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A Branch and Bound Algorithm for Agile Earth Observation Satellite Scheduling

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  • Xiaogeng Chu
  • Yuning Chen
  • Lining Xing

Abstract

The agile earth observing satellite scheduling (AEOSS) problem consists of scheduling a subset of images among a set of candidates that satisfy imperative constraints and maximize a gain function. In this paper, we consider a new AEOSS model which integrates a time-dependent temporal constraint. To solve this problem, we propose a highly efficient branch and bound algorithm whose effective ingredients include a look-ahead construction method (for generating a high quality initial lower bound) and a combined use of three pruning strategies (which help to prune a large portion of the search space). We conducted computational experiments on a set of test data that were generated with information from real-life scenarios. The results showed that the proposed algorithm is efficient enough for engineering application. In particular, it is able to solve instances with 55 targets to optimality within 164 seconds on average. Furthermore, we carried out additional experiments to analyze the contribution of each key algorithm ingredient.

Suggested Citation

  • Xiaogeng Chu & Yuning Chen & Lining Xing, 2017. "A Branch and Bound Algorithm for Agile Earth Observation Satellite Scheduling," Discrete Dynamics in Nature and Society, Hindawi, vol. 2017, pages 1-15, September.
    • Handle: RePEc:hin:jnddns:7345941
    DOI: 10.1155/2017/7345941
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    Cited by:

    1. Rigo, Cezar Antônio & Seman, Laio Oriel & Camponogara, Eduardo & Morsch Filho, Edemar & Bezerra, Eduardo Augusto & Munari, Pedro, 2022. "A branch-and-price algorithm for nanosatellite task scheduling to improve mission quality-of-service," European Journal of Operational Research, Elsevier, vol. 303(1), pages 168-183.
    2. Jie Chun & Wenyuan Yang & Xiaolu Liu & Guohua Wu & Lei He & Lining Xing, 2023. "Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem," Mathematics, MDPI, vol. 11(19), pages 1-20, September.

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