Average Cost Optimality in Inventory Models with Markovian Demands

This paper is concerned with long-run average cost minimization of a stochastic inventory problem with Markovian demand, fixed ordering cost, and convex surplus cost. The states of the Markov chain represent different possible states of the environment. Using a vanishing discount approach, a dynamic programming equation and the corresponding verification theorem are established. Finally, the existence of an optimal state-dependent (s, S) policy is proved.     

Inventory Models with Markovian Demands and Cost Functions of Polynomial Growth

This paper studies stochastic inventory problems with unbounded Markovian demands, ordering costs that are lower semicontinuous, and inventory/backlog (or surplus) costs that are lower semicontinuous with polynomial growth. Finite-horizon problems, stationary and nonstationary discounted-cost infinite-horizon problems, and stationary long-run average-cost problems are addressed. Existence of optimal Markov or feedback policies is established. Furthermore, optimality of (s, S)-type policies is proved when, in addition, the ordering cost consists of fixed and proportional cost components and the surplus cost is convex.     

Distribution Free Procedures for Some Inventory Models

Stochastic inventory models, such as continuous review models and periodic review models, require information on the lead time demand. However, information about the form of the probability distribution of the lead time demand is often limited in practice. We relax the assumption that the cumulative distribution function, say F, of the lead time demand is completely known and merely assume that the first two moments of F are known and finte. The minmax distribution free approach for the inventory model consists of finding the most unfavourable distribution for each decision variable and then minimizing over the decision variable. We solve both the continuous review model and the periodic review model with a mixture of backorders and lost sales using the minmax distribution free approach.     

General Approximate Solutions for some Common Inventory Models

Employing a group of general approximations introduced elsewhere, we derive explicit approximate solutions to the standard newsboy problem, to some (Q, r) models recently developed, and to a periodic review order up to R model. The solutions obtained are general in that no distributional assumptions need to be specified prior to their derivation, and merely knowledge of the first three or four moments (of the relevant random variables) is required. The accuracy achieved is satisfactory, while algebraic simplicity is preserved.     

Duals for Classical Inventory Models via Generalized Geometric Programming

Inventory problems generally have a structure that can be exploited for computational purposes. Here, we look at the duals of two seemingly unrelated inventory models that suggest an interesting duality between discrete time optimal control and optimization over an ordered sequence of variables. Concepts from conjugate duality and generalized geometric programming are used to establish the duality.     

Inventory Models for Increasing Demand Followed by Level Demand

This paper considers a product subject to a period of increasing demand, according to a general power law, followed by a period of level demand. By assuming that the Economic Batch Quantity (EBQ) is used as soon as level demand is reached, we derive the optimal production/purchasing policy for use at any stage during the period of growth. The optimal approach is compared with that of the Silver-Meal heuristic.     

Two Probabilistic Models for Solving an Oxygen-bottle Inventory Problem

In this paper, an inventory oxygen-bottles problem is formulated as (i) an (M/M/C):(GD/N/∞) queueing model with different service rates; (ii) a probabilistic goal programming (PGP) model. By using the first model, the optimum required rate for oxygen bottles to be stored is determined. By using the second model, the decision-maker can determine his policy, which minimizes the total cost. Finally, a case study of the storage of oxygen bottles for the Egyptian Air Force (1980-1984) is presented.     

ARIMA Forecasting Models in Inventory Control

In many industrial inventory control systems the policy of reordering and at what level depends crucially on the statistical properties of the random sum of a sequence of sales demands over the lead time. Current practice has conveniently assumed that the sales demands are independent. This paper considers the modifications required when the ARIMA model form of dependence applies.     

