BP神经网络的遗传模拟退火算法动态选址仿真

针对战区装备保障点动态选址问题的广义最大覆盖选址模型,综合分析传统的启发式算法全局、局部搜索中的优缺点,提出一种基于BP神经网络的遗传模拟退火算法,并将其运用于战区装备保障点动态选址决策实际问题中,对该算法进行了仿真研究,给出具体实例的仿真结果验证了该算法求解最优解的高效性以及运算的高收敛速度.     

模糊需求下保障设施选址-路径模型及算法

针对装备保障过程中被保障单位需求量不确定的问题,在详细分析保障设施选址-路径相关因素的基础上,利用三角模糊数对需求量进行标定。通过运用模糊综合评价与群决策思想确定保障设施备选址点权重,构建时间满意度函数与惩罚成本函数,建立模糊需求下带有时间窗及容量限制的选址-路径模型,并设计了基于聚类分析与蚁群算法的混合启发式求解算法。通过算例分析验证了模型的正确性及算法的有效性。     

变精度粗糙集的战时装备维修保障资源优化配置

以战时维修保障资源难以预测为背景,面向战时维修保障任务,建立了基于变精度粗糙集的战时装备维修保障资源优化配置模型,实现了不同精度下的多资源点、多需求点的资源优化配置。证明了战时维修保障资源所需时间的取值范围,以此推导出模型中所需时间与成本的取值范围。利用粒子群算法对该模型进行求解,得到最优目标函数值,从而验证出此模型与算法的有效性。     

基于偏随机秘钥遗传算法的舰船驻泊方案决策

军港码头需要在舰船抵达泊位时提供保障服务,受码头自身的驻泊条件和保障设施限制,舰船往往需通过多次泊位移动来满足多种保障服务需求,移泊不仅延长了舰船的在港时间,同时也将造成保障资源的额外消耗。为提高舰船运转率,减少移泊事件发生,构建了舰船驻泊的混合整数规划模型,并通过典型算例,釆用偏随机秘钥遗传算法进行优化计算,最终得到最优的舰船驻泊方案。所得到的结果与现有的舰船调度方法进行分析对比,并对计算结果进行了敏感性分析,为军港的舰船驻泊以及保障服务提供了决策依据。     

Hopfield神经网络的军事装备维修保障点选址等级分类

针对当前军事装备维修保障点选址问题热点,分析提出了基于Hopfield神经网络的选址等级分类问题研究思路,构建了Hopfield神经网络模型,设计了具体计算步骤,并结合实际算例进行了选址等级分类适用性验证分析,较好解决了在没有专家参与时的选址等级分类问题,为军方开展各类军事装备维修保障点选址工作提供了较为简捷实用的理论方法借鉴。     

基于随机网络的平均维修保障服务时间分析

利用随机网络建模方法在随机工程项目分析中的优越性,建立了不同保障方式下的维修保障过程模型。重点对网络活动中的维修等待时间和维修等待概率进行了分析,利用随机网络的解析算法,确定了维修力量的平均维修保障服务时间分析模型。最后将该方法应用于伴随修理过程,对其平均维修保障服务时间进行了案例分析。     

基于定周期策略的多品种维修器材联合订购模型

针对维修器材需求的随机性特点,在考虑维修器材保障度约束的条件下,基于定周期策略建立多品种维修器材的联合订购模型并给出了优化算法.该算法采用蒙特卡罗随机模拟技术求解随机变量的期望值,结合遗传算法求出使库存费用最小的基本订购周期、各种器材的订购周期和最大库存量,并能满足预定的保障度要求.与传统单品种补充策略比较发现,联合补充策略对经费节省作用显著.     

通用装备维修保障资源需求仿真建模研究

从新型通用装备维修保障的实际需求出发,在对维修保障系统运行过程分析的基础上,建立了维修资源需求的仿真模型,并给出了相关算法。该模型可实现对各种主要维修资源需求的预测,并能给出装备完好率、备件缺货率和装备待修时间等系统特征指标。该模型和算法可为装备的维修保障提供科学的依据。     

一种新的装备抢修分队选址方法

提出了两步选址法：应用需求势能理论并基于维修需求和路程两项主要指标构建装备抢修分队选址模型。计算得到次优选址区域后，从中选出若干备选点，再用模糊综合评判法确定符合实际的最佳选址点。实例分析表明：两步选址法具有一定的有效性和实用性。     

基于BMOPSO的后装保障基地多目标选址方法研究

基于后装保障基地选址多目标特性,提出了保障水平—保障距离阶跃性、保障加权距离等概念,构建了多目标混合整数规划选址模型;针对模型目标函数多、维度大、难以计算等特点,设计了一种二进制多目标粒子群算法(BMOPSO),并进行算法效率分析;结合算例分析了具体选址问题,计算得到了一组帕累托选址方案组合和帕累托前沿.该方法可根据决策者偏好为其提供选址决策参考.     

