基于损耗率和需求不确定情况下的订购批量

库存管理模型在现实生活中有着广泛的运用。然而,在实际生活中,由于种种不确定性原因的影响,使得经典的确定型的EOQ模型的运用越来越不符合现实的需要;本文将需求和损耗率看成模糊数的同时,将物品的销售价格分成两部分来进行处理,即:没有损耗的产品以一种较高价格出售,对于有部分损耗的产品则按较低的价格出售;采用概率论置信区间估计的方法构建模糊变量的波动区间,构建使得总的利润达到最大的模糊库存模型,并利用三角模糊数、符号距离的方法以及最优化理论进行处理,得出满足条件的最优订购批量。最后,给出了模型分析和算例分析;通过分析,我们发现模糊库存模型的优点在于它自生所具有的不确定性;从数据上看,模糊库存模型比经典的库存模型更能反映出实际情况。     

考虑随机模糊缺陷率且允许缺货的EOQ模型

针对实际库存管理中的产品缺陷问题,研究了含随机模糊缺陷率且允许缺货的经济订购批量(EOQ)模型,并运用随机模糊理论将其转化为确定模型,设计了随机模糊模拟仿真算法进而确定了其最优订购策略.数值算例分析了缺陷率对最优订货量和最优利润的影响.     

含模糊缺陷率且允许缺货的EOQ模型研究

生产系统不完善、自然灾害、运输破损等因素对传统EOQ模型中产品质量100%合格的假设提出了巨大的挑战.本文研究了模糊环境中产品存在质量缺陷情况下允许缺货的库存模型,运用更为一般的LR型模糊数表示缺陷率、缺货成本、订货成本、需求等可能出现不确定情况的参数,建立经济订货批量模型,并运用符号距离法对模型进行反模糊化,确定了使总利润最大化的最优订货批量.最后利用实验仿真验证了模型,分析并揭示了模糊缺陷率等因素对经济订货批量的影响.     

模糊需求下的库存风险及最优库存决策

分析了在模糊需求下,按经典库存模型中的经济订货批量和订货周期所导致的库存风险损失。推导了模糊需求下的经济风险函数。给出了风险函数在模糊需求分布下的重心决策方法,在此基础上得到了模糊需求下经济批量的修正公式,为模糊库存风险分析的研究提供了一种方法。     

安全因子优化与协调模型研究

在需求和供应都不确定的情形下 ,通过模型研究对两阶供应链的安全因子优化与协调作了一些有益的探讨 .本文引入了有效库存水平的概念 ,以反映上游缺货对下游库存的影响 ,在基准库存水平补货模式下构造了的供需双方的库存模型 ,且对安全因子进行整体优化以降低供应链的库存成本 .     

变换核估计在未知需求分布函数条件下多阶段库存控制策略中的应用研究

传统的多阶段库存控制主要致力于库存持有以及过多库存的经济性研究.随机库存模型经常假定需求分布已知,这样可以产生容易解决的方案.但随着销售信息的不断更新,需求分布函数的参数常常未知.这样传统的多阶段库存模型很难产生最优的库存控制策略.当前文献对未知需求分布函数条件下的多阶段库存管理问题研究得不多,当需求分布函数随时间变化,是个多阶段随机规划问题,通常情况难以直接进行求解.针对一般非平稳需求,还缺少有效的库存管理方法.本文致力于变换核估计和优化理论相结合的方法研究未知需求分布函数条件下多阶段库存控制策略,提供一条多阶段库存控制的新思路.可以很好地确定各阶段的最优订货点、最高库存、最低库存等来达到整个系统的最优,从而节省更多的成本,达到营运资本的永久性减少、更高的销售量和客户满意度,从而增加企业的竞争力.     

医院医疗器械—麻醉包库存策略

麻醉包对于医院来说是一种重要的医疗物品,具有需求量大,一旦缺货则损失严重等特点,为此研究了黑龙江省某医院麻醉包的库存问题.以存储费用为标准来评价和优选库存策略,共建立了四大库存模型,分别是经济批量EOQ库存模型;需求离散条件下多周期有准备成本库存模型;需求服从正态分布条件下多周期有准备成本库存模型和蒙特卡罗仿真库存控制模型.其中,三个模型得出了结果,通过证明,一个模型不符合医院实际情况.通过比较上述三个模型求得的最优化库存策略与医院实际使用的库存策略,建议采取蒙特卡罗仿真库存控制模型求解得出的结果作为医院方最优库存策略.     