选址-库存-路径问题模型及其集成优化算法

设施选址、库存控制和车辆路径安排是物流系统优化中的三个关键问题,三者之间存在相互依赖的关系,应该根据这种关系来相应地进行综合优化与管理物流活动。以典型的单一生产基地、单一产品、采用不断审查的(Q, r)库存策略的供应链二级分销网络为研究对象,建立了一个随机型选址-库存-路径问题优化模型;在将非线性混合整数规划转化为线性整数集合覆盖模型的基础上,采用列生成算法来获得一个近似最优解,再用分支定价法对初始解进行改进,以实现对整个问题“完全集成”的优化。最后,用随机生成的方式,产生了10至160个客户的计算实例,分析了运输费用和库存费用对总成本的影响,算法运算时间表明本文给出的算法能较快地求解这一复杂问题。     

搜寻成本下可替代产品的定价策略与库存决策

为探寻存在搜寻成本情况下消费者购买可替代产品时的定价与库存问题,从消费者效用出发,对厂商收益构建了基于马尔可夫决策过程的优化模型。在消费者方面,分析了其购买与继续搜寻的条件,并分别在搜寻成本不变和搜寻成本边际递减的情况下研究了消费者保留效用的变化情况以及购买相应产品的概率。此外,与很多相关文献不同的是,由于搜寻成本的存在,该情形下的消费者并不一定会在完成购买之前搜寻完所有的产品。在厂商方面,根据实际情况构建不同搜寻顺序下的收益模型并求解出最优定价策略与库存策略,并将定价模型与库存策略扩展到了动态的环境,为厂商制定价格及库存方案提供相应的决策支持。     

Inventory Lot Size Models with Vanishing Market

The paper is concerned with equipment which is being phased out because it is superseded by a technologically more advanced product. It nevertheless still requires efficient handling for the rest of its life span.An inventory lot size model without backlogs for a single commodity with a vanishing market is used. The vanishing market is represented by- a demand function that decreases with time and eventually reaches zero, implying that the planning horizon is finite and known. It is assumed that there is no lead time for replenishments. The problem is to devise an optimal management scheme for this inventory system, namely, to determine the number of replenishments and their schedule over the predicted survival time horizon of the product.The problem is completely solved for the class of demand functions which can be approximated to by positive powers of time. The solution is given in a nearly closed form and is proven to be unique.     

Stochastic Multiproduct Inventory Models with Limited Storage

This paper studies multiproduct inventory models with stochastic demands and a warehousing constraint. Finite horizon as well as stationary and nonstationary discounted-cost infinite-horizon problems are addressed. Existence of optimal feedback policies is established under fairly general assumptions. Furthermore, the structure of the optimal policies is analyzed when the ordering cost is linear and the inventory/backlog cost is convex. The optimal policies generalize the base-stock policies in the single-product case. Finally, in the stationary infinite-horizon case, a myopic policy is proved to be optimal if the product demands are independent and the cost functions are separable.     

The Numerical Generation of Lead Time Demand Distributions for Inventory Models

This paper presents a general method for computing a lead time demand distribution from the basic distributions of order quantity, orders per unit time, and lead time. These distributions are specified numerically and can therefore be derived directly from historical data.     

一类变价格的物资库存优化模型

本文提出一类变价格的物资库存优化模型，对模型作了近似处理并求解，从而确定最佳订购次数．     

带有可变库存费的变质性物品的部分延期供给的库存控制模型

传统的库存控制模型都视需求率为常数,在这篇文章中,放松了这个假定,研究了库存费的两种可能的变化:(i)库存费的变化率为存储时间的函数;(ii)库存费的变化率为库存量的函数.在模型中允许短缺发生且假定短缺部分延期供给,且在需求率线性依赖于库存水平的情形下,发展了两个变库存费的库存控制模型.     

Survey of Literature on Continuously Deteriorating Inventory Models

This paper presents a complete and up-to-date survey of published inventory literature for the deteriorating (decaying) inventory models. More specifically, those papers are addressed that consider the effect of deterioration as a function of the on-hand level of inventory. The basic features, extensions and generalization of various models are discussed. A classification scheme is presented along with suggestions for future research.