Base stock policy with retrial demands

In this work, we consider a continuous review base stock policy inventory system with retrial demands. The maximum storage capacity is S. It is assumed that primary demand is of unit size and primary demand time points form a Poisson process. A one-to-one ordering policy is adopted. According to this policy, orders are placed for one unit, as and when the inventory level drops due to a demand. We assume that the demands occur during the stock-out periods enter into the orbit of infinite size. The lead time is assumed to be exponential. The joint probability distribution of the inventory level and the number of demands in the orbit are obtained in the steady state case. Various system performance measures in the steady state are derived. The results are illustrated with suitable numerical examples.     

Lead time and ordering cost reductions in continuous review inventory systems with partial backorders

This paper investigates the impact of ordering cost reduction on the modified continuous review inventory systems involving variable lead time with a mixture of backorders and lost sales. The objective is to simultaneously optimise the order quantity, ordering cost, reorder point and lead time. We first assume the lead time demand follows a normal distribution, then relax this assumption to consider the distribution free case where only the mean and variance of lead time demands are known. An algorithm procedure of finding the optimal solution is developed, and two numerical examples are given to illustrate the results.     

Disassembly EOQ models with price-sensitive demands

The disassembly economic order quantity problem is to determine the quantities of a product to be disassembled at different times over an infinite planning horizon by considering ordering, operation, and inventory costs. The demands for the components are independent, which can lead to accumulations of unnecessary inventories over time. This article proposes the models which integrate price-sensitive demands and disposal decisions in disassembly economic order quantity problems to maximize the profit of disassembly systems without inventory accumulations. Three models are developed and analyzed to obtain solution approaches that give prices, the replenishment cycle time (or, equivalently, the order quantity), and the disposal quantity. The inventory policy integrating both pricing and disposal decisions allows higher profits to be achieved. A numerical experiment shows its efficiency and highlights its potential implementation in practical cases.     

Reducing lost-sales rate on the stochastic inventory model with defective goods for the mixtures of distributions

In this paper, we assume that the demands of different customers are not identical in the lead time. Thus, we investigate a continuous review inventory model involving controllable lead time and a random number of defective goods in buyer’s arriving order lot with partial lost sales for the mixtures of distributions of the lead time demand to accommodate more practical features of the real inventory systems. Moreover, we analyze the effects of increasing investment to reduce the lost sales rate when the order quantity, reorder point, lost sales rate and lead time are treated as decision variables. In our studies, we first assume that the lead time demand follows the mixture of normal distributions, and then relax the assumption about the form of the mixture of distribution functions of the lead time demand and apply the minimax distribution free procedure to solve the problem. By analyzing the total expected cost function, we develop an algorithm to obtain the optimal ordering policy and the optimal investment strategy for each case. Finally, we provide numerical examples to illustrate the results.     

(s, S) Inventory systems with lost sales and Markov renewal demands

In the study of stochastic inventory systems it is generally assumed that the demand epochs are renewable in nature. The present paper deals with a single-item (s, S) inventory model with a finite number of different types of demands, in which the demand epochs form a Markov renewal process. The lead times are exponentially distributed and the demands that occur during stockout periods are not backordered. For this model the transient and limiting inventory level distributions are computed. Also the theory of point processes is used to obtain the mean reorder and shortage rates and their limiting values. For the special case of renewal demands, the problem of minimizing the long-run expected cost rate is analysed.     

Analysis of s,S inventory systems with general lead time and demand distribution and adjustable reorder size

Stochastic Inventory systems of (s, S) type with general lead time distribution are studied when the time intervals between successive demands are independently and identically distributed. The demands are assumed to occur for one unit at a time and the quantity reordered is subject to review at the epoch of replenishment so as to level up the inventory to S. An explicit characterization of the inventory level is provided. The model is flexible enough to allow complete backlogging and or deal with shortages. A general method of dealing with cost over an arbitrary time interval is indicated. Special cases are discussed when either the lead time or the interval between successive demands is exponentially distributed.     

Back Ordering in Inventory Control

An approach to determining the empirical relationship between time out of stock and back ordering is outlined. Using this relationship the expected lost sales in a reorder level system of inventory control are derived for continuous and block demand depletion, fixed and variable lead times. Their applicability to a generalized inventory model is discussed.     

