基于可用度的装备售后保障系统控制策略研究

基于客户端装备系统可用度,构建一个由备件库存和服务台组成的闭环装备保障系统,通过分析备件库存水平状态特征,推导出备件库存水平状态稳态概率分布,并计算出可用度等几个保障质量指标,建立了基于可用度目标约束的保障系统控制优化模型,采用遗传算法并通过数值仿真揭示出可用度性质和保障系统的运作管理策略.     

三级备件保障系统下的备件库存优化模型

目前我军装备备件保障通常采用三级保障模式,以各级备件期望短缺数量之和最小为目标,研究在不同库存水平条件下,三级备件保障系统的备件库存优化模型.经示例分析,验证了该模型的可行性和有效性,该结果可为多级备件保障提供理论依据.     

典型串并联系统的备件库存优化模型

选取典型串并联系统的备件库存优化为研究对象,以系统供应可用度最大为目标,备件总购置费为约束条件构建备件库存优化模型,给出基于边际分析法的优化算法.经示例分析,验证了该模型的可行性和有效性,结果可为备件保障经费的合理配置提供理论依据.     

面向任务保障的多组件系统效益优化策略

为了保障系统在执行任务期间高可靠、高效益的运行,从系统效益的角度出发,构建了系统可靠性模型,采用边际效应思想构建了效益重要度,提出了一种面向任务保障的多组件系统效益优化策略。当系统可靠度下降到设定阈值时,计算系统各组件的效益重要度,选择效益重要度最大的组件进行备件分配,如此进行迭代,直到完成任务保障,形成最优的备件分配序列。通过该策略实现了以运行效益最优为目标,以系统可靠度和任务保障时长为约束的备件分配序列。最后,通过数值仿真验证了该策略的可行性。     

航空备件可拓聚类分析

应用可拓学的基本理论与方法 ,分析了航空备件的数值特征 ,建立了航空备件的物元模型 ;应用可拓原理对传统的聚类分析方法做了拓展 ,并据此对航空备件集合进行了划分 ,为航空备件的分类管理提供了一种崭新的方法 .     

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.     

基于保障度的战时三级备件保障优化研究

针对装备备件保障度问题,结合现有的研究现状,综合考虑了战时装备保障的实际情况和新旧备件使用寿命的差异,深入分析了备件调度对保障度的影响,以保障度为目标函数,建立了战时三级备件保障模型,运用经典的排序算法对模型进行求解。最后,算例表明该方法是有效性的。     

复杂设备最佳维修周期优化决策模型研究

针对现行三级维修机构保障的复杂设备,以规定可用度为约束条件,以单位工作时间内的平均维修费用最低为目标,通过对设备使用、修理流程分析,给出了设备使用与修理状态转移图,建立了设备一个更新周期内,维修周期与维修费用关系模型,并给出了相应应用案例,案例说明了模型的适用性与灵敏性,为维修决策和后续备件保障等提供依据.     

修如新模型中备件量的计算及其置信上限的Fiducial推断

本文研究了在修如新模型下, 对预定贮存期为$T$同时开始贮存的$N$个系统, 给出在$P_0$可修复率下所需备件数的计算公式; 针对贮存寿命服从威布尔分布的系统, 利用枢轴量, 在$P_0$可修复率和预定贮存期为$T$的条件下, 给出$N$个系统所需备件数的置信上限的定义; 并基于系统寿命试验的完全样本, 利用Fiducial方法得出备件数置信上限的计算方法.     

基于多种约束条件的维修备件库存优化方法研究

考虑了维修备件需求的随机性,以装备可用度、完好率置信度以及维修备件的保障程度为约束条件,运用概率论与数理统计方法,将维修备件保障费用达到最小值确定为目标函数,在此基础上,制定维修备件库存的最优方案,并通过示例验证了该方法的有效性和科学性.方法可以为其它相关领域解决物资库存与费用问题提供理论依据.     

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。     

Planning performance based contracts considering reliability and uncertain system usage

This paper investigates a novel quantitative approach for planning and contracting performance-based logistics in the presence of uncertain system usage. Our efforts focus on an integrated service delivery environment where the manufacturer develops capital-intensive systems and also provides after-sales support. We propose an analytical model to characterize system operational availability by comprehending five performance drivers: inherent failure rate, usage rate, spare parts inventory, repair time, and the fleet size. This analytical insight into the system performance allows the service supplier to minimize the total cost across system design, production, maintenance, and repair. Two contracting schemes are investigated under cost minimization and profit maximization schemes. For the first time in literature, reliability design and service parts logistics are seamlessly integrated into one decision support model for improving operational availability while lowering the lifecycle cost. Numerical examples are provided to demonstrate the applicability and the effectiveness of the proposed decision support tool.     

