Optimization algorithm for ammunition support operation scheduling of carrier-borne aircraft

Shaohui ZHANG; Shun LIU; Yafei LI; Zhao JIN; Yuanyuan JIN; Shaocan WANG; Jianbo ZHAO; Mingliang XU

doi:10.7527/S1000-6893.2023.28485

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2023 , Vol. 44 >Issue 20: 228485 - 228485

DOI: https://doi.org/10.7527/S1000-6893.2023.28485

Solid Mechanics and Vehicle Conceptual Design

Optimization algorithm for ammunition support operation scheduling of carrier-borne aircraft

Shaohui ZHANG ,
Shun LIU ,
Yafei LI ,
Zhao JIN ,
Yuanyuan JIN ,
Shaocan WANG ,
Jianbo ZHAO ,
Mingliang XU

Expand

^1.School of Computer Science and Artificial Intelligence，Zhengzhou University，Zhengzhou 　450001，China
^2.School of Network Engineering，Zhoukou Normal University，Zhoukou 　466001，China
^3.Intelligent Cluster System Engineering Research Center of the Ministry of Education，Zhengzhou 　450001，China
^4.National Supercomputing Center in Zhengzhou，Zhengzhou 　450001，China
^5.The 713 Research Institute，China Shipbuilding Industry Corporation，Zhengzhou 　450015，China
^6.School of Economics and Management，Jiangsu University of Science and Technology，Zhenjiang 　212100，China

E-mail： iexumingliang@zzu.edu.cn

Received date: 2023-01-09

Revised date: 2023-03-07

Accepted date: 2023-03-17

Online published: 2023-03-17

Supported by

National Natural Science Foundation of China(62036010);National Science Fund for Excellent Young Scholars(61822701);National Natural Science Foundation of China(62172457);China Postdoctoral Science Foundation(2018M630836);Excellent Youth Fund of Henan Natural Science Foundation(202300410378);Science and Technology Project of Henan Province(212102210098);Training Program for Young Backbone Teachers of Henan Higher Education Institutions(2020GGJS215);Key Research Project of Henan Higher Education Institutions(22A520051)

Fold

Abstract

Considering the highly dynamic and multi-stage characteristics of carrier-based aircraft ammunition support operations， we establish an optimization model based on the theory of swarm intelligence optimization and the method of flexible flow-shop scheduling. Firstly， the ammunition transfer support operation problem is reduced to a flexible flow-shop scheduling problem considering the delivery time of work-pieces， and heuristic rules are introduced to construct the model of Ammunition Transport Support for Carrier-Borne Aircraft （ATSCA）， which takes into account the requirements of efficiency and robustness. Secondly， combined with the practice of ammunition dispatching operation， a Greedy Local Search Genetic Algorithm with Dual-Level Coding （GLSGA-DC） is designed to solve the ATSCA model with the goal of minimizing the maximum ammunition transfer time. The simulation results show that the GLSGA-DC algorithm has obtained the optimal values in the Mean Value （AVG）， Relative Deviation （RD） and other indicators in benchmark tests and multiple groups of experiments in real ammunition transfer operations， demonstrating the effectiveness and robustness of the ATSCA model and algorithm in real ammunition support operations for carrier-borne aircraft.

Key words： carrier-borne aircraft; ammunition support operation; flexible job-shop scheduling; scheduling and optimization; simulation

Cite this article

Shaohui ZHANG , Shun LIU , Yafei LI , Zhao JIN , Yuanyuan JIN , Shaocan WANG , Jianbo ZHAO , Mingliang XU . Optimization algorithm for ammunition support operation scheduling of carrier-borne aircraft[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(20) : 228485 -228485 . DOI: 10.7527/S1000-6893.2023.28485