Determining Net Requirements for Material Requirements Planning

This paper presents a design guideline for netting systems that determine net requirements, for push-type production ordering. Two alternative netting systems (full and single) are formulated in an N stage production and inventory system. The performances of the two alternatives are discussed through numerically analysing production ordering variations and inventory level variations at each stage in the processes. The results obtained suggest that: (1) the full netting system is preferable at the production stages, where variance in the accumulated forecast consumption error by the immediately succeeding stage is smaller than the variance in the accumulated forecast market demand error over a production ordering interval and the lead time for the stage. (2) The single netting system may be preferable at the other stages, particularly in regard to production ordering variations.     

Integrated vendor–buyer cooperative inventory models with controllable lead time and ordering cost reduction

This study deals with the lead time and ordering cost reduction problem in the single-vendor single-buyer integrated inventory model. We consider that buyer lead time can be shortened at an extra crashing cost which depends on the lead time length to be reduced and the ordering lot size. Additionally, buyer ordering cost can be reduced through further investment. Two models are presented in this study. The first model assumes that the ordering cost reduction has no relation to lead time crashing. The second model assumes that the lead time and ordering cost reduction are interacted. An iterative procedure is developed to find the optimal solution and numerical examples are presented to illustrate the results of the proposed models.     

Optimal policy for brownian inventory models with general convex inventory cost

We study an inventory system in which products are ordered from outside to meet demands, and the cumulative demand is governed by a Brownian motion. Excessive demand is backlogged. We suppose that the shortage and holding costs associated with the inventory are given by a general convex function. The product ordering from outside incurs a linear ordering cost and a setup fee. There is a constant leadtime when placing an order. The optimal policy is established so as to minimize the discounted cost including the inventory cost and ordering cost.     

Models for 1-out-of Q systems with stochastic lead times and expedited ordering options for spares inventory

The purpose of this article is to develop two kinds of continuous-time cyclic inventory models with stochastic lead times and expedited ordering options. The order form under consideration is similar to that arising in the spare part inventory management. Especially, the control problem to determine the timing for the regular order is considered, and the optimal policy which minimizes the long-run average cost is analytically characterized. Finally, numerical examples are presented to evaluate the uncertainty of stochastic lead times, and the optimal inventory policy, which is composed of both the ordering time and the order quantity, is numerically calculated.     

An Inventory System with Postponed Demands

Abstract

In this article we consider a continuous review perishable inventory system in which the demands arrive according to a Markovian arrival process (MAP). The items in the inventory have shelf life times that are assumed to follow an exponential distribution. The inventory is replenished according to an (s, S) policy and the replenishing times are assumed to follow a phase type distribution. The demands that occur during stock out periods either enter a pool which has capacity N (<∞) or leave the system. Any demand that arrives when the pool is full and the inventory level is zero, is also assumed to be lost. The demands in the pool are selected one by one, if the replenished stock is above s, with interval time between any two successive selections is distributed as exponential with parameter depending on the number of customers in the pool. The joint probability distribution of the number of customers in the pool and the inventory level is obtained in the steady state case. The measures of system performance in the steady state are derived and the total expected cost rate is also calculated. The results are illustrated numerically.     

Determining optimal order splitting and reorder level for N-supplier inventory systems

This paper considers multiple-supplier single-item inventory systems, where the item acquisition lead times of suppliers and demand arrival are random, and backorder is allowed. The acquisition takes place when the inventory level depletes to a reorder level, and the order is split among multiple suppliers. The acquisition lead times may have different distributions, the unit purchasing prices from suppliers may be different, and thus the order quantities for different suppliers may be different. The problem is to determine the reorder level and order quantity for each supplier so that the expected total cost per unit time, consisting of the fixed ordering cost, procurement cost, inventory holding cost and shortage cost, is minimized. We develop a mathematical model describing the system in detail. We also conduct extensive numerical experiments to analyze the advantages and distinct characteristics of multiple-supplier systems.     

The multi-period newsboy problem

We will try to generalize the so-called newsboy model so that we can deal with unsatisfied demand or unsold quantity. Consider the time interval that consists of multiple ordering cycles. Assume that the probability density function of demand is given for each cycle. Then our problem is to make the ordering plan with which we can maximize the expected profit. In the classical newsboy model ordering quantity is always equal to the (planned) initial inventory level. But if we take account of unsatisfied demand and unsold quantity, the (desired) ordering quantity must be determined by a proper stochastic rule. Then, in stead of determining the ordering quantity of each cycle, we must plan the initial inventory level so that the expected profit may be maximized. If unsold exists in present cycle, the ordering quantity of next cycle becomes smaller than the planned inventory level. And if unsatisfied demand exists in the present cycle, the ordering quantity of next cycle becomes larger than the planned inventory level.     

