基于绩效保障模式的可修备件保障系统运营管理策略

基于绩效保障模式,设计了一个由备件仓库和维修车间组成的装备可修部件闭环保障系统,推导了备件库存水平状态的稳态概率分布,计算了可用度等几个保障绩效度量指标,建立了基于可用度约束的保障系统运作优化模型,并通过仿真分析探讨了保障系统运营管理策略问题。     

基于北斗的装备保障指挥系统设计

针对战时武器装备保障指挥特点,利用北斗系统作为保障指挥系统的定位和通信平台,设计北斗系统的应用结构方式和定位通信分层模型,开发传输层通信协议,构建基于北斗的装备保障指挥系统及保障指挥中心软件体系结构,能够有效的为战时装备保障指挥提供决策支持.     

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。     

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.     

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.     

基于灰色关联分析的航空装备技术保障能力评价模型

航空装备技术保障能力是组成航空兵战斗力的基本要素之一,其能力的高低直接影响到航空装备保障任务完成质量的优劣,因此对航空装备技术保障能力进行科学评价具有重要意义.在现有研究的基础上建立了航空装备技术保障能力评价指标体系,基于灰色关联分析方法提出了指标权重的确定方法,并利用灰色关联分析思想构建出航空装备技术保障能力评价模型,为航空装备技术保障能力评价提供量化分析模型,拓展了航空装备技术保障能力评价模型体系.     

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.     

战时备件配送的MDVRPTW问题研究

战时备件配送的车辆调度是提高装备保障效率的关键因素.以装备效能损失最小化为车辆调度的目标,建立了问题的M DVRPTW模型,并应用蚁群算法对问题进行了求解.算法中,根据问题特征改进了状态转移规则,设计了串行和并行两种路线构造方法,并应用局部搜索模块对蚂蚁构造的路线进行改进.对算例的计算实验表明,串行路线构造方法在精度和速度两方面均优于并行路线构造方法.     

蚁群算法在战损装备抢修任务指派中的应用研究

结合战时装备保障实际情况和战损装备抢修任务特点,分析了现有战损装备抢修任务指派模型的特点及不足.依据紧急程度对战损装备抢修任务进行分类,建立了不同紧急度对应的装备抢修任务指派模型,重点是利用蚁群算法对模型进行求解.最后通过某装备保障想定的实例进行了验证,结果表明该算法操作简单、切实有效,能有效实施战损装备应急抢修任务的指派,在装备保障智能决策系统中有较好的应用前景.     

Scheduling preventive maintenance for modular designed components: A dynamic approach

One of the benefits of modular design is ease-of-service. While modular design helps simplify field maintenance, extensive depot maintenance and spare modules are required to support the field maintenance. This study develops a dynamic approach for scheduling preventive maintenance at a depot with the limited availability of spare modules and other constraints. A backward allocation algorithm is proposed and applied to scheduling the preventive maintenance of an engine module installed in T-59 advanced jet trainers in the Republic of Korea Air Force. The algorithm developed by this study can be used to solve similar problems for various systems such as aerospace vehicles, heavy machinery, and medical equipment. The contribution of this study includes the uniqueness of the algorithm, the flexibility to deal with variables changing over time, and the ability to incorporate additional variables to handle complex situations.     

智能制造环境下的备件生产与运输协同调度问题研究

智能制造和即时配送环境下的备件生产与运输协同调度问题是目前国内研究的一大热点,这是因为备件供应链响应速度已成为当前备件制造企业赢得客户的关键因素。为了提高客户满意度,尽可能缩短从客户下达定制化生产订单到订单配送完成的时间,本文建立了以所有客户总等待时间最短为目标的混合整数规划模型和集合覆盖模型,推导了最优解性质,并设计改进的分支定价算法求得最优解。通过将小规模算例结果与CPLEX进行对比,验证了模型和算法的有效性。多组算例测试结果表明,所提出的模型和算法可以有效提升智能制造环境下的备件供应链运作效率。     

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.     

Multi-objective open shop scheduling by considering human error and preventive maintenance

This study presents an open shop scheduling model by considering human error and preventive maintenance. The proposed mathematical model takes into account conflicting objective functions including makespan, human error and machine availability. In order to find the optimum scheduling, human error, maintenance and production factors are considered, simultaneously. Human error is measured by Human Error Assessment and Reduction Technique (HEART). Three metaheuristic methods including non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective particle swarm optimization (MOPSO) and strength Pareto evolutionary algorithm II (SPEA-II) are developed to find near-optimal solution. The Taguchi method is applied by adjusting parameters of metaheuristic algorithms. Several illustrative examples and a real case study (auto spare parts manufacturer) are applied to show the applicability of the multi-objective mixed integer nonlinear programming model. The proposed approach of this study may be used for similar open shop problems with minor modifications.     

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.     

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.     

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.