基于改进灰狼算法的舰载机弹药保障调度优化

doi:10.12305/j.issn.1001-506X.2024.04.15

Abstract

Abstract:

An improved grey wolf optimizer (GWO) algorithm is proposed to solve the problem of inefficient scheduling faced by carrier-based aircraft ammunition support on the flight deck of aircraft carriers. According to the characteristics of the scenario of multiple lifts and multiple transport vehicles on the deck, the problem of transferring from multiple vehicle fields to multiple targets is modeled. The initial optimization of the initial solution of the grey wolf population is achieved by integrating the idea of genetic algorithm operator crossover, and the improvement of the solution process of the GWO algorithm is achieved by linear path transportation midpoint definition, integer encoding and negative integer sign grouping, etc. At the same time, the free hunting process of individual grey wolf is also added to effectively overcome the problem of results falling into local optimum and prematureness. Finally, the effectiveness and feasibility of the proposed method are verified through the optimal solution of the scenario example.

Key words: grey wolf optimizer (GWO) algorithm, multiple vehicle fields, multiple objectives, integer encoding, sign grouping

CLC Number:

Zhe LIU, Junfei MA, Jiafeng CHEN, Songhua MA. Carrier-based aircraft ammunition support scheduling optimization based on improved grey wolf optimizer algorithm[J]. Systems Engineering and Electronics, 2024, 46(4): 1264-1272.

