装备经费预算项目组合优化决策模型研究

应用项目组合管理理论,综合考虑装备建设的实际需要与经费供给的可能性,建立以预期军事效益最大为目标,以装备经费预算控制指标约束、项目关系约束和项目可分解约束的装备经费预算项目组合优化决策模型。所建立的模型为装备经费预算编制工作提供了可行的决策技术方法,有效增强了装备经费预算编制的科学性、公正性和透明度。     

基于遗传算法的装备维修费用优化分配理论及优化方法研究

在美国工业界武器系统咨询委员会提出的郊能公式基础上 ,建立了武器装备效能与维修费用函数关系 ,为武器装备维修费用的优化分配提供了一个标准 .针对多种武器装备维修费用优化 ,建立了一个维修费用分配模型 ,用遗传算法进行了优化 ,并对优化过程进行了详细阐述 .在此基础上开发了装备维修经费优化与管理辅助决策支持系统 .     

某新型装备维修策略的研究

本文对如何借助于专家的知识、经验,科学、合理地制定某新型武器装备的维修策略进行了研究、探讨,提出了具有军事意义、又具有经济价值的新方法。为该新型装备的贮存、管理提供了决策依据,同时也为其它新型装备维修策略的制定提供了思路。     

基于占线排序 D-收益函数的战时维修改进策略

由于战时作战强度、战场环境变化不确定性,导致战损或故障装备的产生无法提前获知,从而战时装备维修机构受领维修任务进行装备维修作业成为一个占线问题。采用占线单服务排序理论对该问题进行建模,定义了战时装备维修收益函数,设计了一种改进的基于单位平均收益的单服务维修策略,推断了其有效竞争在概率意义上为2／η（η＞＞1,η是与不同维修任务类型发生概率相关的统计量）,为战时装备维修策略选择提供了新思路和可行做法。     

视情维修条件下备件消耗规律

通过对某复杂装备中视换件修理方式的分析,将备件故障后维修消耗规律与备件视情维修消耗规律结合起来,研究备件总的消耗规律。并运用装备可靠性理论及概率论方法,建立了备件消耗规律数学模型。在此基础上给出了视换件储备数量的计算方法,解决了当前因视情维修条件下备件消耗规律不易获取,而导致视换件的储备方案难以制订的问题。最后举例说明了模型的适用性,为科学确定装备维修备件储存量提供了理论依据,且具有重要的指导作用。     

基于GA-SVM的舰船装备临修经费需求预测模型

针对舰船装备临修经费需求预测得不到满意解的问题,运用遗传算法将SVM相应的参数进行优化,建立了基于GA-SVM的舰船装备临修经费预测模型.通过将GA-SVM模型与BP神经网络模型的预测结果进行对比分析,结果表明:GASVM的预测效果更优异,对舰船装备临修经费需求预测有更好的参考意义.     

一种装备维修器材保障准确性计算方法研究

装备维修器材保障准确性是影响装备维修保障工作的重要因素之一.从装备维修器材保障过程中所需满足的数量和品种入手,给出了一种装备维修器材保障准确性计算方法,最后提出了提高装备维修器材保障准确性的对策与措施.     

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.     

作战环境中舰艇装备维修模糊决策支持系统

作战环境中的舰艇所肩负使命和面临的威胁对装备修理优先级的确定有着较大影响。本文介绍一种确定装备维修优先级的模糊决策支持系统。该系统考虑了舰艇使命，所在编队，当时面临的各种威胁以及所处的战场环境。     

考虑横向供应的多阶段装备维修器材供应优化方法研究

装备维修器材的精确、定量和快速供应是部队遂行作战、训练等任务的重要物质保证。针对多阶段装备维修器材供应问题,考虑多种器材供应方式,构建装备维修器材多级供应模式。为提升器材供应方案的全局最优性,将野战仓库选址、器材库存控制及运输车辆路径规划等关键问题融合为一类选址-库存-路径组合优化问题,以总成本最小为目标,考虑部队级仓库间的横向供应方式,构建混合整数线性规划模型。设计一种基于逻辑的Benders分解算法,将原问题分解为主问题和子问题,通过生成Benders切割迭代求解。通过示例分析证明：(1)本文所提出的LBBD算法能够有效降低问题复杂度,提升求解质量,LBBD算法得出的供应方案总成本比CPLEX求解器得出方案成本低41.62%;(2)考虑横向供应能够有效降低保障费用,考虑横向供应能够使总成本降低6.24%,同时,考虑横向供应可提高装备维修器材供应系统的灵活性。     

