季刚, 余涛, 谭美. 基于马尔科夫过程的舰船维修船坞数量需求评估方法研究[J]. 中国舰船研究, 2022, 18(X): 1–6. doi: 10.19693/j.issn.1673-3185.02780
引用本文: 季刚, 余涛, 谭美. 基于马尔科夫过程的舰船维修船坞数量需求评估方法研究[J]. 中国舰船研究, 2022, 18(X): 1–6. doi: 10.19693/j.issn.1673-3185.02780
JI G, YU T, TAN M. Navy maintenance dock demand evaluation method based on Markov process[J]. Chinese Journal of Ship Research, 2022, 18(X): 1–6. doi: 10.19693/j.issn.1673-3185.02780
Citation: JI G, YU T, TAN M. Navy maintenance dock demand evaluation method based on Markov process[J]. Chinese Journal of Ship Research, 2022, 18(X): 1–6. doi: 10.19693/j.issn.1673-3185.02780

基于马尔科夫过程的舰船维修船坞数量需求评估方法研究

Navy maintenance dock demand evaluation method based on Markov process

  • 摘要:
      目的  保障资源预测一直是舰船综合保障的重点和难点,针对舰船维修船坞需求,提出一种支持船坞数量需求评估的模型和方法。
      方法  基于马尔科夫过程,通过分析舰船船坞使用系统特征,建立舰船维修船坞数量需求的马尔科夫数学模型和评价指标,并通过案例分析验证评估方法的有效性。
      结果  算例分析表明,评估模型较好反映了舰船维修需求和船坞数量对系统状态的影响,评价指标反映的最优船坞数量与工程实际吻合性好。
      结论  研究表明,提出的维修船坞数量需求评估模型,能较好地反映物理过程和工程实际特点,可为决策分析提供理论支持,具有较好的实践意义。

     

    Abstract:
      Objective  The prediction of supporting resources has always been a focus and difficulty of naval ship comprehensive support. In this paper, a prediction model and method are proposed for the support needs of naval ship maintenance docks.
      Method  The Markov process is introduced and applied to the prediction of ship dock demand. By analyzing the system characteristics of ship dock use, a Markov forecasting model and evaluation index of dock demand are established, and the validity of the prediction method is verified by case analysis.
      Results  The calculation example shows that the prediction model can better reflect the influence of ship maintenance intensity and the number of docks on the system state, and the optimal dock requirements reflected by the evaluation index are in good agreement with the actual engineering.
      Conclusion  The prediction model of ship repair dock demand proposed in this paper can better reflect the physical process and engineering practice, and provide theoretical support for decision analysis, giving it good practical significance.

     

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