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兵工学报 ›› 2024, Vol. 45 ›› Issue (7): 2442-2450.doi: 10.12382/bgxb.2023.0337

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伴随维修资源配置与任务调度的多目标联合优化

刘盛钰1, 齐小刚1,*(), 刘立芳2   

  1. 1 西安电子科技大学 数学与统计学院, 陕西 西安 710071
    2 西安电子科技大学 计算机学院, 陕西 西安 710071
  • 收稿日期:2023-04-14 上线日期:2023-07-28
  • 通讯作者:
  • 基金资助:
    国家自然科学基金项目(61877067)

Multi-objective Joint Optimization of Resource Allocation and Task Scheduling for Accompanying Repair

LIU Shengyu1, QI Xiaogang1,*(), LIU Lifang2   

  1. 1 School of Mathematics and Statistics, Xidian University, Xi’an 710071, Shaanxi, China
    2 School of Computer Science and Technology, Xidian University, Xi’an 710071, Shaanxi, China
  • Received:2023-04-14 Online:2023-07-28

摘要:

现代战争作战节奏快、横跨地域广,对伴随维修保障模式提出了更高的要求。如何在复杂战场中实现资源配置与任务调度的流程整合,充分发挥伴随维修系统效能,已经成为当今装备维修保障的迫切需求与发展方向。综合考虑多中心、开放式、多修复状态、时间窗、非遍历、容量限制等因素,首次提出随时补货的思路,将地区危险系数与维修组当前成本相结合,以维修组创造维修效益最大化、承担风险成本最小化为目标,建立更完善的数学模型。针对上述问题进行相应的编码,改进多目标人工蜂群(Multi Objective Artificial Bee Colony,MOABC)算法,提出求解质量、收敛速度良好的多目标人工蜂群-多记忆蜜源(Multi Objective Artificial Bee Colony for Memorizing Multiple Honey Sources per Bee, MOABC-MMHS)算法;针对小部分极端值影响均值的问题改进覆盖率(Coverage,C)指标的使用方式,以频率的方式对其进行展示;通过仿真实验与多指标评价验证模型和算法的科学性,实现了伴随维修资源配置与任务调度的联合优化。研究结果表明,上述模型与算法能够为伴随维修保障提供相应的辅助决策。

关键词: 装备维修保障, 伴随维修, 资源配置, 任务调度, 多目标优化

Abstract:

The modern war has a fast pace and covers a wide area, which puts forward higher requirements for the accompanying repair support mode. Integrate the process of resource allocation and task scheduling in a complex battlefield to give full play to the effectiveness of accompanying repair system has become the urgent demand and development direction of equipment maintenance support. Comprehensively considering the factors of multi-center, open, multi-repair state, time window, non-traversal, capacity limitation and so on, an idea of replenishing goods at any time is put forward for the first time, which combines the regional risk coefficient with the current cost of the maintenance teams. A more perfect mathematical model is established to maximize the maintenance benefit and minimize the risk cost. For the above problems, the multi-objective artificial bee colony (MOABC) algorithm is improved, a multi-objective artificial bee colony algorithm for memorizing multiple honey sources per bee is proposed. The proposed algorithm shows good performance in terms of solution quality and convergence speed. Then, aiming at the problem that a small number of extreme values affect the mean, the use of coverage rate indicator is improved and displayed in the way of frequency. Finally, the scientificness of the model and algorithm is verified through the simulation experiments and the evaluation of multiple indicators, and the joint optimization of resource allocation and task scheduling for accompanying repair is realized. The study shows that the above model and algorithm are able to provide the appropriate assistant decision-making for accompanying repair support.

Key words: equipment maintenance support, accompanying repair, resource allocation, task scheduling, multi-objective optimization

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