Δ-MILP: Deep Space Network Scheduling via Mixed-Integer Linear Programming

Claudet, Thomas; Alimo, Ryan; Goh, Edwin; Johnston, Mark; Madani, Ramtin; Wilson, Brian

doi:10.1109/access.2022.3164213

Cited by 2 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to assess the performance of our QUBO formulation, we compare our results with those of Claudet et al [26] who used the same data as we did with a conventional Mixed Integer Linear Programming (MILP) algorithm. The comparison is summarized in Table 4.…”

Section: Resultsmentioning

confidence: 99%

“…This means that although the MILP based algorithm can split tracks into more tracks than the QUBO formulation can (2 tracks), this ability does not result in a better user satisfaction. Our implementation of the QUBO formulation schedules more tracks, in less time, while achieving a better user satisfaction than the ∆-MILP(0) implementation of [26].…”

Section: Resultsmentioning

confidence: 99%

“…At this point one could enforce any secondary desiderata; for this problem there were none and so we simply centered each activity period in the assigned time window. [26] using the same DSN data. The run times for ∆-MILP(0) and ∆-MILP(0)-L were 30 and 450 minutes respectively.…”

Section: Custom Solver With Resultsmentioning

confidence: 99%

“…We compared our results with those of Claudet et al [26] who used the same data and produced DSN schedules with a conventional Mixed Integer Linear Programming technique. Solutions returned by the HSS with our QUBO matrices satisfied substantially more requests, scheduled more hours and had lower unsafistied time fraction than those obtained with MILP using a fifteen time longer run time than the HSS run time.…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Deep Space Network Scheduling Using Quantum Annealing

Guillaume

Goh

Johnston

et al. 2022

IEEE Trans. Quantum Eng.

Self Cite

View full text Add to dashboard Cite

NASA's Deep Space Network (DSN) is responsible for communication and navigation for several NASA and international missions. The DSN comprises three complexes located in Goldstone (California, USA), Cambera (Australia) and Madrid (Spain). This distribution in longitude guarantees a full sky coverage. Each complex has one 70-meter and several 34-meter antennas. The network routinely serves a few dozens missions. The scheduling of the DSN is complex and involves humans interventions as well as automated solutions. In order to increase the level of automation, different computing paradigms have been explored. In this paper, we report on the quadratic unconstrained binary optimization (QUBO) formulation of the DSN scheduling, as well as a custom classical solver designed around some of the unique features of this scheduling problem. Thanks to a hybrid framework that extends the size of the problems that can be solved with a quantum annealer, we are able to generate a schedule from the QUBO formulation of the problem for one week's worth of user antenna requests, which represents the time period scheduled during operation. In other words, this work describes a real-world application of quantum annealing using real-world, operational data. We compare the resulting schedules' quality to solutions obtained using a mixed-integer linear programming formulation on a commercial solver. Our custom solver, based on a quantum-inspired optimization technique called Substochatic Monte-Carlo, while much faster in generating schedules, could only treat a subset of requests and hence we report its results independently.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Custom Solver With Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Space Network Scheduling Using Quantum Annealing

Guillaume

Goh

Johnston

et al. 2022

IEEE Trans. Quantum Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…A notorious example is [21], that designs a MILP formulation of the NASA's Deep Space Network (DSN) scheduling problem, and later a non-MILP based heuristic to validate the results for a real week with 14 resources, 286 activities, 1430 hours of tracking time, and 30 missions. Subsequently, this same formulation is improved in [22], that introduces a new set of constraints and compared the results with the previous one. The variant allows to prioritize emergency or landing scenarios and satisfies all the requested constraints.…”

confidence: 99%

A Mixed Integer Linear Programming Model for Resolution of the Antenna-Satellite Scheduling Problem

Linares,

Vazquez,

Perea

et al. 2024

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

This paper deals with one of the types of "Satellite Range Scheduling" problems arising in Earth Observation Satellite operations, Antenna-Satellite Scheduling. Given a set of satellites, a set of available antennas and a time horizon, the problem consists of designing an operational plan that assigns satellites to antennas in an optimal fashion. Extending a previous Integer Linear Programming (ILP) model (Shortening Model, with only integer variables), we propose a Mixed Integer Linear Programming (MILP) model (Shaving Model, which includes both continuous and integer variables), to more efficiently solve this problem. After computing the passes generated by the satellites' windows of visibility from the antennas, the optimal planner is able to cancel a pass, move it to another antenna, or shorten its duration, in order to avoid scheduling conflicts. In contrast to the Shortening Model, which used intersections between passes to determine the best schedule, the shortening operation is now referred to as shaving, since the Shaving Model can arbitrarily adjust the duration of a pass in a razor-like fashion, giving the model its name. Computational results obtained in tests over realistic scenarios prove that the Shaving Model outperforms the Shortening Model, producing fewer cancellations, smaller shaved times, and a fairer distribution of cancelled passes among satellites, with much shorter pre-processing times.

show abstract

Δ-MILP: Deep Space Network Scheduling via Mixed-Integer Linear Programming

Abstract: This paper introduces ∆-MILP, a powerful variant of the mixed-integer linear programming (MILP) optimization framework to solve NASA's Deep Space Network (DSN) scheduling problem. This work is an extension of our original MILP framework

Cited by 2 publications

References 27 publications

Deep Space Network Scheduling Using Quantum Annealing

Deep Space Network Scheduling Using Quantum Annealing

A Mixed Integer Linear Programming Model for Resolution of the Antenna-Satellite Scheduling Problem

Contact Info

Product

Resources

About