系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (5): 1277-1286.doi: 10.12305/j.issn.1001-506X.2021.05.15

• 系统工程 • 上一篇    下一篇

不确定条件下后装协同保障链优化调度

曾斌1,*(), 张泉先1(), 李厚朴2()   

  1. 1. 海军工程大学管理工程与装备经济系, 湖北 武汉 430033
    2. 海军工程大学导航工程系, 湖北 武汉 430033
  • 收稿日期:2020-06-13 出版日期:2021-05-01 发布日期:2021-04-27
  • 通讯作者: 曾斌 E-mail:zbtrueice@163.com;2312497662@qq.com;lihoupu@126.com
  • 作者简介:曾斌(1970—), 男, 教授, 博士, 主要研究方向为信息管理。E-mail: zbtrueice@163.com|张泉先(1995—), 男, 硕士研究生, 主要研究方向为物流系统建模与仿真。E-mail: 2312497662@qq.com|李厚朴(1985—), 男, 副教授, 博士, 主要研究方向为海上导航。E-mail: lihoupu@126.com
  • 基金资助:
    国家自然科学基金(41771487);湖北省杰出青年科学基金(2019CFA086)

Optimal scheduling for cooperative support chain of logistics and equipment under uncertainty

Bin ZENG1,*(), Quanxian ZHANG1(), Houpu LI2()   

  1. 1. Department of Management Engineering and Equipment Economics, Naval University of Engineering, Wuhan 430033, China
    2. Department of Navigation Engineering, Naval University of Engineering, Wuhan 430033, China
  • Received:2020-06-13 Online:2021-05-01 Published:2021-04-27
  • Contact: Bin ZENG E-mail:zbtrueice@163.com;2312497662@qq.com;lihoupu@126.com

摘要:

后装保障链是联合作战环境下的重要支撑, 针对保障链中的2个重要节点-前进基地和保障基地的资源协调问题, 提出了考虑不确定因素影响的优化模型及基于信息共享的协同保障算法; 为了解决保障数据样本较小情况下的不确定参数估算问题, 利用模糊规划方法把不确定优化模型转化为概率约束模型; 并利用增强ε-约束法来估算多目标Pareto解, 帮助后装指挥人员在难以事先给出权重的情况下选择合适的解决方案; 为了解决规划模型的计算复杂性过大的问题, 设计了嵌入自适应大规模邻域搜索的Memetic算法进行求解。最后通过仿真实验对模型和算法的有效性进行了验证。

关键词: 后装保障链, 优化配置, 不确定性, 多目标规划, Memetic算法

Abstract:

Logistics and equipment support chain is an important support in joint operational environment. In order to solve the resource coordination problem between the two key members of support chain for forward operating base and support base, an optimal model considering the influence of uncertainty factor and its corresponding cooperation support algorithm based on information exchange are proposed. The optimal model under uncertainty is transformed into probabilities constraint model with the fuzzy programming method to solve the problem of uncertain parameter estimation in the case of small data sample problem. Augmented ε-constraint method is adopted to estimate the multi-objective Pareto solutions, which helps the logistics and equipment commander to choice the suitable solution without a priori weight knowledge. Furthermore, a Memetic algorithm with adaptive large neighborhood search heuristic is proposed to face the computation complexity of the programming model. Finally, the simulation experiments show that the models and algorithms are efficient.

Key words: logistics and equipment support chain, optimal configuration, uncertainty, multi-objective programming, Memetic algorithm

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