多品种维修器材统一订购策略的库存决策模型

分析了一种多品种维修器材联合订货的库存决策模型,该模型采用连续检查的多品种统一订购库存策略。维修器材库存控制的目的是在确保维修器材服务水平的基础上,降低库存成本。根据这一思想设计了算法,该算法首先假设订货点和订购提前期为零,根据库存成本确定最大库存量,然后考虑订货点和订购提前期不为零的情况,根据维修器材满足率确定各品种维修器材的订货点,从而求得决策参数。最后通过实例证明模型和算法的有效性。     

消耗性航材备件订货模型的确定

本文在讨论航材备件需求率的基础上,从分析备件保障率入手,研究提出了按照航材保障率来确定航材的最佳订货周期和最佳订货批量的一种新的科学方法,建立了消耗性航材备件的订货模型,避免了以往单纯以年度需求量R作为消耗性器材订货依据的经验误区。     

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.     

基于状态的维修与备件库存决策联合优化研究

针对需进行定期功能检测的设备,研究其基于状态的维修策略与备件库存策略联合优化方法。引入延迟时间理论,分析定期功能检测策略下备件库存的消耗与供应过程;基于设备功能退化和备件库存变化规律,建立以设备维修保障费用为优化目标的数学模型,并采用仿真的方法对设备功能检测周期、备件最大库存水平、备件订购周期等决策变量进行联合优化。通过一个算例对所提出的方法和建立的模型进行应用验证。     

联合补充博弈的费用分摊

就一个仓库、多个零售商,对联合订货费用函数的模型进行分析,给出了一个求解最佳订货周期的多项式时间的算法,且算法的时间复杂性为O（nlogn）。利用文献[8]中的技巧,给出了该库存博弈的核。     

基于果蝇优化算法的备件柜布局优化

备件车是保障维护其他装备正常运行的车辆,其备件柜的布局是否优化决定备件的更换速率,甚至影响整个系统的工作效率。针对备件柜的布局进行了深入研究,并通过计算实现了备件柜的布局分布。在备件柜的布局优化过程中运用了剩余矩形匹配的布局策略,并结合备件柜的布局特点对该算法进行了相应改进,使其更好地运用在本研究的布局问题中。同时运用果蝇优化算法来确定剩余矩形匹配权重的计算,在布局过程中将传统的布局策略和智能算法相结合以达到优化的目的。     

基于完备相容Rough决策表的备件品种确定方法

针对备件品种确定问题,分析了常用的备件品种确定方法,探讨了影响备件品种确定的主要因素,包括装备单元的关键性、易损性、经济性、可更换性和获得难度。采用了Rough决策表的属性约简与属性值约简相结合的方法,对决策表进行信息挖掘,得到了备件品种确定的简化决策规则。最后,通过案例分析验证了该方法的可行性,结果表明该方法具有可操作性强、易于实施的特点,为装备备件品种确定提供了实用的分析方法。     

无人机携行备件品种确定方法

无人机在战场的应用日趋频繁,因此对其备件的保障日益迫切.在携行标准下,备件的品种大多采用定性的分析和逻辑判断法来确定.针对无人机的特点,建立无人机携行备件品种评价指标.通过层次和TOPSIS评价方法进行定量计算,得出无人机各备件的排序,以间接确定其备件的品种.该数学模型为携行备件品种的确定提供了一种更为科学、合理的方法.     

新型小规模装备备件品种确定的犹豫模糊粗糙集决策方法

针对新研装备备件品种确定过程中决策信息“犹豫性”和“模糊性”特点突出、难以运用传统备件品种确定方法进行决策的问题,提出一种基于犹豫模糊粗糙集的备件品种确定方法。利用风险偏好系数对不完备犹豫模糊信息进行数值延拓,为构建不同风险偏好下备件品种确定的犹豫模糊决策信息系统奠定了基础；考虑得分函数和数值延拓边界的综合因素影响给出了改进的包含度计算公式,并基于包含度定义进行了证明；给出了基于改进包含度计算的备件品种决策属性的约简条件和规则获取方法,实现了犹豫模糊决策信息的深度挖掘和有效利用。以某新研装备备件品种确定为例进行了方法验证,研究结果表明：通过该法能够有效处理犹豫模糊决策信息,获取精简实用的备件品种决策规则集,验证了方法的可行性。     

二级库存-运输系统的联合经济订购模型

结合器材供应的实际,构建了一个国家仓库-地区仓库形成的二级库存-运输系统,以最低费用为目标,建立了该系统的联合经济订购模型,求解了该系统各仓库的最佳订货量和订货次数;运用该模型对3种订货策略（年度1次订货、年度多次订货、零库存订货）进行了算例验证,表明了该模型在优化多级库存运输系统订货参数方面的意义。     

备件库存限量分析及决策模型

分析了备件库存管理所对应的库存控制策略及其有关的因素,以费用为目标函数、不缺备件概率为约束条件,运用概率论及库存论原理建立了备件库存限量的决策摸型。     

The mixed ordering policy in periodic review stochastic multi-commodity inventory systems

In multi-commodity inventory systems with variable setup costs, the mixed ordering policy assumes that commodities may be ordered either individually, or may be arbitrarily grouped for joint ordering. Thus, for a two-commodity system, commodity one or commodity two or commodities one and two may be ordered incurring respectively fixed order costs of K, K₁, or K₂, where max (K₁, K₂) ≤ K ≤ K₁ + K₂, This paper considers a two-commodity periodic review system. The stationary characteristics of the system are analyzed, and, for a special case, explicit solutions are obtained for the distribution of the stock levels at the beginning of the periods. In a numerical example, optimal policy variables are computed, and the mixed ordering policy is compared with individual and joint ordering policies.     

