基于状态预测的备件规划模型

基于状态预测的备件规划模型为西安交通大学针对航改燃机备件管理开发的基于状态预测的备件规划模型，该算法基于python环境进行开发。该算法模型通过建立备件的使用状态和故障率模型，利用公式法预测出未来订货期内的所需备件数量，并优化备件周转计划，使订货周期尽可能梯度化，进而使备件库存量尽可能满足需求且满足偶发故障率。

The spare parts planning model based on condition prediction is a specialized model developed by Xi'an Jiaotong University for aero-derivative gas turbine spare parts management. This algorithmic model is implemented in the Python environment. By establishing the usage status and failure rate models of spare parts, it uses the formula-based method to predict the required quantity of spare parts within the future ordering period, optimizes the spare parts turnover plan to make the ordering cycles as graded as possible, thereby ensuring that the spare parts inventory can meet the actual demand while meeting the requirements of occasional failure rates.

搜集汇总

背景与挑战

背景概述

该数据集是西安交通大学开发的用于航改燃机备件管理的规划模型。它基于Python环境，通过建立备件状态与故障率模型，预测未来订货期的备件需求并优化库存周转计划，旨在满足需求的同时控制偶发故障率。