满足任务要求的航材库存限额确定方法

针对航材需求不确定性大、保障经费紧张导致难以全面充分满足航材保障需求的问题,提出了航空兵团日常训练任务航材需求量和外地驻训任务航材携行量的确定方法.以满足日常训练任务的航材需求量为库存下限,满足日常训练任务和外地驻训任务的航材需求量为库存上限,确定了满足任务要求的航材库存限额标准,为实现航材精确保障提供了决策依据.最后结合实例验证了方法的可行性和有效性.     

基于战役的多机种战伤备件需求模型

为了合理储备战时航材备件,通过分析战时故障备件的需求特点,改进了传统的单机故障备件需求模型.根据多机种协同作战任务的不同,引入备件工作运行比的概念,建立了基于作战任务的多机种故障备件需求模型.为解决多机种协同作战时的保障资源配置问题提供了思路和方法.     

基于AFSA-RS的废旧航材可回收性判定研究

针对当前废旧航材回收处理工作存在的难题,在分析影响废旧航材回收因素的基础上,利用人工鱼群算法对粗糙集理论进行改进,将离散化过程转化为目标寻优问题,通过寻优对离散区间进行有效合并,从而得到较少的离散区间,以解决离散化过程中由于区间分割不当造成的病态问题,最终得到一种科学准确判定废旧航材是否值得回收的方法,提高了航材回收工作的科学性,减少了军费资金投入.     

基于超效率DEA的战役仓库航材保障效能评估研究

战役仓库航材保障效能评估是提升空军航材保障水平的重要手段.结合战役仓库航材保障的目标任务,建立了战役仓库航材保障效能评估的指标体系;运用超效率DEA方法构建了战役仓库航材保障效能评估模型,并运用该模型得到了15个战役仓库航材保障效能评估的结果和最终排序;最后以1号战役仓库为例,提出了增强其航材保障效能的改进措施,运行结果表明,改进措施切实有效,能够为提高战役仓库航材保障效能提供科学依据.     

正交试验法在航材中修费优化分配中的应用

空军航材中修费分配的合理与否,直接影响着航材保障的整体效果.为了合理地分配航材中修费,本文建立了航材中修费的优化分配模型.但此分配模型的目标函数复杂,如用常规方法求解则非常繁琐,本文根据其特点利用正交试验方法进行求解取得了较好的效果.     

空军消耗性航材储备优化模型研究

针对我军装备改型多以及部分消耗性航材长期储存容易变质失效的实际借鉴EOQ模型的思想,构建了消耗性航材储备模型.模型以总储备经费最小为目标,综合考虑了航材使用优先级以及航材储备中产生的各项费用,并从理论上证明了模型具有全局最优解.最后通过算例说明方法可行.     

基于二元语义的高原地区航材配送中心选址决策研究

针对高原地区航材配送中心选址决策特点,构建了航材配送中心选址指标体系,提出了基于二元语义的群决策选址方法,给出了基于二元语义群决策的航材配送中心选址步骤.通过实例得出了备选点的综合评价值,证明了方法的有效性.     

基于二元语义的高原地区航材配送中心选址决策研究

针对高原地区航材配送中心选址决策特点,构建了航材配送中心选址指标体系,提出了基于二元语义的群决策选址方法,给出了基于二元语义群决策的航材配送中心选址步骤.通过实例得出了备选点的综合评价值,证明了该方法的有效性.     

基于灰色关联分析与PLS-LSSVM的航材备件需求预测模型

航材备件是保障航空装备日常训练和作战正常使用的重要影响因素,针对部分航材备件样本数据量少,影响因素多且复杂多变,预测结果与装备系统完好性要求偏差较大等问题.建立基于灰色关联分析(GRA)与偏最小二乘(PLS)及最小二乘向量机(LSSVM)相结合的航材备件预测模型,采集某无人机航材备件数据,通过对统计数据进行灰色关联分析...     

基于机群完好率的航材库存优化模型

针对航空兵部队航材配置不合理导致装备完好率不高的问题,分析了机群完好率与单机使用可用度之间的关系,建立了单机使用可用度及航材库存费用计算模型.以机群完好率为约束,以航材库存费用最低为目标,建立了基于机群完好率的航材库存优化模型,并给出了运用边际分析方法求解模型最优解的具体步骤.最后结合算例验证了模型的有效性.     

一种基于随机森林的备件预测模型研究

对舰船零部件发生故障问题进行故障诊断,并对故障诊断结果进行分析,建立舰船零部件备件需求模型,给出零部件之间的发生故障概率的关系与备件需求特征;将随机森林回归原理应用到了舰船零部件的备件需求预测领域,构建了基于随机森林的预测模型,以及预测结果准确率的评价。用诊断结果数据对算法进行验证,结果表明,将随机森林算法运用到舰船的备件预测领域可以为舰船装备在一次海上任务期内备件配置问题提供参考价值。     

