Research on Multi-Objective Dynamic Scheduling

ZHANG Shuaiju; CAO Jiping; QI Xiaogang; LI Jiahui; LIU Lifang

doi:10.12382/bgxb.2025.0699

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Research on Multi-Objective Dynamic Scheduling

更新时间：2026-03-13

- Research on Multi-Objective Dynamic Scheduling
- Acta Armamentarii (2026)
- 作者机构：
  
  1. 西安电子科技大学数学与统计学院,陕西,西安,710071
  2. 中国人民解放军火箭军工程大学作战保障学院,陕西,西安,710025
  3. 西安市信息网络优化与数学方法重点实验室,陕西,西安,710071
  4. 西安电子科技大学计算机科学与技术学院,陕西西安710071、AdaptiveParticleSwarmOptimization-basedGeneticAlgorithm，APSO-GA、Non-dominatedSortingGeneticAlgorithmII，NSGA-Ⅱ
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0699
  CLC： E92
- Received：29 July 2025，
  
  Online First：13 March 2026，
- 稿件说明：
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张帅举，曹继平，齐小刚，等. 面向战时维修保障的多目标动态调度问题研究[J/OL]. 兵工学报, 2026(2026-03-13). https://doi.org/10.12382/bgxb.2025.0699.

ZHANG S, CAO J P, QI X G, et al. Research on multi-objective dynamic scheduling[J/OL]. Acta Armamentarii, 2026(2026-03-13). https://doi.org/10.12382/bgxb.2025.0699. (in Chinese)
张帅举，曹继平，齐小刚，等. 面向战时维修保障的多目标动态调度问题研究[J/OL]. 兵工学报, 2026(2026-03-13). https://doi.org/10.12382/bgxb.2025.0699. DOI：

ZHANG S, CAO J P, QI X G, et al. Research on multi-objective dynamic scheduling[J/OL]. Acta Armamentarii, 2026(2026-03-13). https://doi.org/10.12382/bgxb.2025.0699. (in Chinese) DOI：

摘要

在现代战场环境下，装备维修保障面临任务频繁到达、资源调度复杂与时间窗约束等挑战，传统调度方法难以满足动态环境下的高效决策需求。针对战时抢修任务中的动态多目标优化问题，以维修任务完成数、维修重要度总和及维修完成后的二次作战时长为优化目标，构建维修小组调度与备件运输路径的联合调度模型，并提出一种基于自适应粒子群融合遗传算法(Adaptive Particle Swarm Optimization-based Genetic Algorithm，APSO-GA)。该算法融合全局搜索与局部优化机制，引入动态重调度策略，可在任务或资源状态变化时快速完成调度调整。仿真实验结果表明，与非支配排序遗传算法(Non-dominated Sorting Genetic Algorithm II，NSGA-Ⅱ)相比，APSO-GA在3个优化目标上平均提升4.6%、4.8%、11%，且各目标标准差降低20%以上，展现出更优的稳定性与可靠性。

Abstract

In modern battlefield environments

equipment maintenance support faces challenges such as frequent task arrivals

complex resource scheduling

and strict time window constraints. Traditional scheduling methods often fail to meet the requirements for efficient decision-making in such dynamic settings. To address the dynamic multi-objective optimization problem in wartime emergency repair tasks

a joint scheduling model for maintenance team assignment and spare parts transportation routing

aiming to optimize three objectives: the number of completed repair tasks

the total importance of completed tasks

and the remaining operational time after repair. Anadaptive particle swarm optimization-based genetic algorithm (APSO-GA) is developed

which integrates global exploration with local exploitation and incorporates a dynamic rescheduling mechanism to quickly adapt to changes in task or resource states. Simulation results demonstrate that

compared with thenon-dominated sorting genetic algorithm II (NSGA-II)

APSO-GA achieves average improvements of 4.6%

4.8%

and 11% across the three objectives

while reducing the standard deviation of each objective by more than 20%

indicating superior stability and reliability.

关键词

Keywords

references

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