机会损失最小化报童模型中的订购决策研究

结合条件风险值(conditional value-at-risk,CVaR)准则对机会损失最小化报童模型中零售商的订购决策进行研究.研究结果表明:当订购过量损失大于订购不足损失时,零售商基于CVaR机会损失最小化的订购量小于期望机会损失最小化的订购量,且随着零售商对风险厌恶程度的增加而减少;反之,当订购过量损失小于订购不足损失时,零售商基于CVaR机会损失最小化的订购量大于期望机会损失最小化的订购量,且随着零售商对风险厌恶程度的增加而增加;随着零售商对风险规避程度的增加,零售商基于CVaR机会损失最小化的订购量所对应的期望利润和期望机会损失分别减少和增加,即低风险意味着低收益,高收益伴随着高风险.     

带有二次订购和二次销售的报童问题

本文提出二次进货二次销售的报童模型 ,并分析了新模型与经典报童问题和带有反馈生产模型的最优订购量及收益关系 ,分析了模型的灵敏度 ,服务水平等 .     

三种不同需求分布条件下报童问题期望费用的计算

报童问题是运筹学中典型的随机性存贮模型.目前很多运筹学和管理科学教科书一般只给出报童问题最优订购量应该满足的条件,而没有给出具体的期望费用的表达式,这对于报童问题模型在实际中的应用造成不便.研究了三种不同需求分布条件下报童问题期望费用计算问题,所得出的关于期望费用计算公式具有简洁和便于实际应用的特点.     

带有随机订货点的两阶段报童模型

销售商可通过二次订货以达到降低风险、增加利润的目的,然而在实际中由于生厂商供货能力不足等不可控因素,销售商往往无法确定何时能进行第二次订货。针对这种现象,本文研究二次订货时间不确定的报童问题,假设随机订货时间点和需求率均服从均匀分布,建立了带有随机订货点的两阶段报童模型,给出了两阶段最优期望总订货量,使得零售商在整个销售期内的期望利润达到最大值。最后通过数值算例,对比分析了本文的二次订购模型与传统一次订购模型,研究结果指出在整个销售期内二次订货可以提高零售商的期望利润。     

两类供应风险下装配商的最优订购策略

针对两类供应风险(不确定产能与随机产出率)下装配制造商的零部件订购决策这一难题,运用随机非线性规划方法,以装配商期望利润最大化为目标,建立零部件订购决策的多维优化模型,刻画了确定需求下的最优订购量,并对其进行了灵敏性分析。最后,通过数值算例验证了模型结论并进一步探讨不同类供应风险的影响,为装配商的零部件订购决策和风险管理提供有益的管理启示。     

风险厌恶条件下竞争报童问题的二层规划模型

有效应用数学规划方法研究报童类产品的库存管理,能够降低企业的成本和风险,从而提高企业的经济效益.此类问题的研究,目前主要集中在应用Nash博弈研究竞争报童问题以及零售商所持风险态度对供应链库存水平的影响.试图研究序贯决策下的竞争报童问题模型以及零售商所持风险态度对供应链库存水平的影响.由此建立了二层规划模型.算例表明序贯决策的竞争报童问题中,总订购量随风险厌恶程度的提高而降低.     

一类过程需求存贮系统的策略研究及其费用弹性分析

本文首先讨论了需求到达为复合泊松随机过程的库存管理问题,给出了在单位时间内期望总成本费用最小的条件下的确定性的最优订货策略(Q,T).然后分析了在订购量和订购周期为随机变量,其联合分布已知的条件下,基于随机局部弹性理论,分析了总费用关于订购量和订购周期的局部弹性的联合分布,为订购策略的制定提供了合理的依据.     

公平偏好下基于回购契约的供应链协调模型

在报童模型的基础上,以一个供应商和一个零售商组成的两级供应链为研究背景,把公平关切引入到回购契约的研究中,并根据Nash讨价还价博弈思想建立公平偏好框架,通过建立效用函数模型,研究了公平关切对回购契约的影响．研究发现,公平关切不会影响回购契约的协调性,公平关切程度也不会影响零售商的最优订购量,进而可以得出,即使供应商在不知道零售商对公平偏好的程度下,依然可以通过制定合适的批发价格和回购价格来做出使自己效用最大化的最优决策．另外,通过灵敏度分析发现,在公平关切时,零售商的最优订购量会随着产品单位生产成本的增加而增加．     

价格数量折扣下多阶段报童问题的在线策略

价格数量折扣可以提高订购量, 是库存决策中的一个重要因素. 特别地, 当订购量达到一定水平时, 价格折扣才会发生. 应用理论计算机科学兴起的弱集成算法, 研究具有这种价格数量折扣的多阶段报童问题的在线策略. 弱集成算法是一种在线序列决策算法, 其主要特点是不对未来输入做任何统计假设, 克服了报童问题研究中需要对需求做概率假设的困难. 主要将弱集成算法应用到固定订购量的专家策略, 给出了价格数量折扣下多阶段报童问题的具体在线策略;得到了该在线策略相对于最优专家策略的理论保证. 进一步将回收价值和缺货损失费引入, 给出了推广的在线策略及其理论结果. 最后应用数值算例说明了给出的在线策略具有较好的竞争性能.     

