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Hybrid Differential Evolution Optimisation for Earth Observation Satellite Scheduling with Time-Dependent Earliness-Tardiness Penalties

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  • Guoliang Li
  • Cheng Chen
  • Feng Yao
  • Renjie He
  • Yingwu Chen

Abstract

We study the order acceptance and scheduling (OAS) problem with time-dependent earliness-tardiness penalties in a single agile earth observation satellite environment where orders are defined by their release dates, available processing time windows ranging from earliest start date to deadline, processing times, due dates, sequence-dependent setup times, and revenues. The objective is to maximise total revenue, where the revenue from an order is a piecewise linear function of its earliness and tardiness with reference to its due date. We formulate this problem as a mixed integer linear programming model and develop a novel hybrid differential evolution (DE) algorithm under self-adaptation framework to solve this problem. Compared with classical DE, hybrid DE employs two mutation operations, scaling factor adaptation and crossover probability adaptation. Computational tests indicate that the proposed algorithm outperforms classical DE in addition to two other variants of DE.

Suggested Citation

  • Guoliang Li & Cheng Chen & Feng Yao & Renjie He & Yingwu Chen, 2017. "Hybrid Differential Evolution Optimisation for Earth Observation Satellite Scheduling with Time-Dependent Earliness-Tardiness Penalties," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-10, August.
    • Handle: RePEc:hin:jnlmpe:2490620
    DOI: 10.1155/2017/2490620
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    Cited by:

    1. 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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