013

Space Operations: Experience, Mission Systems, and Advanced Concepts

Self Cite

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NASA has recently deployed a new mid-range scheduling system for the antennas of the Deep Space Network (DSN), called Service Scheduling Software, or S 3 . This system is architected as a modern web application containing a central scheduling database integrated with a collaborative environment, exploiting the same technologies as social web applications but applied to a space operations context. This is highly relevant to the DSN domain since the network schedule of operations is developed in a peer-to-peer negotiation process among all users who utilize the DSN (representing 37 projects including international partners and ground-based science and calibration users). The initial implementation of S 3 is complete and the system has been operational since July 2011. S 3 has been used for negotiating schedules since April 2011, including the baseline schedules for three launching missions in late 2011. S 3 supports a distributed scheduling model, in which changes can potentially be made by multiple users based on multiple schedule "workspaces" or versions of the schedule. This has led to several challenges in the design of the scheduling database, and of a change proposal workflow that allows users to concur with or to reject proposed schedule changes, and then counter-propose with alternative or additional suggested changes. This paper describes some key aspects of the S 3 system and lessons learned from its operational deployment to date, focusing on the challenges of multi-user collaborative scheduling in a practical and mission-critical setting. We will also describe the ongoing project to extend S 3 to encompass long-range planning, downtime analysis, and forecasting, as the next step in developing a single integrated DSN scheduling tool suite to cover all time ranges. I. IntroductionThe scheduling of NASA's Deep Space Network (DSN) presents a variety of challenges. As a unique and mission-critical capability serving an increasingly large user community, it is essential that the network be efficiently scheduled and utilized. At the same time, DSN users represent an enormous range of types of scheduling requirements and constraints, that vary substantially depending on type of mission, on time as spacecraft enter different mission phases, and seasonally as visibility intervals overlap in different ways. Against a backdrop of increasing budget pressure, the DSN has undertaken a project to replace its existing aging scheduling software with a new and unified tool suite intended to improve all aspects of the DSN planning and asset allocation and scheduling processes.

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Section: B Status Of Dsn Scheduling Softwarementioning

confidence: 99%

Section: Dsn Service Scheduling Softwarementioning

confidence: 99%

Section: B Leveraged Development Of Long-range Planning and Forecastmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automating Mid- and Long-Range Scheduling for NASA's Deep Space Network

Johnston¹,

Tran²,

Arroyo³

et al. 2013

Space Operations: Experience, Mission Systems, and Advanced Concepts

Self Cite

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“…[14][15] One defines sets of valid plans that cover all possible contingencies. Science priorities are described by objective functions that map specific science or measurement objectives to scalar values.…”

Section: Approachmentioning

confidence: 99%

Agile science operations: a new approach for primitive bodies exploration

Thompson¹,

Castillo‐Rogez²,

Chien³

et al. 2012

SpaceOps 2012 Conference

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Primitive body exploration missions such as potential Comet Surface Sample Return or Trojan Tour and Rendezvous would challenge traditional operations practices. Earth-based observations would provide only basic understanding before arrival and many science goals would be defined during the initial rendezvous. It could be necessary to revise trajectories and observation plans to quickly characterize the target for safe, effective observations. Detection of outgassing activity and monitoring of comet surface activity are even more time constrained, with events occurring faster than round-trip light time. "Agile science operations" address these challenges with contingency plans that recognize the intrinsic uncertainty in the operating environment and science objectives. Planning for multiple alternatives can significantly improve the time required to repair and validate spacecraft command sequences. When appropriate, time-critical decisions can be automated and shifted to the spacecraft for immediate access to instrument data. Mirrored planning systems on both sides of the light-time gap permit transfer of authority back and forth as needed. We survey relevant science objectives, identifying time bottlenecks and the techniques that could be used to speed missions' reaction to new science data. Finally, we discuss the results of a trade study simulating agile observations during flyby and comet rendezvous scenarios. These experiments quantify instrument coverage of key surface features as a function of planning turnaround time. Careful application of agile operations techniques can play a significant role in realizing the Decadal Survey plan for primitive body exploration.

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Deep Space Network Scheduling via Mixed-Integer Linear Programming

et al. 2021

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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.

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Automated Scheduling for NASA's Deep Space Network

Cited by 14 publications

References 15 publications

Automating Mid- and Long-Range Scheduling for NASA's Deep Space Network

Automating Mid- and Long-Range Scheduling for NASA's Deep Space Network

Agile science operations: a new approach for primitive bodies exploration

Deep Space Network Scheduling via Mixed-Integer Linear Programming

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