基于报童模型的过度自信零售商的订货决策与协调研究

根据以往过度自信的研究,我们借用报童模型,设定了一个期望需求及方差预测都存在偏差的过度自信零售商,探究了该零售商与理性零售商在订购量及利润方面的差别,算出了过度自信零售商的利润损失。并且分情况讨论,供应商是否能采取契约机制实现有过度自信零售商的供应链利润最大化。     

多周期报童模型在煤炭物资库存管理中的应用

为了降低煤炭企业物资库存管理成本,本文结合郑州煤电物资供销公司的实际情况,提出了考虑供过于求时,剩余物资对库存以及订货量影响的多周期库存模型。不同于经典报童模型中以订货量为自变量,本模型以期望利润为目标函数,以初始库存为自变量,在期望利润最大的情况下,得出每个周期的初始库存水平。通过郑州煤电物资供销公司的物资库存管理实例计算,结果表明可降低库存成本50%左右,说明该模型合理可行。     

A semi-analytical solution for a newsboy problem with mid-period replenishment

This paper is an exploratory study of a very common variation of the classical newsboy problem: in addition to the supply at the beginning of the sales period, an additional replenishment opportunity exists sometime during the sales period. A basic model for the problem is presented and a semi-analytical solution procedure is developed. Numerical solutions obtained reveal certain interesting properties of the model. The paper shows that the replenishment extension makes the newsboy problem much more challenging managerially and also that it can be practically solved with significant profit implications.     

The Budget Constrained Multi-Product Newsboy Problem with Reactive Production: A Problem from Entrepreneurial Network Construction

This paper develops an extended newsboy model and presents a formulation for this model. This new model has solved the budget contained multi-product newsboy problem with the reactive production. This model can be used to describe the status of entrepreneurial network construction. We use the Lagrange multiplier procedure to deal with our problem, but it is too complicated to get the exact solution. So we introduce the homotopy method to deal with it. We give the flow chart to describe how to get the solution via the homotopy method. We also illustrate our model in both the classical procedure and the homotopy method. Comparing the two methods, we can see that the homotopy method is more exact and efficient.     

传统离散型报童决策模型的一个问题及其改进方案

实践表明 ,对状态变量的递增量较大的报童问题 ,应用传统的离散型报童模型所确定的最优决策量与实际最优方案之间可能存在较大偏差 .分析了这一偏差出现的原因 ,提出了状态变量递增量较大的情况下的离散变量报童模型和求解方法 ,并证明这一模型比原模型得出的求解结果更为精确 .最后 ,通过算例验证了新模型对原模型的改进 .     

Inventory control problem with freight cost and stochastic demand

This paper generalizes the standard newsboy model to the case including freight cost, in which the capacity of one container is the limit and the freight cost is proportional to the number of the containers used. We show that the optimal ordering quantity is either the newsboy solution or some multiple of the container’s capacity. We also propose an algorithm to compute the optimal policy. Furthermore, we generalize these results to the case in which the inventory and the price are determined jointly with emergency purchase.     

A stochastic inventory problem with fuzzy shortage cost

Up to now, many inventory models have been considered in the literature. Some assume stochastic demands and others consider the deterministic case. Though they include a shortage cost due to lost sales, it is usually assumed to be known concretely and a priori. This paper introduces fuzziness of shortage cost explicitly into the classical newsboy problem. That is, we investigate the so-called fuzzy newsboy problem where its shortage cost is vague and given by an L shape fuzzy number. Then the total expected profit function also becomes a fuzzy number. Finally, we find an optimal ordering quantity realizing the fuzzy max order of the profit function (fuzzy min order considering the profit function) and compare it with the optimal ordering quantity of the non-fuzzy newsboy problem.     

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.     

The Distribution Free Newsboy Problem with Balking

The purpose of this paper is to study the classic newsboy model with more realistic assumptions. First, we allow customers to balk when inventory level is low. Secondly, we relax the assumption that the cumulative distribution function of the demand is completely known and merely assume that the first two moments of the distribution function are known.     

The distribution-free newsboy problem under the worst-case and best-case scenarios

New theoretical foundations for analyzing the newsboy problem under incomplete information about the probability distribution of random demand are presented. Firstly, we reveal that the distribution-free newsboy problem under the worst-case and best-case demand scenarios actually reduces to the standard newsboy problem with demand distributions that bound the allowable distributions in the sense of increasing concave order. Secondly, we provide a theoretical tool for seeking the best-case and worst-case order quantities when merely the support and the first k moments of the demand are known. Using this tool we derive closed form formulas for such quantities in the case of known support, mean and variance, i.e. k = 2. Consequently, we generalize all results presented so far in literature for the worst-case and best-case scenarios, and present some new ones. Extensions of our findings to the cases of the known mode of a unimodal demand distribution, the known median, and to other stochastic inventory problems are indicated.