基于历史故障数据的租赁设备预防维护策略优化

针对租赁设备的特殊性,提出了一种周期预防维护策略模型。该策略综合考虑设备的当前维护周期、预防维护、小修以及惩罚机制等因素对维护成本的影响,从设备的当前维护周期出发,构造出故障率分布的平滑函数,以设备的历史故障数据信息为依据,使用最大似然估计解析方法对设备的故障率分布函数参数进行有效估计,建立以租赁企业维护成本最小化为目标的周期预防维护策略模型。最后是算例分析,研究表明,该策略符合租赁设备维护的实际情况,可为租赁企业提供有效的维护解决方案。     

Optimal threshold values of age and two-phase maintenance policy for leased equipments using age reduction method

This paper investigates the optimal threshold values of age to perform preventive maintenance (PM) actions for leased equipment within the lease period. In this paper, we use age reduction method to describe the degree of PM and construct the maintenance cost function. For repairable leased equipment, two maintenance models are proposed: (i) maintenance policy of single-phase and (ii) maintenance policy of two-phase. During the lease period, PM actions are carried out when the age of equipment reaches a certain threshold value. Any failure of the leased equipment is rectified by a minimal repair within the lease period. Under these maintenance schemes, the mathematical models of the expected total cost for maintenance policies of single-phase and two-phase are established, and the optimal maintenance policies are derived such that the expected total cost is minimized. Finally, the features of the optimal maintenance policy are illustrated through numerical examples.     

配电设备实时故障风险评估方法

现有配电设备故障风险评估方法在因素分析方面不够全面,未能综合考虑天气等环境因素与设备健康状态因素对设备故障风险的影响,且数据来源主要为长期历史数据,缺乏时效性。为解决此问题,本文提出了一种配电设备实时故障风险评估方法,结合天气状况、设备状态两大因素计算配电设备实时故障概率,通过负荷损失量、停电用户数量、负荷重要等级三个因素评估配电设备故障影响程度,并以设备故障概率和故障影响程度为准则建立风险评估模型。通过IEEE-RBTS BUS2算例分析,证明该模型能够有效评估配电设备实时故障风险,对于电力企业优化设备检修工作,提升应急管理水平具有重要的指导意义。     

Optimal maintenance service contract negotiation with aging equipment

In recent years, there has been a growing trend to out-source service operations in which the equipment maintenance is carried out by an external agent rather than in-house. Often, the agent (service provider) offers more than one option and the owners of equipment (customers) are faced to the problem of selecting the optimal option, under the terms of a contract. In the current work, we develop a model and report results to determine the agent’s optimal strategy for a given type of contract. The model derives in a non-cooperative game formulation in which the decisions are taken by maximizing expected profits. This work extends previous models by considering the realistic case of equipments having an increasing failure intensity due to imperfect maintenance, instead of the standard assumption that considers failure times are exponentially distributed (constant failure intensity). We develop a model using a linear function of time to characterize the failure intensity. The main goal, for the agent, is to determine the pricing structure in the contract and the number of customers to service. On the other hand, for the clients, the main goal is to define the period between planned actions for preventive maintenance and the time to replace equipments. In order to give a complete characterization of the results, we also carry out a sensitivity analysis over some of the factors that would influence over the failure intensity.     

