A heuristic solution framework for the resource-constrained multi-aircraft scheduling problem with transfer of resources and aircraft

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@Article{SU:2023:eswa,

• author = "Xichao Su and Rongwei Cui and Changjiu Li and Wei Han and Jie Liu",
• title = "A heuristic solution framework for the resource-constrained multi-aircraft scheduling problem with transfer of resources and aircraft",
• journal = "Expert Systems with Applications",
• volume = "228",
• pages = "120430",
• year = "2023",
• ISSN = "0957-4174",
• DOI = "doi:10.1016/j.eswa.2023.120430",
• URL = "https://www.sciencedirect.com/science/article/pii/S0957417423009326",
• keywords = "genetic algorithms, genetic programming, Project scheduling, Heuristics, Flight deck operation, Carrier aircraft",
• abstract = "Efficient and feasible flight deck operation scheduling plan is of great significance for improving the sortie rate of carrier aircraft. Oxygen filling, fueling, and other operations of each carrier aircraft are executed in the pre-flight preparation stage. Different personnel, support equipments, and supply resources, are required while different operations are being executed. To date, most approaches proposed in the literature are based on the unrealistic assumption that the transfer process of carrier aircraft can be ignored. To close this gap, the resource-constrained multi-aircraft scheduling problem on the flight deck is examined in this study. The main contributions of the study are as follows. First, by considering the transfer process of aircraft on the flight deck operations, the model formulation of resource-constrained multi-aircraft scheduling problem is established. Second, the parallel and serial scheduling framework are proposed, where parking spot priority rules and resource allocation rules are presented to select the most suitable resources. Third, a modified genetic programming algorithm is presented for obtaining efficient activity priority rules. Fourth, the well-performed scheduling framework, priority rules, and allocation rules are selected by conducting computational experiments. Furthermore, experiment results show that the parallel scheduling framework outperforms the serial framework in most experiments (average outperformance is 10.19percent), and the parking spot priority rules slightly affect the serial scheduling framework",

}

Genetic Programming entries for Xichao Su Rongwei Cui Changjiu Li Wei Han Jie Liu

Citations