Joint Optimization of Equipment Maintenance Decision-making and Task Scheduling under Fixed-point Maintenance Mode

LIANG Xiaolong; QI Xiaogang; MO Lina; LI Jiahui; LIU Lifang

doi:10.12382/bgxb.2024.1125

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Joint Optimization of Equipment Maintenance Decision-making and Task Scheduling under Fixed-point Maintenance Mode

更新时间：2026-04-24

- Joint Optimization of Equipment Maintenance Decision-making and Task Scheduling under Fixed-point Maintenance Mode
- Acta Armamentarii Vol. 47, Issue 2, Pages: 241125(2026)
- 作者机构：
  
  西安电子科技大学数学与统计学院，陕西西安 710071
  西安市信息网络优化与数学方法重点实验室，陕西西安 710071
  西安电子科技大学计算机科学与技术学院，陕西西安 710071
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2024.1125
  CLC： E92
- Received：16 December 2024，
  
  Online First：25 December 2025，
  
  Published：28 February 2026
- 稿件说明：
移动端阅览
LIANG Xiaolong, QI Xiaogang, MO Lina, et al. Joint Optimization of Equipment Maintenance Decision-making and Task Scheduling under Fixed-point Maintenance Mode[J]. Acta Armamentarii, 2026, 47(2): 241125.
DOI：

LIANG Xiaolong, QI Xiaogang, MO Lina, et al. Joint Optimization of Equipment Maintenance Decision-making and Task Scheduling under Fixed-point Maintenance Mode[J]. Acta Armamentarii, 2026, 47(2): 241125. DOI： 10.12382/bgxb.2024.1125.

摘要

在复杂集中维修环境下，如何快速决策与任务调度流程整合、相关模型建立与求解，已经逐渐成为装备维修保障的迫切需求。综合定点维修模式下的装备维修具有维修模式众多、维修约束复杂、维修决策情况繁琐等问题，考虑多维修模式、时间冲突、备件数量、维修时间、优先级等因素，提出串换件决策，与选择性维修决策结合，将维修装备的二次使用时间与优先级结合作为维修收益，以有限时间下的维修收益、维修装备数量最大化作为目标，建立更完善的数学模型。针对上述问题设计相应的编码与解码方式，提出自适应混合粒子群遗传算法（Self-adaptive Hybrid Particle Swarm Optimization and Genetic Algorithm，SHPSO-GA），通过设计仿真实验与评价指标验证模型与算法合理性。实验结果表明，SHPSO-GA在10组案例中较其他算法Pareto最优解平均提升4.2%，算法收敛速度提高65.4%～83.7%，有效解决了定点模式下维修决策中时间、资源冲突问题，为战场环境下装备快速修复与任务调度提供了高鲁棒性解决方案。

Abstract

In complex centralized maintenance environments

how to quickly integrate the decision-making and task scheduling processes

as well as establish and solve the related models

has gradually become an urgent requirement for equipment maintenance support. The equipment maintenance under the comprehensive fixed-point maintenance mode has problems such as numerous maintenance modes

complex maintenance constraints

and cumbersome maintenance decision-making. In consideration of factors such as multiple maintenance modes

time conflicts

spare parts quantity

maintenance time

and priority

a decision of serial replacement parts is proposed. The decision of serial replacement parts is combined with selective maintenance decision

and the combination of the secondary use time and the priority of maintenance equipment is taken as maintenance benefits. A more comprehensive mathematical model is established to maximize the maintenance benefits and the number of maintenance equipment within a limited time. The corresponding encoding and decoding methods are designed to address the above issues

and a self-adaptive hybrid particle swarm optimization and genetic algorithm(SHPSO-GA)is proposed. The rationality of the model and algorithm is verified by simulation experiment and evaluation indicators. Experimental results shown that SHPSO-GA improves the average Pareto optimal solution by 4.2% in 10 cases compared to other algorithms

and increases the convergence speed by 65.4%-83.7%. It effectively solves the conflicts of time and resource in maintenance decision-making under fixed-point mode

providing a highly robust solution for rapid equipment repair and task scheduling in battlefield environments.

关键词

Keywords

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