Optimal systems for equipment maintenance and replacement under Markovian deterioration

Deterioration of equipment is modeled as a multistate discrete time controlled Markov process. The states are classified according to the degree of deterioration. The problem of design of optimal systems for equipment maintenance and replacement is considered when the decision-maker may take, in each stage, one of many available maintenance actions, classified according to their “stochastic effectiveness”; no action and replacement are included as alternatives. It is assumed that the transition probabilities satisfy two conditions which effectively describe a trend for monotonically increasing expected deterioration and rate of deterioration. Under these assumptions it is proved in the paper that the optimal (cost minimizing) decision system in an infinite horizon is of the control limit rule type, rapidly obtained by policy improvement algorithms. A numerical example is presented for a specific practical application; detailed data are available from the authors on request.     

Optimal preventive maintenance,protection, and replacement of a revenue-earning asset

The problem of determining the optimal pattern of expenditure on preventive maintenance or protection for a revenue-earning asset subject to catastrophic breakdown or destruction is discussed. It is assumed that the probability of breakdown at any time depends on the age of the asset and on the current rate of prevention expenditure. The objective considered is the maximization of the expected present value of revenues earned net of prevention and replacement costs. Three distinct cases are discussed: (a) when revenue is earned only until breakdown; (b) when there is automatic replacement following a breakdown; and (c) when there is the option of periodic replacement. Use of the Pontryagin maximum principle enables the determination of the optimal prevention schedules in all cases. In addition, when periodic replacement is possible the optimal replacement interval can be determined. Numerical methods are required to obtain solutions.     

基于梯形模糊综合分析法的弹药维修安全风险评估

针对弹药维修的复杂性和特殊性,提出了一种基于梯形模糊综合分析法的弹药维修安全风险评估的方法.从环境、管理、人员、设备和物资等5个方面考虑,建立了弹药维修安全风险评估指标体系;鉴于人判断的模糊性和判断矩阵一致性检验要求,采用了基于梯形模糊数期望值的层次分析法确定指标的权重系数;建立了基于梯形模糊综合分析法的弹药维修安全风险评估模型.通过对某弹药维修工厂的实例分析证明,所建立的弹药安全风险评估模型合理、科学、有效,能够为制定相应的法规提供理论的支撑.     

Structured risk management: filling a gap in decision analysis education

Risk management is a standard management tool that does not generally appear in decision analysis textbooks nor is it explicitly cited as part of the standard decision-analysis paradigm. In contrast, risk management articles and books describe how decision trees can be used to evaluate specific risk management strategies. In this paper we describe a series of steps that should be a routine part of every decision tree analysis. They are designed to assess the expected value of developing a risk management strategy with regard to different aspects of uncertainty. The method is intended to trigger a focused brainstorming session to search for specific strategies to manage targeted risks. The procedure adds structure to the value-enhancing dimension of decision analysis that creates new strategies with less risk and higher expected values. The material presented here can easily be incorporated into even an overview of decision analysis in a survey class of operational research.     

Bi-objective workforce-constrained maintenance scheduling: a case study

In this paper, a real maintenance workforce-constrained scheduling problem is formulated as a bi-objective mixed-integer programming model with the aim of simultaneously minimizing the workforce requirements and maximizing the equipment availability. The skilled workforce is provided by internal and external resources using regular time, overtime and contracting. The equipment availability is measured by the downtime required for preventive maintenance (scheduled) and failure repair (unscheduled) jobs. We also encounter imminent or potential failures whose priorities depend on the severity of the failure on the system (secondary failure). The total weighted flow time is used as a scheduling criterion to measure the equipment availability; the weight of each job directly depends on the expected downtime resulting from the associated failure. The proposed model is verified using two comprehensive numerical examples and some sensitivity analyses. We conclude by discussing the results.     

The Decision to Repair or Scrap a Machine

A simple algebraic formula for combining repair data with prior experience determines the time when a machine should be replaced in order to minimize the expected cost of equipment purchase and maintenance. A random sample from an exponential distribution represents the cost of each repair, and a time-dependent Poisson process represents the intervals between repairs. Bayes' formula provides the basis for combining data with previous judgement about the characteristics of the equipment. Automobile maintenance records support the basic assumptions of the model, and illustrate the method of deciding when to scrap a given machine.