面向多阶段任务的武器系统备件优化配置建模

资源优化配置是作战单元维修保障的关键因素.当作战单元执行单阶段任务时,讨论了部件结构为串联系统和k/n系统的任务成功概率建模问题,在此基础上,建立了k/n结构动态变化的多阶段任务系统的任务成功概率模型.在满足系统任务成功概率约束条件下,给出了防空作战单元备件携行量优化模型,并运用边际分析法进行了求解,通过示例表明了模型的正确性.     

Optimization of component reliability in the design phase of capital goods

We introduce a quantitative model to support the decision on the reliability level of a critical component during its design. We consider an OEM who is responsible for the availability of its systems in the field through service contracts. Upon a failure of a critical part in a system during the exploitation phase, the failed part is replaced by a ready-for-use part from a spare parts inventory. In an out-of-stock situation, a costly emergency procedure is applied. The reliability levels and spare parts inventory levels of the critical components are the two main factors that determine the downtime and corresponding costs of the systems. These two levels are decision variables in our model. We formulate the portions of Life Cycle Costs (LCC) which are affected by a component’s reliability and its spare parts inventory level. These costs consist of design costs, production costs, and maintenance and downtime costs in the exploitation phase. We conduct exact analysis and provide an efficient optimization algorithm. We provide managerial insights through a numerical experiment which is based on real-life data.     

A study of repairable parts inventory system operating under performance-based contract

Performance-Based Logistics (PBL) is becoming a dominant logistics support strategy, especially in the defense industry. PBL contracts are designed to serve the customer’s key performance measures, while the traditional contracts for after-sales services, such as Fixed-price (FP) and Cost-plus (C+), only provide insurance or incentive. In this research, we develop an inventory model for a repairable parts system operating under a PBL contract. We model the closed-loop inventory system as an M/M/m queue in which component failures are Poisson distributed and the repair times at the service facility are exponential. Our model provides the supplier and the customer increased flexibility in achieving target availability. Analysis of key parameters suggests that to improve the availability of the system with repairable spare parts, the supplier should work to improve the components reliability and efficiency of repair facility, rather than the base stock level, which has minimal impact on system availability.     

Maximizing system availability through joint decision on component redundancy and spares inventory

For a repairable k  -out-of-n:G$n:G$ system consisting of line-replaceable units, its operational availability depends on component reliability, its redundancy level, and spare parts availability. As a result, it is important to consider redundancy allocation and spare parts provisioning simultaneously in maximizing the system’s operational availability. In prior studies, however, these important aspects are often handled separately in the areas of reliability engineering and spare parts logistics. In this paper, we study a collection of operational availability maximization problems, in which the component redundancy and the spares stocking quantities are to be determined simultaneously under economic and physical constraints. To solve this type of problem, continuous-time Markov chain models are developed first for a single repairable k  -out-of-n:G$n:G$ system under different shut-off rules, and some important properties of the corresponding operational availability and spare parts availability are derived. Then, we extend the models to series systems consisting of multiple repairable k  -out-of-n:G$n:G$ subsystems. The related optimization problems are reformulated as binary integer linear programs and solved using a branch-and-bound method. Numerical examples, including a real-world application of automatic test equipment, are presented to illustrate this integrated product-service solution and to offer valuable managerial insights.     

基于干扰管理理论的航空备件模型参量扰动性分析

概述了干扰管理理论的内涵、意义以及目标,依据未来不确定因素引起飞机备件需求量变化以及备件供应时限性增强的实际情况,创建了时限一致度算子,优化了备件需求泊松分布模型,提高了备件供应的精确度,并选取了优化后的模型中的多个参量,进行了多因素的扰动性分析,最后构造了算例,验证了本方法的科学性和有效性.     

A condition-based order-replacement policy for a single-unit system

This paper presents a condition-based order-replacement policy for a single-unit system, aiming to optimize the condition-based maintenance and the spare order management jointly. The concerned system deteriorates stochastically and gradually, and is inspected periodically. Under the proposed policy, both the preventive replacement and the spare order are decided based on the observed deterioration level of the system. Therefore, the decision variables for this order-replacement problem include the inspection interval, the ordering threshold, and the preventive replacement threshold. The analytical modeling of the condition-based order-replacement policy is presented in detail in this paper. The policy performance is evaluated in terms of the long-run average cost per unit time, the mean availability, and the rate of preventive replacement, for which the mathematical models are also derived. Numerical examples illustrate the performance of the condition-based order-replacement policy, especially the influences of the lead time of the spare order over the different performance criteria.     

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.