Optimal policies for a multi-product inventory system with negotiable lead times

The objective of this paper is to determine the optimum inventory policy for a multi-product periodic review dynamic inventory system. At the beginning of each period two decisions are made for each product. How much to “normal order” with a lead time of λ_n periods and how much to “emergency order” with a lead time of λ_e periods, where λe = λ_n - 1. It is assumed that the emergency ordering costs are higher than the normal ordering costs. The demands for each product in successive periods are assumed to form a sequence of independent identically distributed random variables with known densities. Demands for individual products within a period are assumed to be non-negative, but they need not be independent. Whenever demand exceeds inventory their difference is backlogged rather than lost. The ordering decisions are based on certain costs and two revenue functions. Namely, the procurement costs which are assumed to be linear for both methods of ordering, convex holding and penalty costs, concave salvage gain functions, and linear credit functions. There is a restriction on the total amount that can be emergency ordered for all products. The optimal ordering policy is determined for the one and N-period models.     

Fixed‐interval joint‐replenishment policies for distribution systems with multiple retailers and stochastic demand

We consider a distribution system consisting of a central warehouse and a group of retailers facing independent stochastic demand. The retailers replenish from the warehouse, and the warehouse from an outside supplier with ample supply. Time is continuous. Most previous studies on inventory control policies for this system have considered stock‐based batch‐ordering policies. We develop a time‐based joint‐replenishment policy in this study. Let the warehouse set up a basic replenishment interval. The retailers are replenished through the warehouse in intervals that are integer multiples of the basic replenishment interval. No inventory is carried at the warehouse. We provide an exact evaluation of the long‐term average system costs under the assumption that stock can be balanced among the retailers. The structural properties of the inventory system are characterized. We show that, although it is well known that stock‐based inventory control policies dominate time‐based inventory control policies at a single facility, this dominance does not hold for distribution systems with multiple retailers and stochastic demand. This is because the latter can provide a more efficient mechanism to streamline inventory flow and pool retailer demand, even though the former may be able to use more updated stock information to optimize system performance. The findings of the study provide insights about the key factors that drive the performance of a multiechelon inventory control system. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 60: 637–651, 2013     

Fluctuations of the optimal advertising and inventory policy: A qualitative analysis

This paper discusses the properties of the inventory and advertising policy minimizing the expected discounted cost over a finite horizon in a dynamic nonstationary inventory model with random demand which is influenced by the level of promotion or goodwill. Attention is focused on the relation between the fluctuations over time of the optimal policies and the variations over time of the factors involved, i.e., demand distributions and various costs. The optimal policies are proved to be monotone in the various factors. Also, three types of fluctuations over time of the optimal policies are discussed according to which factor varies over time. For example, if over a finite interval, the random demand increases (stochastically) from one period to the next, reaches a maximum and then decreases, then the optimal inventory level will do the same. Also the period of maximum of demand never precedes that of maximum inventory. The optimal advertising behaves in the opposite way and its minimum will occur at the same time as the maximum of the inventory. The model has a linear inventory ordering cost and instantaneous delivery of stocks; many results, however, are extended to models with a convex ordering cost or a delivery time lag.     

On ordering perishable inventory under erlang demand

Existing models for describing optimal ordering policies for perishable inventory cast the problem as a multidimensional dynamic program, the dimensionality being one less than the product lifetime in periods. An approach developed in previous work takes explicit account of outdating in the single period model. Formulas for the expected quantity of any new order which will outdate are developed for the case where the demand has a stationary Erlang distribution. A modified version of the one period model is shown to yield a reasonable approximation to the stationary optimal policy.     

A dynamic inventory system with recycling

This paper deals with a periodic review inventory system in which a constant proportion of stock issued to meet demand each period feeds back into the inventory after a fixed number of periods. Various applications of the model are discussed, including blood bank management and the control of reparable item inventories. We assume that on hand inventory is subject to proportional decay. Demands in successive periods are assumed to be independent identically distributed random variables. The functional equation defining an optimal policy is formulated and a myopic base stock approximation is developed. This myopic policy is shown to be optimal for the case where the feedback delay is equal to one period. Both cost and ordering decision comparisons for optimal and myopic policies are carried out numerically for a delay time of two periods over a wide range of input parameter values.     

GM(1,1)模型在装备备件订购中的应用

分析了武器装备备件订购决策的目标和常用方法,并运用灰色理论GM(1,1)模型及关联度分析法,提出了在军用备件订购工作中费用目标预测的应用方法,以及提高预测精度的措施。     

Technical note: Worst‐case performance of power‐of‐two policies for serial inventory systems with incremental quantity discounts

This study presents power‐of‐two policies for a serial inventory system with constant demand rate and incremental quantity discounts at the most upstream stage. It is shown that an optimal solution is nested and follows a zero‐inventory ordering policy. To prove the effectiveness of power‐of‐two policies, a lower bound on the optimal cost is obtained. A policy that has a cost within 6% of the lower bound is developed for a fixed base planning period. For a variable base planning period, a 98% effective policy is provided. An extension is included for a system with price dependent holding costs. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Optimal policies under the shortage probability criterion for an inventory model with unknown dependent demands

The principal innovation in this paper is the consideration of a new objective function for inventory models which we call the shortage probability criterion. Under this criterion we seek to minimize the total expected discounted cost of ordering subject to the probability that the stock level at the end of the period being less than some fixed quantity not exceed some prescribed number. For three different models we show that the minimum order policy is optimal. This result is then applied to a particular inventory model in which the demand distribution is not completely known. A Bayesian procedure is discussed for obtaining optimal policies.