Deep Space Network Scheduling via Mixed-Integer Linear Programming

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.

Dynamic Synchronization Modeling and Simulation of Video Sensing Nodes Based on Internet of Things

“…Sabol et al studied the partitioning of software and hardware under variable demands of intelligent systems and proposed a set of partitioning algorithms for enhanced hardware/software resource sharing parameters. Sabol et al studied the hardware and software partitioning of heterogeneous multiprocessor system-on-chip (MPSoC) and proposed an optimized integer linear programming algorithm to solve small input hardware and software A method for partitioning the problem [12].…”

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

“…This section introduces new constraints in addition to the ones implemented in [14]. But all of the them are being reviewed for the purpose of completeness.…”

“…Section §III derives the DSN scheduling problem formulation. Section §IV presents the results obtained by the algorithm and compares its performance with previous iterations from [14]. Section §V finally draws conclusions on this work.…”

“…In our previous work [14], the problem was formulated as a mixed-integer linear program (MILP). MILP has many applications such as for task graphs [15], [16], energy hub plants [17], flow shop scheduling [18], [19], or job shop scheduling [20].…”

“…Finding valid schedules that satisfy the important constraints encountered in the real world leads to a large MILP problem with tens of thousands of binary optimization variables and hundreds of millions of elements to set the problem up. This work in [14] presented a new, computationally efficient, and scalable formulation and algorithm to solve the DSN scheduling problem on a set of historical DSN requests. In this paper, that framework is extended by incorporating new constraints to achieve higher quality schedules in terms of feasibility and optimality.…”