Two-Commodity Inventory System with Individual and Joint Ordering Policies and Renewal Demands

Abstract

This article analyzes a two-commodity continuous review inventory system with renewal demands. The ordering policy is a combination of policies namely ordering individual commodities and ordering jointly both commodities. The steady state probability distribution for the joint inventory levels is computed. Various system performance measures in the steady state are derived. The results are illustrated numerically.     

Inventory Problems with Partially Observed Demands and Lost Sales

This paper considers the case of partially observed demand in the context of a multi-period inventory problem with lost sales. Demand in a period is observed if it is less than the inventory level in that period and the leftover inventory is carried over to the next period. Otherwise, only the event that it is larger than or equal to the inventory level is observed. These observations are used to update the demand distributions over time. The state of the resulting dynamic program consists of the current inventory level and the current demand distribution, which is infinite dimensional. The state evolution equation for the demand distribution becomes linear with the use of unnormalized probabilities. We study two demand cases. First, the demands evolve according to a Markov chain. Second, the demand distribution has an unknown parameter which is updated in the Bayesian manner. In both cases, we prove the existence of an optimal feedback ordering policy. Permanent address of J. Adolfo Minjárez-Sosa: Departamento de Matemáticas, Universidad de Sonora, Hermosillo, Sonora, México. This project was partially supported by NSF Grant 0509278, ARPATP Grant 009741-0019-2006, and CONACYT (Mexico) Grant 46633-F.     

A periodic review inventory system with two supply modes

We describe a periodic review inventory system in which there are two modes of resupply, namely a regular mode and an emergency mode. Orders placed through the emergency channel have a shorter supply lead time but are subject to higher ordering costs compared to orders placed through the regular channel. We analyze this problem within the framework of an order-up-to-R inventory control policy. At each epoch, the inventory manager must decide which of the two supply modes to use and then order enough units to raise the inventory position to a level R. We show that given any non-negative order-up-to level, either only the regular supply mode is used, or there exists an indifference inventory level such that if the inventory position at the review epoch is below the indifference inventory level, the emergency supply mode is used. We also develop procedures for solving for the two policy parameters, i.e., the order-up-to level and the indifference inventory level.     

On learning process of a newsvendor with censored demand information

A behavioural study on the newsvendor problem without demand distribution knowledge or realized demands is conducted. We configure a censored-information scenario, where only sales data in history are provided. With laboratory experiments, we analyse participants ordering levels and ordering oscillations over time. It is observed that participants perform a significant learning process in the censored-information scenario. Moreover, we find that participants make orders by anchoring on the previous-period sales and adjusting to an adapted inventory level. On the basis of the observations, we propose an exponential-type learning (EXP) model to describe the behaviour of decision makers. Comparing with a chasing model that is popularly used in the full-information scenario, the EXP model is more recommended in the censored-information scenario.     

需求分布未知且提前期可控的联合库存决策

在提前期内需求为自由分布且提前期依赖于订购批量和生产率的条件下,建立了同时考虑供需双方成本的联合库存决策模型,运用最小最大准则分析了模型最优解的存在性,设计了高效的最优解搜索算法.通过数值算例说明算法的有效性,分析了获取需求分布信息的代价与收益;并与供需独立的决策模型比较,表明了联合库存决策可以显著地降低供应链成本.     

具有Weibull生存死亡特征的冷链品库存补货定价策略研究

建立了无限期内冷链品具有Weibull生存死亡特征、随机需求且受售价影响的库存补货定价模型,其中售价是连续变化的,需求率是售价的指数函数,变质率服从的三参数Weibull分布,提前期固定。系统以利润最大化为目标函数,在(r,Q)库存策略下,建立库存模型,采用直接法,对模型近似求解,得到最优补货定价策略。利用Matlab进行算例模拟和灵敏度分析发现:补货提前期和单位仓储成本对补货定价策略影响较大,二者增大会导致系统利润降低;单位处理成本的增加,在一定程度上使得系统降低最优补货量,使系统利润增加;保鲜期固定的前提下,受冷链品的流动环境因子和存储环境因子影响的变质率对补货定价策略影响较大,它的增大会使系统利润降低。这些发现能够帮助优化系统模型,对现实问题具有一定的指导意义。