References

1	李亚飞，吴庆顺，徐明亮，等. 基于强化学习的舰载机保障作业实时调度方法［J］. 中国科学：信息科学， 2021， 51（2）： 247-262.
	LI Y F， WU Q S， XU M L， et al. Real-time scheduling for carrier-borne aircraft support operations： A reinforcement learning approach［J］. Scientia Sinica Informationis， 2021， 51（2）： 247-262 （in Chinese）.
2	吕培，陈伟超，张权，等. 群组运动驱动的舰船舱室空间布局设计与优化［J］. 计算机辅助设计与图形学学报， 2021， 33（9）： 1337-1348.
	LYU P， CHEN W C， ZHANG Q， et al. The design and optimization of ship cabin space layout based on crowd simulation［J］. Journal of Computer-Aided Design & Computer Graphics， 2021， 33（9）： 1337-1348 （in Chinese）.
3	薛均晓，孔祥燕，郭毅博，等. 基于深度强化学习的舰载机动态避障方法［J］. 计算机辅助设计与图形学学报， 2021， 33（7）： 1102-1112.
	XUE J X， KONG X Y， GUO Y B， et al. Dynamic obstacle avoidance method for carrier aircraft based on deep reinforcement learning［J］. Journal of Computer-Aided Design & Computer Graphics， 2021， 33（7）： 1102-1112 （in Chinese）.
4	田德红，何建敏，齐洁，等. 航空弹药动态调运决策优化建模与仿真研究［J］. 西北工业大学学报， 2018， 36（6）： 1236-1242.
	TIAN D H， HE J M， QI J， et al. Research on the modeling and simulation of optimal dynamic aerial ammunition scheduling and transportation［J］. Journal of Northwestern Polytechnical University， 2018， 36（6）： 1236-1242 （in Chinese）.
5	魏天宇，张孝虎，雷宇，等. 基于混合算法的战时机载弹药保障任务效能评估［J］. 舰船电子工程， 2019， 39（9）： 140-145.
	WEI T Y， ZHANG X H， LEI Y， et al. Effectiveness evaluation of wartime airborne ammunition support task based on hybrid algorithm［J］. Ship Electronic Engineering， 2019， 39（9）： 140-145 （in Chinese）.
6	陶俊权，苏析超，韩维，等. 基于EDA算法的航母弹药调度优化研究［J］. 兵器装备工程学报， 2022， 43（5）： 125-131.
	TAO J Q， SU X C， HAN W， et al. Study of aircraft carrier ammunition scheduling optimization based on EDA algorithm［J］. Journal of Ordnance Equipment Engineering， 2022， 43（5）： 125-131 （in Chinese）.
7	侯德飞，田德红，林聪仁，等. 基于博弈的多目标弹药调度策略优化研究［J］. 南京航空航天大学学报， 2019， 51（6）： 841-847.
	HOU D F， TIAN D H， LIN C R， et al. Optimization of multi-objective ammunition scheduling strategies based on game theory［J］. Journal of Nanjing University of Aeronautics & Astronautics， 2019， 51（6）： 841-847 （in Chinese）.
8	SMITH W. Scheduling stored combat load retrieval［J］. Journal of Defense Analytics and Logistics， 2018， 2（2）： 80-93.
9	YU L F， ZHU C， LI W J， et al. Research on the aviation support groups scheduling for multi-wave aircrafts based on the ammunition transportation［C］∥2021 IEEE International Conference on Unmanned Systems （ICUS）. Piscataway： IEEE Press， 2021： 383-389.
10	RYAN J C， BANERJEE A G， CUMMINGS M L， et al. Comparing the performance of expert user heuristics and an integer linear program in aircraft carrier deck operations［J］. IEEE Transactions on Cybernetics， 2014， 44（6）： 761-773.
11	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. Advances in carrier-based aircraft deck operation scheduling［J］. Systems Engineering-Theory & Practice， 2017， 37（1）： 49-60 （in Chinese）.
12	王能建，刘钦辉，李江. 舰载机出动回收能力仿真研究［M］. 北京：科学出版社， 2018： 166-180.
	WANG N J， LIU Q H， LI J. Simulation study on recovery capability of carrier-based aircraft［M］. Beijing： Science Press， 2018： 166-180 （in Chinese）.
13	RYAN J， CUMMINGS M， ROY N， et al. Designing an interactive local and global decision support system for aircraft carrier deck scheduling［C］∥Proceedings of the Infotech@Aerospace 2011. Reston： AIAA， 2011.
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.
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.
16	李俊青，杜宇，田杰，等. 带运输资源约束柔性作业车间调度问题的人工蜂群算法［J］. 电子学报， 2021， 49（2）： 324-330.
	LI J Q， DU Y， TIAN J， et al. An artificial bee colony algorithm for flexible job shop scheduling with transportation resource constraints［J］. Acta Electronica Sinica， 2021， 49（2）： 324-330 （in Chinese）.
