北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (11): 2056-2068.doi: 10.13700/j.bh.1001-5965.2019.0581

• 论文 • 上一篇    下一篇

基于边际-人工蜂群算法的舰载机机群出动保障人员配置-调度联合优化方法

苏析超1, 伍恒2, 崔荣伟1, 张勇1   

  1. 1. 海军航空大学, 烟台 264001;
    2. 海军研究院, 北京 100191
  • 收稿日期:2019-11-13 发布日期:2020-12-01
  • 通讯作者: 苏析超 E-mail:suxich@126.com
  • 作者简介:苏析超,男,博士,讲师。主要研究方向:舰载航空保障工程。
  • 基金资助:
    国家自然科学基金(61671462);泰山学者建设工程;武器装备预先研究项目

Joint optimization method for carrier-based aircraft fleet sortie support personnel configuration and scheduling based on marginal-ABC algorithm

SU Xichao1, WU Heng2, CUI Rongwei1, ZHANG Yong1   

  1. 1. Naval Aviation University, Yantai 264001, China;
    2. Naval Research Academy, Beijing 100191, China
  • Received:2019-11-13 Published:2020-12-01

摘要: 保障人员配置和保障作业调度是舰载机机群出动保障任务决策的2项核心内容。针对复杂甲板作业约束条件下保障人员配置-调度联合优化的实际问题,首先,系统分析舰载机机群出动保障流程约束、出动时限约束、保障人员约束、保障设备约束、工位空间约束和资源供给能力约束。其次,以保障人员数量和负载方差和最小化为优化目标,建立了混合整数规划模型,进而提出了基于边际-人工蜂群(ABC)算法的两层优化决策架构。上层决策模型基于边际优化算法对保障人员配置方案进行迭代优化,下层决策模型采用改进的双向人工蜂群算法对舰载机机群出动保障任务调度进行优化。最后,通过典型算例验证了所提模型和两层优化机制的可行性与有效性。

关键词: 机群出动保障, 保障人员配置, 保障调度, 边际优化, 人工蜂群(ABC)算法

Abstract: Support personnel configuration and scheduling for support operations are two core contents of decision making for the carrier-based aircraft sortie support, and the practical problem of joint optimization for support personnel configuration and scheduling under complex constraints within flight deck operations is studied. Firstly, the precedence relations constraints, time-limit constraint for sortie, support personnel constraints, support equipment constraints, workstation space constraint and supply capacity constraint are analyzed systematically. Secondly, with the optimization objectives of minimizing the total number of support personnel and sum of load variance, a mixed integer programming model is established. Thirdly, a bi-level optimized decision framework based on marginal-Artificial Bee Colony (ABC) algorithm is designed. The upper decision model is built for optimizing the support personnel configuration with the marginal algorithm, and the lower decision model is built for optimizing the schedule for the aircraft fleet sortie support mission with the improved double-justified artificial bee colony algorithm. Finally, the feasibility and effectiveness of the proposed model and bi-level optimized decision mechanism are verified by a typical aircraft fleet sortie support case.

Key words: sortie support of aircraft fleet, support personnel configuration, support scheduling, marginal optimization, Artificial Bee Colony (ABC) algorithm

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