基于对故障规律统计分析的汽车备件需求预测

汽车备件的需求与汽车故障紧密相关,文章介绍了一种在对汽车故障进行统计分析并确定其分布规律的基础上预测备件需求的方法,预测中需要结合整车保有量的历史数据以及故障与备件的对应表。用统计的方法对某型客车的故障信息进行分析,认为故障的规律可用四种典型的分布进行描述。实例验证了这种方法的准确性高于传统方法,并且在计算机的辅助下可以方便操作。     

A two-step method for forecasting spare parts demand using information on component repairs

Forecasting spare parts demand is notoriously difficult, as demand is typically intermittent and lumpy. Specialized methods such as that by Croston are available, but these are not based on the repair operations that cause the intermittency and lumpiness of demand. In this paper, we do propose a method that, in addition to the demand for spare parts, considers the type of component repaired. This two-step forecasting method separately updates the average number of parts needed per repair and the number of repairs for each type of component. The method is tested in an empirical, comparative study for a service provider in the aviation industry. Our results show that the two-step method is one of the most accurate methods, and that it performs considerably better than Croston’s method. Moreover, contrary to other methods, the two-step method can use information on planned maintenance and repair operations to reduce forecasts errors by up to 20%. We derive further analytical and simulation results that help explain the empirical findings.     

Failure replacement and preventive maintenance spare parts ordering policy

This paper addresses inventory policy for spare parts, when demand for the spare parts arises due to regularly scheduled preventive maintenance, as well as random failure of units in service. A stochastic dynamic programming model is used to characterize an ordering policy which addresses both sources of demand in a unified manner. The optimal policy has the form (s(k),S(k)), where k is the number of periods until the next scheduled preventive maintenance operation. The nature of the (s(k),S(k)) policy is characterized through numeric evaluation. The efficiency of the optimal policy is evaluated, relative to a simpler policy which addresses the failure replacement and preventive maintenance demands with separate ordering policies.     

备件品种选择的rough方法

分析了影响备件储备品种选择的重要性、可更换性、消耗性、获得难度和经济性等五个因素,利用粗集方法,按条件属性的不同顺序对备件储备品种的选择规则进行了约简,根据工程实践,从中选择一个更加合理的约简结果作为最终的备件储备品种的选择规则,并得到了相应的决策算法.该方法能够克服各影响因素在备件品种选择过程中的影响不足或影响过强等弱点,具有简单、操作方便等特点,为导弹武器装备备件保障部门选择导弹备件品种提供了一种新的理论依据.     

Optimal lateral transshipment policies for a two location inventory problem with multiple demand classes

We consider an inventory model for spare parts with two stockpoints, providing repairable parts for a critical component of advanced technical systems. As downtime costs for these systems are expensive, ready–for–use spare parts are kept in stock to be able to quickly respond to a breakdown of a system. We allow for lateral transshipments of parts between the stockpoints upon a demand arrival. Each stockpoint faces demands from multiple demand classes. We are interested in the optimal lateral transshipment policy. There are three ways in which a demand can by satisfied: from own stock, via a lateral transshipment, or via an emergency procedure. Using stochastic dynamic programming, we characterize and prove the structure of the optimal policy, that is, the policy for satisfying the demands which minimizes the average operating costs of the system. This optimal policy is a threshold type policy, with state-dependent thresholds at each stockpoint for every demand class. We show a partial ordering in these thresholds in the demand classes. In addition, we derive conditions under which the so-called hold back and complete pooling policies are optimal, two policies that are often assumed in the literature. Furthermore, we study several model extensions which fit in the same modeling framework.     

A stochastic model for joint spare parts inventory and planned maintenance optimisation

Spare parts demands are usually generated by the need of maintenance either preventively or at failures. These demands are difficult to predict based on historical data of past spare parts usages, and therefore, the optimal inventory control policy may be also difficult to obtain. However, it is well known that maintenance costs are related to the availability of spare parts and the penalty cost of unavailable spare parts consists of usually the cost of, for example, extended downtime for waiting the spare parts and the emergency expedition cost for acquiring the spare parts. On the other hand, proper planned maintenance intervention can reduce the number of failures and associated costs but its performance also depends on the availability of spare parts. This paper presents the joint optimisation for both the inventory control of the spare parts and the Preventive Maintenance (PM) inspection interval. The decision variables are the order interval, PM interval and order quantity. Because of the random nature of plant failures, stochastic cost models for spare parts inventory and maintenance are derived and an enumeration algorithm with stochastic dynamic programming is employed for finding the joint optimal solutions over a finite time horizon. The delay-time concept developed for inspection modelling is used to construct the probabilities of the number of failures and the number of the defective items identified at a PM epoch, which has not been used in this type of problems before. The inventory model follows a periodic review policy but with the demand governed by the need for spare parts due to maintenance. We demonstrate the developed model using a numerical example.     

智能电表最优更换及备件库存联合决策

智能电表是智能电网运行的关键部件,提高其可靠性和可用度对保证电力的持续不间断供应和准确电能测量至关重要。充足的智能电表库存是其换装与维修的基本保障。本文基于智能电表的故障特性和换装需求分析,建立了智能电表的最优更换与备件库存联合决策模型,并给出了优化方法,以求得可以使系统长期平均运营成本最小的最优更换与备件库存策略。