两栖攻击舰舰载直升机群出动作业一体化调度研究

韩维; 韩啸华; 苏析超; 王刘松; 吴浩南

doi:10.13700/j.bh.1001-5965.2024.0085

两栖攻击舰舰载直升机群出动作业一体化调度研究

doi: 10.13700/j.bh.1001-5965.2024.0085

1.
海军航空大学航空基础学院，烟台 264001
2.
海军航空大学航空作战勤务学院，烟台 264001
3.
中国人民解放军 91860部队，上海 200000

基金项目:

中国科协青年人才托举工程

详细信息

通讯作者:
E-mail：suxich@126.com

中图分类号: V355.2
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- 被引次数: 0
出版历程
- 收稿日期: 2024-02-08
- 录用日期: 2024-04-02
- 网络出版日期: 2024-05-30
- 整期出版日期: 2026-04-30

Research on integrated scheduling of shipboard helicopters sortie operation on amphibious assault ship

1.
Aeronautical Foundation College，Naval Aviation University，Yantai 264001，China
2.
Aeronautical Operation College，Naval Aviation University，Yantai 264001，China
3.
Unit 91860 of the PLA，Shanghai 200000，China

Funds:

Young Elite Scientists Sponsorship Program by CAST

More Information

Corresponding author: E-mail: suxich@126.com

摘要

摘要:
甲板作业调度是提升舰载机出动率的关键技术。针对两栖攻击舰舰载直升机群出动作业多阶段、高耦合特性，提出一种包含出库及甲板调运、机务勤务保障、出动离场在内的调度模型和优化算法。深入分析舰载直升机出动作业流程，基于各阶段的逻辑约束和资源约束，以最小化机群出动时间为优化目标，建立舰载直升机群出动作业一体化调度模型；将该问题转化为具有转移时间特征的资源受限项目调度问题，基于串行调度生成机制，设计一种两段编码的遗传-邻域搜索算法求解。通过对12架舰载直升机集中出动案例进行仿真实验，验证了调度模型和优化算法在优化出动流程方面的有效性，显著降低了机群出动准备时间，进一步发现机务勤务保障阶段的决策在全局调度中更为关键。
- 两栖攻击舰 /
- 舰载直升机 /
- 出动作业 /
- 一体化调度 /
- 资源受限项目调度
Abstract:
Deck operation scheduling is the key technology to improve the sortie rate of carrier-based aircraft. In view of the multi-stage and high coupling characteristics of amphibious assault ship carrier-borne helicopters operation, an integrated scheduling model and optimization algorithm were proposed, which included warehousing and deck transportation, maintenance service support, and sortie departure. First, an integrated scheduling model of carrier helicopter sortie operation was developed based on the resource and logical restrictions of each stage in order to reduce the duration of the sortie. Then, the problem is abstracted as a resource-constrained project scheduling problem with transition times, and a two-segment genetic-neighborhood search algorithm is designed to solve the problem based on a serial schedule generation scheme. Through simulation experiments conducted on the concentrated sortie cases of 12 shipboard helicopters, the effectiveness of the scheduling model and algorithm in optimizing the sortie process was verified. This significantly reduced the sortie preparation time for the helicopter group. Additionally, it was shown that the entire scheduling is more significantly impacted by decisions made during the maintenance service support phase.
- amphibious assault ship /
- shipboard helicopter /
- sortie operation /
- integrated scheduling /
- resource-constrained project scheduling

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图 1 舰载直升机出动作业流程

Figure 1. Process of shipborne helicopter sortie operation

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图 2 G-NSA 流程

Figure 2. G-NSA flow

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图 3 个体编码方案

Figure 3. Individual coding scheme

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图 4 停机位分配

Figure 4. Gate assignment

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图 5 保障人员分配

Figure 5. Support personnel assignment

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图 6 舰载直升机初始布列位置

Figure 6. Initial position of shipboard helicopters

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图 7 单机保障流程

Figure 7. Support process of single-helicopter

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图 8 舰载直升机调度甘特图

Figure 8. Gantt chart of shipboard helicopter scheduling

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图 9 保障人员调度甘特图

Figure 9. Gantt chart of personnel scheduling

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图 10 保障设备调度甘特图

Figure 10. Gantt chart of support equipment scheduling

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图 11 不同算法收敛曲线

Figure 11. Convergence curves of different algorithms

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表 1 停机位分配优先级

Table 1. Priority for gate assignment

初始机位	目标机位
初始机位	A1	A2	A3	A4	A5	A6	停机区Ⅰ	停机区Ⅱ
C1	1	2	3	4	6	5	7	8
C2	2	3	4	5	6	1	8	7
停机区Ⅰ	1	2	3	4	5	6
停机区Ⅱ	6	5	4	3	2	1

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表 2 保障工序及对资源需求

Table 2. Guaranteed processes and resource requirements

工序号	内容	设备	专业
1	虚拟开始
2	平台区机械检查		机械
3	下部机械检查		机械
4	旋翼展开	供电站	特设/机械
5	航电外观检查		航电
6	航电座舱检查	供电站	航电
7	任务数据加载	供电站	特设/航电
8	平台区特设检查		特设
9	下部特设检查		特设
10	特设座舱检查	供电站	特设
11	燃油加注	加油站	机械
12	惯导对准		航电
13	军械外观检查		军械
14	机载弹药挂载		军械
15	虚拟结束

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表 3 不同算法对比结果

Table 3. Comparison results of different algorithms

算法	最小化机群出动时间/min			收敛到最优值的最小评价次数平均值
算法	平均值	最优值	方差	收敛到最优值的最小评价次数平均值
G-NSA	76.7	75.9	0.958	2940
GA	77.1	75.5	1.172	3749
PSO	78.5	75.9	2.312	3724
DE	79.8	78.1	0.733	2757

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表 4 个体分段优化对比结果

Table 4. Comparison results of individual segment optimization

算法	最小化机群出动时间/min			收敛到最优值的最小评价次数平均值
算法	平均值	最优值	方差	收敛到最优值的最小评价次数平均值
G-NSA	76.7	75.9	0.958	2940
G-NSA （第一段编码）	83.5	82.3	0.776	1833
G-NSA (第二段编码)	77.7	76.7	1.231	3900

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