A multi-objective hyper heuristic framework for integrated optimization of carrier-based aircraft flight deck operations scheduling and resource configuration

It is of great significance to produce an efficient flight deck operations scheduling plan for improving the carrier-based aircraft sortie rate and enhancing the combat capability of aircraft carrier formation. Flight deck operations scheduling plan is closely related to flight deck resource configu...

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Bibliographic Details
Published in:Aerospace science and technology Vol. 107; p. 106346
Main Authors: Cui, Rongwei, Han, Wei, Su, Xichao, Zhang, Yong, Guo, Fang
Format: Journal Article
Language:English
Published: Elsevier Masson SAS 01.12.2020
Subjects:
Flight deck operations
Hyper heuristic
Integrated optimization
Resource configuration
Integrated optimization
Hyper heuristic
Flight deck operations
Resource configuration
ISSN:1270-9638, 1626-3219
Online Access:Get full text
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Summary:It is of great significance to produce an efficient flight deck operations scheduling plan for improving the carrier-based aircraft sortie rate and enhancing the combat capability of aircraft carrier formation. Flight deck operations scheduling plan is closely related to flight deck resource configuration. In this paper, in order to find a feasible method to produce an effective operations scheduling plan under different resource configurations, the flight deck operations scheduling problem and resource configuration optimization problem for the pre-flight preparation stage are studied simultaneously. The work is different from the existing literature in three aspects: (1) with considering the transfer time of resources and multiple operation execution modes and analyzing the precedence constraints and resource constraints for the flight deck operations, the integrated optimization model of operations scheduling and resource configuration is established and regarded as a multi-objective optimization model. (2) a novel choice function based multi-objective hyper heuristic is proposed for solving the model. The low level heuristics are three well-known multi-objective evolutionary algorithms, and the heuristic selection strategy is an online learning choice function. (3) in order to further improve the performance of proposed hyper heuristic, two modified hyper heuristics are introduced, one of which uses a simulated annealing-based non-deterministic move acceptance strategy and the other uses a modified initiation method, a simulated binary crossover operator, and a normal distribution sampling mutation operator. By conducting simulation experiments in the case study section, the correctness of the model and the superiority of the multi-objective hyper heuristic framework are verified.
ISSN:1270-9638
1626-3219
DOI:10.1016/j.ast.2020.106346