Algorithms for solving the conditional covering problem on paths

Consider the conditional covering problem on an undirected graph, where each node represents a site that must be covered by a facility, and facilities may only be established at these nodes. Each facility can cover all sites that lie within some common covering radius, except the site at which it is located. Although this problem is difficult to solve on general graphs, there exist special structures on which the problem is easily solvable. In this paper, we consider the special case in which the graph is a simple path. For the case in which facility location costs do not vary based on the site, we derive characteristics of the problem that lead to a linear‐time shortest path algorithm for solving the problem. When the facility location costs vary according to the site, we provide a more complex, but still polynomial‐time, dynamic programming algorithm to find the optimal solution. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

战役级军械维修器材保障决策支持系统设计研究

传统的面向事务的数据处理方法已经无法适应处理大量数据的要求,为解决这一问题,将面向主题的数据仓库技术用于军械维修器材保障决策支持系统的设计,阐述了基于数据仓库的军械维修器材保障决策支持系统需求分析、系统设计和系统实现,讨论了系统设计原则、设计方法、逻辑结构设计、多维数据仓库设计、底层数据库设计、前端用户视图设计等内容,对军械维修器材保障决策支持系统的开发提供了基本思路．     

基于安全库存的战时应急弹药储备库选址建模研究

为了更好适应战时弹药需求的不确定性,必须做好对战时应急保障系统的管理工作,应急弹药储备库的选址问题就是应急保障系统的关键环节．首先依据战时弹药需求特点,从库存策略的角度出发,引入安全库存概念;其次,建立了以建设成本和运输总距离为目标函数的战时应急弹药储备库选址模型;最后,运用Matlab对模型进行了仿真,验证了该模型的有效性．     

基于遗传算法的战时维修保障系统优化研究

分析了战时维修保障系统的结构功能,给出了维修保障系统的解析模型,提出了维修保障系统的可控参数及指标;为了使维修保障系统发挥最佳效能,以最小二乘准则作为系统的优化准则,建立了维修保障系统的优化模型,采用遗传算法对优化模型进行求解,给出了计算方法,并以计算实例验证了方法的可行性。     

Optimal state-dependent pricing policies for a class of stochastic multiunit service systems

This paper models a k-unit service system (e.g., a repair, maintenance, or rental facility) with Poisson arrivals, exponential service times, and no queue. If we denote the number of units that are busy as the state of the system, the state-dependent pricing model formalizes the intuitive notion that when most units are idle, the price (i.e., the service charge per unit time) should be low, and when most units are busy, the price should be higher than the average. A computationally efficient algorithm based on a nonlinear programming formulation of the problem is provided for determination of the optimal state-dependent prices. The procedure ultimately reduces to the search on a single variable in an interval to determine the unique intersection point of a concave increasing function and a linear decreasing function. The algorithm takes, on the average, only about 1/2 second per problem on the IBM 360/65 (FORTRAN G Compiler). A discrete optimal-control approach to the problem is shown to result in essentially the same procedure as the nonlinear-programming formulation. Several properties of the optimal state-dependent prices are given. Comparisons of the optimal values of the objective function for the state-dependent and state-independent pricing policies show that the former is on the average, only about 0.7% better than the latter, which may explain partly why state-dependent pricing is not prevalent in many service systems. Potential generalizations of the model are discussed.     

Ambulance location for maximum survival

This article proposes new location models for emergency medical service stations. The models are generated by incorporating a survival function into existing covering models. A survival function is a monotonically decreasing function of the response time of an emergency medical service (EMS) vehicle to a patient that returns the probability of survival for the patient. The survival function allows for the calculation of tangible outcome measures—the expected number of survivors in case of cardiac arrests. The survival‐maximizing location models are better suited for EMS location than the covering models which do not adequately differentiate between consequences of different response times. We demonstrate empirically the superiority of the survival‐maximizing models using data from the Edmonton EMS system. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

Application of Markov renewal theory and semi-Markov decision processes in maintenance modeling and optimization of multi-unit systems

In this paper, a condition-based maintenance model for a multi-unit production system is proposed and analyzed using Markov renewal theory. The units of the system are subject to gradual deterioration, and the gradual deterioration process of each unit is described by a three-state continuous time homogeneous Markov chain with two working states and a failure state. The production rate of the system is influenced by the deterioration process and the demand is constant. The states of the units are observable through regular inspections and the decision to perform maintenance depends on the number of units in each state. The objective is to obtain the steady-state characteristics and the formula for the long-run average cost for the controlled system. The optimal policy is obtained using a dynamic programming algorithm. The result is validated using a semi-Markov decision process formulation and the policy iteration algorithm. Moreover, an analytical expression is obtained for the calculation of the mean time to initiate maintenance using the first passage time theory.     

A note on equity across groups in facility location

An equity model between groups of demand points is proposed. The set of demand points is divided into two or more groups. For example, rich and poor neighborhoods and urban and rural neighborhoods. We wish to provide equal service to the different groups by minimizing the deviation from equality among groups. The distance to the closest facility is a measure of the quality of service. Once the facilities are located, each demand point has a service distance. The objective function, to be minimized, is the sum of squares of differences between all pairs of service distances between demand points in different groups. The problem is analyzed and solution techniques are proposed for the location of a single facility in the plane. Computational experiments for problems with up to 10,000 demand points and rectilinear, Euclidean, or general ?_p distances illustrate the efficiency of the proposed algorithm. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Managing waiting times of backordered demands in single‐stage (Q,r) inventory systems

We present a service constrained (Q, r) model that minimizes expected holding and ordering costs subject to an upper bound on the expected waiting time of demands that are actually backordered. We show that, after optimizing over r, the average cost is quasiconvex in Q for logconcave continuous lead time demand distributions. For logconcave discrete lead time demand distributions we find a single‐pass efficient algorithm based on a novel search stopping criterion. The algorithm also allows for bounds on the variability of the service measure. A brief numerical study indicates how the bounds on service impact the optimal average cost and the optimal (Q, r) choice. The discrete case algorithm can be readily adapted to provide a single pass algorithm for the traditional model that bounds the expected waiting time of all demands (backordered or not). © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 557–573, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10028