Earth Observation Satellite Scheduling With Interval-Varying Profits

This study investigates an Earth observation satellite scheduling problem for monitoring key targets’ dynamics, where each target requires two observations within a reasonable time interval. The observation profit and observation effect depend on the interval between the two observations. To define...

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Vydané v:IEEE transactions on aerospace and electronic systems Ročník 60; číslo 6; s. 8273 - 8288
Hlavní autori: Li, Jiaojiao, Zhu, Jianghan, Xu, Dongyang, Wang, Jianjiang, Li, Zhimeng, Zhang, Kunlun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.12.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Branch-and-price (B&P) algorithm
Computing time
discrete optimization
Diving
diving heuristic (DH)
Earth
Earth observations (from space)
Earth Observing System
Heuristic
Heuristic algorithms
Integer programming
interval-varying profits
Linear programming
Lower bounds
Mixed integer
neighborhood search (NS)
Optimal scheduling
Orbits
Profits
Satellite observation
satellite scheduling
Satellites
Scheduling
Search algorithms
Task analysis
ISSN:0018-9251, 1557-9603
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Shrnutí:This study investigates an Earth observation satellite scheduling problem for monitoring key targets’ dynamics, where each target requires two observations within a reasonable time interval. The observation profit and observation effect depend on the interval between the two observations. To define the relationship between observation profits and intervals, a new profit function is introduced. Subsequently, a mixed-integer linear programming model is formulated. Furthermore, in order to efficiently address the problem, an exact branch-and-price algorithm is proposed. To improve solution efficiency, a neighborhood search algorithm is utilized to provide initial feasible solutions. In addition, the pricing problem is solved using a bidirectional label-setting algorithm, employing dynamic ng-path relaxation. To obtain integer solutions quickly, diving heuristic and matheuristic branching strategies are presented. More specifically, the diving heuristic strategy is used to obtain a lower bound at each column generation iteration. Computational results demonstrate that the proposed branch-and-price algorithm is superior to the conventional branch-and-cut algorithm used in CPLEX software package. Furthermore, when integrating the diving heuristic and matheuristic branching strategies, computational time is drastically reduced by an average of 98.7% compared to the exact branch-and-price algorithm. The practical applicability of the proposed algorithms is further validated and assessed through a real-world case study.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2024.3427090