
基于改进深度子Q网络算法的军队建设规划年度计划生成
陈子夷, 杨克巍, 豆亚杰, 姜江, 谭跃进
系统工程理论与实践 ›› 2025, Vol. 45 ›› Issue (5) : 1673-1686.
基于改进深度子Q网络算法的军队建设规划年度计划生成
Annual schedules generation for military construction planning based on improved Deep SubQ-Network algorithm
军队建设规划 / 军队建设项目 / 动态规划 / 多目标优化 / 深度强化学习 {{custom_keyword}} /
military construction planning / military construction projects / dynamic programming / multi-objective optimization / deep reinforcement learning {{custom_keyword}} /
[1] |
纪海涛. 军队建设重大项目群管理研究[J]. 国防, 2019(6): 57-61.
Ji H T. Research on the management of major project clusters for military construction[J]. National Defense, 2019(6): 57-61. |
[2] |
赵得智, 廉振宇, 游光荣. 基于改进RCPSP的军队建设项目中长期规划问题建模与求解[J]. 军事运筹与系统工程, 2021, 35(1): 28-34.
Zhao D Z, Lian Z Y, You G R. Modelling and solving medium and long term planning problems for army construction projects based on improved RCPSP[J]. Military Operations Research and Systems Engineering, 2021, 35(1): 28-34. |
[3] |
Kyle R H, Saber E, Ivan G, et al. Portfolio optimization for defence applications[J]. IEEE Access, 2020, 8: 60152-60178.
|
[4] |
Darya A, Maryam A, Seyed H G. A multi objective-BSC model for new product development project portfolio selection[J]. Expert Systems with Applications, 2020, 162: 113757.
|
[5] |
Xie F, Li H T, Zhe X. Multi-mode resource-constrained project scheduling with uncertain activity cost[J]. Expert Systems with Applications, 2021, 168: 114475.
|
[6] |
Muhammad B, Lukumon O O. Big data with deep learning for benchmarking profitability performance in project tendering[J]. Expert Systems with Applications, 2020, 147: 113194.
|
[7] |
Li J C, Ge B F, Jiang J, et al. High-end weapon equipment portfolio selection based on a heterogeneous network model[J]. Journal of Global Optimization, 2020, 78: 743-761.
|
[8] |
Dou Y J, Zhou Z X, Zhao D L, et al. Weapons system portfolio selection based on the contribution rate evaluation of system of systems[J]. Journal of Systems Engineering and Electronics, 2019, 30(5): 905-919.
|
[9] |
周宇, 姜江, 赵青松, 等. 武器装备体系组合规划的高维多目标优化决策[J]. 系统工程理论与实践, 2014, 34(11): 2944-2954.
Zhou Y, Jiang J, Zhao Q S, et al. Many-objetive optimization and decision-making for portfolio planning of armament system of systems[J]. Systems Engineering — Theory & Practice, 2014, 34(11): 2944-2954. |
[10] |
郭栋, 张迎新, 韩高飞, 等. 武器装备体系规划备选方案生成方法[J]. 指挥控制与仿真, 2020, 42(5): 101-107.
Guo D, Zhang Y X, Han G F, et al. Methods for generating alternative weapons system planning shcemes[J]. Command Control & Sumulation, 2020, 42(5): 101-107. |
[11] |
Ismail M A, Hasan H T, Sondoss E. A military fleet mix problem for high-valued defense assets: A simulation-based optimization approach[J]. Expert Systems with Applications, 2023, 213: 118964.
|
[12] |
林木, 王维平, 王涛, 等. 基于使命能力框架的国防项目组合结构优化方法[J]. 系统工程理论与实践, 2022, 42(10): 2829-2839.
Lin M, Wang W P, Wang T, et al. Optimization method of defense projects portfolio structure based on a mission-capability framework[J]. Systems Engineering — Theory & Practice, 2022, 42(10): 2829-2839. |
[13] |
Jorge D F, José C. The construction process of the synthetic risk model for military ship building projects in brazil[J]. Procedia Computer Science, 2016, 100: 796-803.
|
[14] |
Lee H C, Liu H Y, Teng S Y. Distributed energy strategy using renewable energy transformation in Kinmen island: Virtual power plants that take the military camps as the mainstay[J]. Energy Strategy Reviews, 2022, 44: 100993.