双向替代产品库存模型研究

运用最优控制理论和数理经济学方法研究了供应链管理时代下广泛存在的双向替代产品的最优库存问题,建立了利润最大化前提下的双向替代品的库存模型,证明了该问题解的存在性,并给出了求解最优订货量的方法步骤,可为实施有效的库存管理、降低企业的物流成本提供借鉴.     

模糊环境下可控提前期供应链库存优化的Stackelberg模型研究

为了有效地缩短提前期与降低库存成本,研究了模糊环境下可控提前期的供应链库存优化问题.利用三角形模糊数描述需求的不确定性,建立了一类模糊需求条件下可控提前期供应链库存优化的Stackelberg模型.利用三角形模糊数描述成本系数的不确定性,建立了模糊成本系数条件下可控提前期供应链库存优化的Stackelberg模型,并提出利用均值面积度量法来解模糊化.通过数值分析来验证两类模型的优化效果.     

VMI条件下具有复合二项随机需求的销售商库存策略研究

考虑一个典型的单一产品的二级供应链系统:单供应商对单销售商,假定系统中销售商的需求分布为复合二项分布,未满足的需求机会损失;补货间隔时间为一随机变量.本文采用概率方法对销售商的需求分布、期望缺货、期望库存周期及库存的稳定性分布进行研究的基础上,构建了使单位时间内销售商的期望库存成本费用最小的库存模型,由此模型便可确定VMI模式下供应商对销售商的库存补货参数s和S,并且给出了在补货响应时间为泊松分布的情况下模型的求解算法,还给出了及时补货响应情况下的5个算例.为补货策略的实施提供了一种简单易于控制的思路和方法.     

一个新的最优模糊存储模型

提出一个新的具有积压定单的关于模糊订购量的模糊存储模型.在模糊函数原理下,给出了模糊总存储成本.为了寻找最优解,把最优模糊存储模型转化为双目标最优化模型,利用L ingo8.0求解不等式约束问题,我们发现最优解都是确定的实数.此外,当模糊订购量和模糊总需求都是三角形(或权重均为1/2梯形)模糊数时,我们提出模型的最优解与经典的具有积压定单存储模型具有相同的结果.     

Fuzzy pricing,marketing and service planning in a fuzzy inventory model: A geometric programming approach

In the real world markets, demand is influenced by different parameters. Recently, many researchers have been interested in integrated production and marketing planning strategies in inventory models where demand depends on different parameters such as price and/or marketing expenditure. The quality of services that are offered to customers of a product is one of the most important parameters that affects demand in the real markets and has not been considered in development of inventory models. On the other hand, the cost parameters in real inventory systems and other parameters such as price, marketing and service elasticity to demand are imprecise and uncertain in nature. So, the notion of fuzziness can be applied to cope with this uncertainty. In this paper, a new fuzzy profit maximization inventory model with shortages is proposed. The demand is considered as a power function of price, marketing expenditure and service expenditure. Furthermore, unit cost is determined as a power function of order quantity. Since the proposed model is in a fuzzy environment, a fuzzy decision should be made to meet the decision criteria, and the results should be fuzzy. Therefore, the proposed model is formulated and solved using geometric programming and fuzzy optimization techniques to derive an approximation of the results’ membership functions. The model is illustrated with a numerical example and finally a case study is provided for evaluation and validation of the results of model.     

模糊随机需求的连续盘点存储策略研究

考虑到需求的模糊随机性,建立模糊随机需求情况下连续盘点存储策略的模糊随机成本模型。利用模糊随机变量的期望值理论,推导出了其成本期望值模型的解析表达式,进而给出了最优再订货点所属区间的判别条件以及最优再订货点和经济订货量的计算式;基于此,设计了一模糊随机需求的连续盘点最优存储策略算法。最后结合数值算例,分析了模糊随机需求概率分布及缺货成本对最优存储策略的影响。     