17	越民义，李荣珩. 组合优化导论［M］. 2版. 北京：科学出版社， 2014.
	YUE M Y， LI R H. Introduction to combinatorial optimization［M］. 2nd edition. Beijing： Science Press， 2014 （in Chinese）.
18	高亮，张国辉，王晓娟. 柔性作业车间调度智能算法及其应用［M］. 武汉：华中科技大学出版社， 2012.
	GAO L， ZHANG G H， WANG X J. Intelligent algorithm for flexible job shop scheduling and its application［M］. Wuhan： Huazhong University of Science and Technology Press， 2012 （in Chinese）.
19	PAN Y X， GAO K Z， LI Z W， et al. Improved meta-heuristics for solving distributed lot-streaming permutation flow shop scheduling problems［J］. IEEE Transactions on Automation Science and Engineering， 2023， 20（1）： 361-371.
20	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）： 1-15.
21	崔琪，吴秀丽，余建军. 变邻域改进遗传算法求解混合流水车间调度问题［J］. 计算机集成制造系统， 2017， 23（9）： 1917-1927.
	CUI Q， WU X L， YU J J. Improved genetic algorithm variable neighborhood search for solving hybrid flow shop scheduling problem［J］. Computer Integrated Manufacturing Systems， 2017， 23（9）： 1917-1927 （in Chinese）.
22	BACK T. Evolutionary algorithm in theory and practice［M］. New York： Oxford University Press， 1996： 129-130.
23	MONTGOMERY D C. Design and analysis of experiments［M］. New York： John Wiley & Sons， 2004.
24	CARLIER J， NERON E. An exact method for solving the multi-processor flow-shop［J］. RAIRO - Operations Research， 2000， 34（1）： 1-25.
25	DRISS I， MOUSS K N， LAGGOUN A. A new genetic algorithm for flexible job-shop scheduling problems［J］. Journal of Mechanical Science & Technology， 2015， 29（3）： 1273-1281.
26	张洪亮，刘建伟，马羚，等. 基于离散粒子群的舰载机弹药调度［J］. 舰船电子工程， 2021， 41（4）： 146-149.
	ZHANG H L， LIU J W， MA L， et al. Ammunition scheduling of carrier based aircraft based on discrete particle swarm optimization［J］. Ship Electronic Engineering， 2021， 41（4）： 146-149 （in Chinese）.
27	CUI Z， GU X S. An improved discrete artificial bee colony algorithm to minimize the makespan on hybrid flow shop problems［J］. Neurocomputing， 2015， 148： 248-259.
28	李经，孙哲，李梦龙，等. 舰载机保障作业调度决策研究［J］. 舰船电子工程， 2018， 38（12）： 165-168， 184.
	LI J， SUN Z， LI M L， et al. Research on carrier-based aircraft deck operation scheduling［J］. Ship Electronic Engineering， 2018， 38（12）： 165-168， 184 （in Chinese）.
29	薛均晓，徐明亮，李亚飞，等. 面向航空母舰电子显灵板的多智能体建模技术研究进展［J］. 计算机辅助设计与图形学学报， 2021， 33（10）： 1475-1485.
	XUE J X， XU M L， LI Y F， et al. Research progress of multi-agent technology for aircraft carrier electronic display panel［J］. Journal of Computer-Aided Design & Computer Graphics， 2021， 33（10）： 1475-1485 （in Chinese）.
30	王华，韩璐，楚世理，等. 基于Frenet标架下三维元胞自动机的航母舰载机集群运动建模［J］. 计算机辅助设计与图形学学报， 2018， 30（9）： 1719-1727.
	WANG H， HAN L， CHU S L， et al. Shipboard aircraft swarm modeling using a 3D cellular automata model under the frenet frame［J］. Journal of Computer-Aided Design & Computer Graphics， 2018， 30（9）： 1719-1727 （in Chinese）.
31	王可，徐明亮，李亚飞，等. 一种面向航空母舰甲板运动状态预估的鲁棒学习模型［J］. 自动化学报， 2021， doi： 10.16383/j.aas.c210664 .
	WANG K， XU M L， LI Y F， et al. A robust learning model for deck motion prediction of aircraft carrier［J］. Acta Automatica Sinica， 2021， doi： 10.16383/j.aas.c210664 （in Chinese）.
32	万兵，苏析超，郭放，等. 不确定性工时下甲板作业的前摄性鲁棒调度［J］. 航空学报， 2022， 43（12）： 385-402.
	WAN B， SU X C， GUO F， et al. Proactive robust scheduling of aircraft carrier flight deck operations with uncertain activity durations［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（12）： 385-402 （in Chinese）.
33	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.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References