|
[15] |
Clint B S, Damarys A A, Edith M G, et al. Development of a suitable project management approach for projects with parallel planning and execution[J]. Procedia Manufacturing, 2020, 51: 1544-1550.
|
[16] |
de Oliveira L L, de Oliveira Ribeiro C, Qadrdan M. Analysis of electricity supply and demand intra-annual dynamics in brazil: A multi-period and multi-regional generation expansion planning model[J]. International Journal of Electrical Power & Energy Systems, 2022, 137: 107886.
|
[17] |
Smith C B, Acevedo-Acevedo D, Martínez-Guerra E, et al. Developing water resiliency solutions at military installations[J]. Climate Risk Management, 2022, 37: 100451.
|
[18] |
Kettunen J, Lejeune M A. Data-driven project portfolio selection: Decision-dependent stochastic programming formulations with reliability and time to market requirements[J]. Computers & Operations Research, 2022, 143: 105737.
|
[19] |
Salo A, Andelmin J, Oliveira F. Decision programming for mixed-integer multi-stage optimization under uncertainty[J]. European Journal of Operational Research, 2022, 299(2): 550-565.
|
[20] |
邹鑫, 王仁锋, 张立辉, 等. 计及软逻辑的重复性项目离散时间费用权衡及其约束规划模型研究[J]. 中国管理科学, 2022, 30(10): 109-118.
Zou X, Wang R F, Zhang L H, et al. A study of discrete time cost tradeoffs for repetitive projects and their constrained planning models with soft logic[J]. Chinese Journal of Management Science, 2022, 30(10): 109-118. |
[21] |
Kedir N S, Somi S, Fayek A R, et al. Hybridization of reinforcement learning and agent-based modeling to optimize construction planning and scheduling[J]. Automation in Construction, 2022, 142: 104498.
|
[22] |
Shi S, Li J J, Li G H, et al. GPM: A graph convolutional network based reinforcement learning framework for portfolio management[J]. Neurocomputing, 2022, 498: 14-27.
|
[23] |
Fang Z G, Tan T, Yan J Y, et al. Automated portfolio-based strategic asset management based on deep neural image classification[J]. Automation in Construction, 2022, 142: 104481.
|
[24] |
Park H, Sim M K, Choi D G. An intelligent financial portfolio trading strategy using deep q-learning[J]. Expert Systems with Applications, 2020, 158: 113573.
|
[25] |
Asghari V, Wang Y, Biglari A J, et al. Reinforcement learning in construction engineering and management: A review[J]. Journal of Construction Engineering and Management, 2022, 148(11): 03122009.
|
[26] |
Wang S Q, Wang Q K. Effect evaluation of construction engineerization management for military projects[J]. Systems Engineering Procedia, 2012, 3: 351-356.
|
[27] |
Hasan M M, Lwin K, Imani M, et al. Dynamic multi-objective optimisation using deep reinforcement learning: Benchmark, algorithm and an application to identify vulnerable zones based on water quality[J]. Engineering Applications of Artificial Intelligence, 2019, 86: 107-135.
|
[28] |
Zhang Z, Wu Z, Zhang H, et al. Meta-learning-based deep reinforcement learning for multiobjective optimization problems[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(10): 7978-7991.
|
[29] |
Li J H, Zhang Y, Yang X Y, et al. Online portfolio management via deep reinforcement learning with high-frequency data[J]. Information Processing & Management, 2023, 60(3): 103247.
|
[30] |
陈亚强, 穆龙新, 翟光华, 等. 海外油气项目多目标投资组合优化方法[J]. 系统工程理论与实践, 2017, 37(11): 3018-3024.
Chen Y Q, Mu L X, Zhai G H, et al. The multi-objective portfolio optimization method for overseas oil & gas projects[J]. Systems Engineering — Theory & Practice, 2017, 37(11): 3018-3024. |
[31] |
Chagas J B C, Wagner M. A weighted-sum method for solving the bi-objective traveling thief problem[J]. Computers & Operations Research, 2022, 138: 105560.
|
[32] |
Weir T, Johns K. Longitudinal models for project expenditure plans[C]// Proceedings of the 22nd International Congress on Modelling and Simulation, 2017: 695-701.
|
[33] |
Kingma D P. A method for stochastic optimization[J]. ArXiv Preprint, 2014, arXiv: 1412. 6980, 1412: 6980.
|
[34] |
Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks[C]// Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, 2010: 249-256.
|
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