Figures/Tables 15

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References 31

1	JEWELL A, WIGGE M A, GAGNON C M K, et al. USS Nimitz and carrier airwing nine surge demonstration[R]. Alexandria: Center for Naval Analyses, 1998.
2	张少辉, 刘舜, 李亚飞, 等. 航空母舰舰载机弹药保障作业调度优化算法[J]. 航空学报, 2023, 44 (20): 224- 241.
	ZHANG S H , LIU S , LI Y F , et al. Optimization algorithm for ammunition support operation scheduling of carrier-borne aircraft[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44 (20): 224- 241.
3	GUO F , HAN W , SU X C , et al. A bi-population immune algorithm for weapon transportation support scheduling problem with pickup and delivery on aircraft carrier deck[J]. Defence Technology, 2023, 22, 119- 134. doi: 10.1016/j.dt.2021.12.006
4	SMITH W . Scheduling stored combat load retrieval[J]. Journal of Defense Analytics and Logistics, 2018, 2 (2): 80- 93. doi: 10.1108/JDAL-07-2017-0012
5	YU L F, ZHU C, LI W J, et al. Research on the aviation su-pport groups scheduling for multi-wave aircrafts based on the ammunition transportation[C]//Proc. of the IEEE International Conference on Unmanned Systems, 2021: 383-389.
6	YUAN P L , HAN W , SU X C , et al. A dynamic scheduling method for carrier aircraft support operation under uncertain conditions based on rolling horizon strategy[J]. Applied Sciences, 2018, 8 (9): 1546. doi: 10.3390/app8091546
7	RYAN J C , BANERJEE A G , CUMMINGS M L , et al. Comparing the performance of expert user heuristics and an integer liner program in aircraft carrier deck operations[J]. IEEE Trans.on Cybernetics, 2014, 44 (6): 761- 773. doi: 10.1109/TCYB.2013.2271694
8	FENG Q, BI W J, SUN B, et al. Dynamic scheduling of carrier aircraft based on improved ant colony algorithm under disruption and strong constraint[C]//Proc. of the 2nd International Conference on Reliability Systems Engineering, 2017.
9	SU X C , WU H , CUI R W , et al. Joint optimization method for carrier-based aircraft fleet sortie support personnel configuration and scheduling based on marginal-ABC algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46 (11): 2056- 2068.
10	LIU Y J , HAN W , SU X C , et al. Optimization of fixed aviation support resource station configuration for aircraft carrier based on aircraft dispatch mission scheduling[J]. Chinese Journal of Aeronautics, 2023, 36 (2): 127- 138. doi: 10.1016/j.cja.2022.06.023
11	吕晓峰, 杨东泽, 马羚. 舰载机模块化弹药调度方案优化设计[J]. 系统工程与电子技术, 2023, 45 (2): 465- 471.
	LYU X F , YANG D Z , MA L . Optimal design of modular ammunition scheduling scheme for carrier-based aircraft[J]. Systems Engineering and Electronics, 2023, 45 (2): 465- 471.
12	刘翱, 刘克. 舰载机保障作业调度问题研究进展[J]. 系统工程理论与实践, 2017, 37 (1): 49- 60.
	LIU A , LIU K . Advanced in carrier-based aircraft deck operation scheduling[J]. Systems Engineering—Theory & Practice, 2017, 37 (1): 49- 60.
13	RYAN J C, CUMMINGS M L, ROY N, et al. Designing an interactive local and global decision support system for aircraft carrier deck scheduling[EB/OL]. [2023-04-05]. https://doi.org/10.2514/6.2011-1516.
14	LIU Y J , HAN W , SU X C , et al. Optimization of fixed aviation support resource station configuration for aircraft carrier based on aircraft dispatch mission scheduling[J]. Chinese Journal of Aeronautics, 2023, 36 (2): 127- 138. doi: 10.1016/j.cja.2022.06.023
15	SUN Z W , GU X S . A novel hybrid estimation of distribution algorithm for solving hybrid flow-shop scheduling problem with unrelated parallel machine[J]. Journal of Central South University, 2017, 24 (8): 1779- 1788. doi: 10.1007/s11771-017-3586-6
16	WANG X W , LIU J , SU X C , et al. A review on carrier aircraft dispatch path planning and control on deck[J]. Chinese Journal of Aeronautics, 2020, 33 (12): 3039- 3057. doi: 10.1016/j.cja.2020.06.020
17	YU L F , ZHU C , SHI J M , et al. An extended flexible job shop scheduling model for flight deck scheduling with priority, parallel operations, and sequence flexibility[J]. Scientific Programming, 2017, 2017, 1- 15.
18	YE GQ, WANG X T, WANG H C, et al. Wartime consumption prediction of aircraft carrier airborne ammunition based on case-based reasoning[C]//Proc. of the 3rd International Conference on Advanced Information Science and System, 2022.
19	MIRABI M , FATEMI G S M T , JOLAI F . Efficient stochastic hybrid heuristics for the multi-depot vehicle routing problem[J]. Robotics and Computer-Integrated Manufacturing, 2010, 26 (6): 564- 569. doi: 10.1016/j.rcim.2010.06.023
20	ZHOU Y W , LIU J , ZHANG Y T , et al. A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems[J]. Transportation Research Part E: Logistics and Transportation Review, 2017, 99, 77- 95. doi: 10.1016/j.tre.2016.12.011
21	MIRJALILI S , MIRJALILI S M , LEWIS A . Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69, 46- 61. doi: 10.1016/j.advengsoft.2013.12.007
22	KONG X H , YAO Y H , YANG W Q , et al. Solving the flexible job shop scheduling problem using a discrete improved grey wolf optimization algorithm[J]. Machines, 2022, 10 (11): 1100. doi: 10.3390/machines10111100
23	LI X Y , XIE J , MA Q J , et al. Improved gray wolf optimizer for distributed flexible job shop scheduling problem[J]. Science China Technological Sciences, 2022, 65 (9): 2105- 2115. doi: 10.1007/s11431-022-2096-6
24	GU J C , JIANG T H , ZHU H Q , et al. Low-carbon job shop scheduling problem with discrete genetic-grey wolf optimization algorithm[J]. Journal of Advanced Manufacturing Systems, 2020, 19 (1): 1- 14. doi: 10.1142/S0219686720500018
25	HUANG G W , CAI Y , LIU J Q , et al. A novel hybrid discrete grey wolf optimizer algorithm for multi-UAV path planning[J]. Journal of Intelligent & Robotic Systems, 2021, 103 (3): 49- 66.
26	ZHENG X M, LI B, WANG L T, et al. Design of carrier ammunition scheduling scheme based on improved genetic algorithm[C]//Proc. of the 7th International Conference on Control Engineering and Artificial Intelligence, 2023: 162-167.
27	黄戈文, 蔡延光, 戚远航, 等. 自适应遗传灰狼优化算法求解带容量约束的车辆路径问题[J]. 电子学报, 2019, 47 (12): 2602- 2610.
	HUANG G W , CAI Y G , QI Y H , et al. Adaptive genetic grey wolf optimizer algorithm for capacitated vehicle routing problem[J]. Acta Electronica Sinica, 2019, 47 (12): 2602- 2610.
28	CUI R W , HAN W , SU X C , et al. A multi-objective hyper heuristic framework for integrated optimization of carrier-based aircraft flight deck operations scheduling and resource con-figuration[J]. Aerospace Science and Technology, 2020, 107, 1- 15.
29	LIU M , LI G F . Ammunition scheduling method in airborne weapon depot based on improved genetic algorithm[J]. Journal of Physics: Conference Series, 2021, 1948 (1): 012050. doi: 10.1088/1742-6596/1948/1/012050
30	WANG L T, LI F Q, HUANG J R, et al. Optimization design of ammunition scheduling scheme for carrier-based aircraft based on improved DPSO algorithm[C]//Proc. of the 5th International Conference on Algorithms, Computing and Artificial Intelligence, 2022.
31	刘哲, 陈佳峰, 马俊飞, 等. 舰载机弹药保障调度仿真系统[J/OL]. 系统仿真学报, 2023. https://doi.org/10.16182/j.issn.1004731x.joss.23-0393.
	LIU Z, CHEN J F, MA J F, et al. Simulation system for ca-rrier-based aircraft ammunition support scheduling[J/OL]. Journal of System Simulation, 2023. https://doi.org/10.16182/j.issn.1004731x.joss.23-0393.