Long-term inventory policy-making through fuzzy decision-making models

The inventory policy, meant as a replenishment rule, has a considerable impact on most firms. The paper considers the determination of optimal inventory policy of firms from a global viewpoint of top management. The inventory is represented as a fuzzy system with the fuzzy inventory level as the output, the fuzzy replenishment as the input and fuzzy demand. The control problem is formulated in terms of decision-making in a fuzzy environment with fuzzy constraints imposed on replenishments, a fuzzy goal for preferable inventory levels to be attained and the fuzzy decision as the intersection of fuzzy constraints and the fuzzy goal at subsequent stages. The planning horizon is infinite. The problem is to find an optimal time-invariant strategy relating the optimal replenishments to the current inventory levels, maximizing the membership function of fuzzy decision. The existence of such a strategy is proved and an algorithm for its determination is given. The optimal time-invariant strategy obtained is represented as a fuzzy conditional statement equated with a fuzzy relation which is the firm's optimal fuzzy replenishment rule.     

Fuzzy profit measures for a fuzzy economic order quantity model

Changing economic conditions make the selling price and demand quantity more and more uncertain in the market. The conventional inventory models determine the selling price and order quantity for a retailer’s maximal profit with exactly known parameters. This paper develops a solution method to derive the fuzzy profit of the inventory model when the demand quantity and unit cost are fuzzy numbers. Since the parameters contained in the inventory model are fuzzy, the profit value calculated from the model should be fuzzy as well. Based on the extension principle, the fuzzy inventory problem is transformed into a pair of two-level mathematical programs to derive the upper bound and lower bound of the fuzzy profit at possibility level α. According to the duality theorem of geometric programming, the pair of two-level mathematical programs is transformed into a pair of conventional geometric programs to solve. By enumerating different α values, the upper bound and lower bound of the fuzzy profit are collected to approximate the membership function. Since the profit of the inventory problem is expressed by the membership function rather than by a crisp value, more information is provided for making decisions.     

A fuzzy stochastic single-period model for cash management

The major purpose of this paper is to apply a stochastic single-period inventory management approach to analyze optimal cash management policies with fuzzy cash demand based on fuzzy integral method so that total cost is minimized. We will find that, after defuzzification, the cash-raising amounts and the total costs between the fuzzy case and the crisp case are slightly different when the variation of cash demand is small. As a result, we point out that the fuzzy stochastic single-period model is one extension of the crisp models. In any case, one may conclude that a conscientious analysis in fuzzy mathematics like that presented in this paper provides a financial decision maker with a deeper insight into the more real cash management problem.     

Inventory models with a mixture of backorders and lost sales under fuzzy cost

Continuous review and periodic review inventory models in which a fraction of demand is backordered and the remaining fraction is lost during the stockout period are considered under fuzzy environment. Fuzziness is introduced by allowing the cost components imprecise and vague to certain extent. Trapezoidal fuzzy numbers are used to represent these characteristics. The optimum policies of these models under fuzzy costs are derived. Numerical results highlighting the sensitivity in the decision variables are also described.     

Dynamic pricing model and algorithm for perishable products with fuzzy demand

This paper studies the dynamic pricing problem of selling fixed stock of perishable items over a finite horizon, where the decision maker does not have the necessary historic data to estimate the distribution of uncertain demand, but has imprecise information about the quantity demand. We model this uncertainty using fuzzy variables. The dynamic pricing problem based on credibility theory is formulated using three fuzzy programming models, viz.: the fuzzy expected revenue maximization model, α‐optimistic revenue maximization model, and credibility maximization model. Fuzzy simulations for functions with fuzzy parameters are given and embedded into a genetic algorithm to design a hybrid intelligent algorithm to solve these three models. Finally, a real‐world example is presented to highlight the effectiveness of the developed model and algorithm. Copyright © 2009 John Wiley & Sons, Ltd.     

Fuzzy periodic review system with fuzzy random variable demand

In this paper, a periodic review inventory system has been analyzed in a mixed imprecise and uncertain environment where fuzziness and randomness appear simultaneously. A model has been developed with customer demand assumed to be a fuzzy random variable. The lead-time has been assumed to be a constant. The lead-time demand and the lead-time plus one period’s demand have also been assumed to be fuzzy random variables. A methodology has been developed to determine the optimal inventory level and the optimal period of review such that the total expected annual cost in the fuzzy sense is minimized. A numerical example has been presented to illustrate the model.