A mixed integer linear programming model for multi-satellite scheduling

Chen, Xiaoyu; Reinelt, Gerhard; Dai, Guangming; Spitz, Andreas

doi:10.1016/j.ejor.2018.11.058

Cited by 90 publications

(35 citation statements)

References 36 publications

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“…As a result, the transition times between the observation chances of different VTWs can be pre-computed and thus the time-dependency is ignored. Chen et al [11] also build a MIP model for multiple satellite scheduling. However, they use an upper bound to replace the actual transition time empirically.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving the Agile Earth Observation Satellite Scheduling Problem With Time-Dependent Transition Times

Peng

Song

et al. 2022

IEEE Trans. Syst. Man Cybern, Syst.

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The scheduling of Agile Earth Observation Satellites is to select a subset of candidate targets each associated with a profit during their visible time windows in order to maximize the collected profits, under some operational constraints. For each pair of two consecutive observations, a transition time is required to perform a rotating movement of the camera, depending on the start times of the two observations. This time-dependency significantly increases the complexity of the scheduling problem. To solve this problem efficiently, we model the time-dependent transition time and prove that it satisfies the First-In-First-Out rule and the triangle inequalities rule. On this basis, we develop a novel hybrid heuristic, called "Greedy Randomized Iterated Local Search" (GRILS). A specific insert operator including a fast feasibility check and an assignment procedure are specifically designed to address the operational constraints of the scheduling. Extensive experiments on the single satellite instances and multi-satellite instances demonstrate that our algorithm outperforms the state-of-the-art algorithms with respect to solution quality and computation time. Index Terms-time-dependent transition time, satellite scheduling, GRILS heuristic.

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Section: Literature Reviewmentioning

confidence: 99%

Solving the Agile Earth Observation Satellite Scheduling Problem With Time-Dependent Transition Times

Peng

Song

et al. 2022

IEEE Trans. Syst. Man Cybern, Syst.

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“…Oversubscribed, overloaded, or overconstrained scheduling problems arise naturally in many domains such as airport gate assignment [24], processor task scheduling [25], [26], and meeting scheduling [27], [28]. Perhaps most similar to the DSN scheduling problem are problems in the satellite scheduling domain, including satellite range scheduling [29], [30], telescope scheduling [31], [32], payload scheduling [33], [34], and in particular the satellite observation scheduling problem [35]- [41], which also includes the time window constraints encountered in DSN scheduling. Although the DSN scheduling problem is similar to many of the oversubscribed scheduling problems present in the literature, it has several unique properties.…”

Section: A Literature Reviewmentioning

confidence: 99%

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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“…This model is then solved by CPLEX, but only for very small instances. Chen et al [17] build a MIP model for multiple satellite scheduling. In their model, the transition times are displaced by their upper bounds, i.e., the maximum possible transition times.…”

Section: Literature Reviewmentioning

confidence: 99%

An Exact Algorithm for Agile Earth Observation Satellite Scheduling with Time-Dependent Profits

Peng

Song

Xing

et al. 2020

Computers & Operations Research

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The scheduling of an Agile Earth Observation Satellite (AEOS) consists of selecting and scheduling a subset of possible targets for observation in order to maximize the collected profit related to the images while satisfying its operational constraints. In this problem, a set of candidate targets for observation is given, each with a time-dependent profit and a visible time window. The exact profit of a target depends on the start time of its observation, reaching its maximum at the midpoint of its visible time window. This time dependency stems from the fact that the image quality is determined by the look angle between the satellite and the target to be observed. We present an exact algorithm for the single-orbit scheduling for an AEOS considering the time-dependent profits. The algorithm is called Adaptive-directional Dynamic Programming with Decremental State Space Relaxation (ADP-DSSR). This algorithm is based on the dynamic programming approach for the Orienteering Problem with Time Windows (OPTW). Several algorithmic improvements are proposed to address the time-dependent profits. The proposed algorithm is evaluated based on extensive computational tests. The experimental results show that the algorithmic improvements significantly reduce the required computational time. The comparison between the proposed exact algorithm and

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A mixed integer linear programming model for multi-satellite scheduling

Cited by 90 publications

References 36 publications

Solving the Agile Earth Observation Satellite Scheduling Problem With Time-Dependent Transition Times

Solving the Agile Earth Observation Satellite Scheduling Problem With Time-Dependent Transition Times

Deep Space Network Scheduling via Mixed-Integer Linear Programming

An Exact Algorithm for Agile Earth Observation Satellite Scheduling with Time-Dependent Profits

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