参数变量	含义
P	升降机数量
N	运输车数量
Q_n	运输车装载容量
K	目标工作点数量
k	运输暂存目标工作点数量
q_i	目标工作点i所需要的容量
v_m	运输车最大运输速度
[st_i, ft_i]	时间窗要求的目标工作点i的开始、结束时间
U	影响运输过程的相关因素集合
$i, j(\in K)=\left\{\begin{array}{l}(1, 2, \cdots, K), \text { 各目标工作点} \\ 0, \text { 货物升降机}\end{array}\right.$	物体标号
n(∈N)	各运输车
(x_i, y_i)	目标工作点i的位置坐标
$L_{i j}=\sqrt{\left(x_i-y_i\right)^2+\left(y_i-y_j\right)^2}$	目标工作点i和j之间的直线距离
$\theta_{i j}=\left\{\begin{array}{l}1, \text { 目标工作点从} i \text { 到} j \text { 满足顺序要求} \\ 0, \text { 目标工作点从} i \text { 到} j \text { 不满足顺序要求}\end{array}\right.$	顺序要求参数
$\alpha_{i j n}=\left\{\begin{array}{l}1, \text { 运输车} n \text { 经目标工作点} i \text { 到达} j \\ 0, \text { 运输车} n \text { 未经目标工作点} i \text { 到达} j\end{array}\right.$	路径参数
$\beta_{i n}=\left\{\begin{array}{l}1, \text { 运输车} n \text { 经过目标工作点} i \\ 0, \text { 运输车} n \text { 未经过目标工作点} i\end{array}\right.$	到达参数
$t_{i n}=\left\{\begin{array}{l}\text { 运输车} n \text { 到达目标点} i \text { 的时间, } \beta_{i n}=1 \\ 0, \beta_{i n}=0\end{array}\right.$	等待时间
t_m	第m组的总体等待时间

序号	X相对坐标	Y相对坐标	类型
01	-63	319	升降机
02	7	140	升降机
03	7	-206	升降机
1	-77	113	运输点
2	-95	77	运输点
3	-91	-30	运输点
4	-87	-83	运输点
5	0	243	运输点
6	-47	369	运输点
7	-78	370	运输点
8	-40	-351	运输点
9	-45	94	装载点
10	-55	52	装载点
11	-64	-42	装载点
12	-70	-95	装载点
13	28	243	装载点
14	-56	438	装载点
15	-85	406	装载点
16	-30	-368	装载点

算法	实验次数	最优解次数	最优解频率/%	平均收敛代数
改进GWO算法	20	17	85	85.0
遗传算法	20	11	55	112.5

序号	X相对坐标	Y相对坐标	类型
01	-63	319	升降机
02	7	140	升降机
03	7	-206	升降机
1	-77	113	运输点
2	-95	77	运输点
3	-91	-30	运输点
4	-87	-83	运输点
5	0	243	运输点
6	-47	369	运输点
7	-78	370	运输点
8	-40	-351	运输点
9	-202	245	运输点
10	-173	183	运输点
11	-122	109	运输点
12	-109	-591	运输点
13	-45	94	装载点
14	-55	52	装载点
15	-64	-42	装载点
16	-70	-95	装载点
17	28	243	装载点
18	-56	438	装载点
19	-85	406	装载点
20	-30	-368	装载点
21	-161	229	装载点
22	-136	183	装载点
23	-81	160	装载点
24	-75	-402	装载点

算法	实验次数	最优解次数	最优解频率/%	平均收敛代数
改进GWO算法	20	12	60	322.5
遗传算法	20	4	20	350.0

Carrier-based aircraft ammunition support scheduling optimization based on improved grey wolf optimizer algorithm

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