爱尔朗型按订单装配系统最优生产——库存控制策略研究

本文研究具有复杂装配结构的爱尔朗型按订单装配(ATO)系统的组件生产与库存优化控制问题。系统涉及多种组件,一个最终产品和多类客户需求。在此系统中,各种组件的生产时间服从爱尔朗分布,各类客户的需求为泊松到达过程。针对不同客户需求类型:产品需求与独立组件需求且同为销售损失型,建立基于马尔可夫决策过程(MDP)的平均总成本模型,应用动态规划方法求解最优策略。仿真模拟方法实现最优策略,并通过数值实验分析多生产阶段和系统参数对最优策略的影响。研究结果表明,爱尔朗型生产时间ATO系统的最优策略为状态依赖型策略,即组件的生产与库存分配由动态基础库存水平值和动态库存配给水平值控制。对于任一组件,其基础库存水平值和库存配给水平值均随着生产阶段的增加而降低,且生产阶段对基础库存水平值和平均总成本的影响较显著。     

面向批量订货和多需求类的生产与库存配给

针对一个面向两个需求类的生产企业,根据客户每次订货是否可分批交货,提出了当客户订货可分割和不可分割时供应商的最优生产和库存配给策略.分析表明,供应商的最优生产控制策略可用一个取决于系统状态的基准库存水平表示,最优的库存配给策略则用一个多层的取决于状态的配给水平向量表示.随后,该结论被推广至包含任意多个需求类的生产系统.数值分析验证了文中最优策略的有效性.     

有限耐烦期随机库存系统的最优控制

对供应链内部库存系统进行优化控制是提高供应链协调性的一种重要手段。本文针对实际库存活动中普遍存在的有限耐烦期这种现象,综合考虑顾客退货、库存损失等随机因素对最优库存策略的影响,构造出一种更加贴近实际的随机库存系统最优控制模型,并借助于动态规划原理给出了最优控制律的表达式,然后给出了算例及说明。其结果可为随机库存系统的管理决策提供了理论依据,也可为进一步提高供应链的协调性提供了借鉴。     

随机需求直接发运的运输与库存整合优化研究

分销系统的运输与库存整合优化是供应链管理的重要研究课题.本文分析了随机需求两级分销系统的三种配送策略,并具体给出了基于整车直接发运策略的运输决策与库存控制整合优化数学模型,可得到最优的两级最优库存水平和最优的运输频率.最后对给出的算例,利用MATLAB7的规划求解功能容易获得了问题的最优解.     

供需不确定下基于MOI和VMI模式的供应链协同比较研究

为分析供应链运作机制对降低随机供应商产出和随机需求的影响,对比研究了两供应商-单制造商系统在MOI和VMI两种模式下的供应链协同模型。MOI模式下,制造商管理并持有库存,提出了供应风险共享的协同机制;VMI模式下,供应商管理并持有库存,提出了收益共享与额外惩罚的协同机制。分析了集中决策、MOI和VMI模式下的最优批量决策,证明了VMI模式下存在唯一的纳什均衡。研究还发现,VMI模式更容易协调供应链,有效降低供需不确定的影响。MOI模式下的供应链可实现帕累托改进,但不能实现协调;而当参数满足一定关系时,VMI模式下供应链的期望利润可达到集中决策。     

一类制造商与零售商纵向集成供应链的最优生产与库存策略

对供应链内部库存系统进行优化控制是提高供应链协调性的一种重要手段.而竞争的不断激化加剧了需求的波动,此时作为消化需求波动缓冲库的库存系统对提高供应链的协调性起着重要的作用.本文综合考虑顾客退货、库存自然损失等随机因素对供应链生产和库存策略的影响,构造出一类制造商与零售商纵向集成的供应链的最优生产与库存模型,并借助于动态规划的最优性原理给出了最优生产库存策略的表达式.本文从协调生产与销售的角度探索了提高供应链协调性的方法,不仅对供应链协调性的理论研究而且对实际应用中如何供应链协调性都作了一个崭新的尝试.     

风险偏好和需求不确定性对销售努力和补货决策库存系统的影响

研究风险偏好和需求不确定性对库存和销售努力决策库存系统的影响.运用应用概率中的随机比较方法,分别在一阶和二阶随机占优的意义下给出比较需求分布不同两个系统的最优利润和努力水平的充分条件或充分必要条件.证明存在一类需求分布在一定条件下系统的最优利润和努力水平都随需求可变性的增加而增大.数值例子验证了获得的研究结果.     

多个MRP系统在供应链环境下的协调策略研究

物料需求计划系统的目的是准时生产,减少库存,以实现单个企业内部的最优化生产.供应链管理则是从系统观点出发,纵观全局,力求使供应链整体的效益达到最大化.供应链管理是当前市场环境里最有效的管理方式,但是当采用了物料需求计划系统的多个企业组成供应链时,物料需求计划系统追求企业内部最优的特性和供应链致力于全体最优的特性就会产生冲突,因此必须进行协调合作.在分析了各种可能产生冲突的原因的基础上,提出了一些协调策略.     

随机订单干扰下考虑合并决策的供应链网络调度研究

为满足客户个性化需求的快速响应，企业需具备柔性的外部供应链网络结构，以协同方式共同完成产品生产。本文考虑具有交互特征的多个不同类型协同供应链网络，构建生产成本、库存成本、等待成本以及订单延期交货成本最小化的目标函数，并设计合并决策判断变量构建同类订单在相同协同企业处的开始时间约束。此外，模型中考虑确定订单以及随机订单两种类型订单，并设计随机订单在区间时间段中离散时间点的到达概率。为获取协同供应链网络生产调度优化策略，基于随机订单到达与否的场景构建四个子决策模型，并进一步设计判断提前安排随机订单协同生产和不提前安排随机订单协同生产不同调度策略下成本差异的主决策模型。仿真结果表明合并决策在带来生产成本效益的同时也引起了部分订单的延期交货，且不同类型的协同供应链网络对随机订单的抗干扰能力存在一定程度的差异。     

固定分区下随机需求IRP问题最优策略及算法

随机需求库存-路径问题(stochastic demand inventory routing problem,SDIRP)是典型的NP难题,考虑随机需求环境下供应链中库存与配送问题的协调优化,是实施供应商管理库存策略的关键所在.文章的研究基于固定分区策略(fixed partition policy,FPP),在FPP下客户被分为若干个服务区域,在同一区域中的所有客户均被同时配送.根据分区策略对配送以及库存成本的影响提出了基于修正C-W节约算法的客户分区算法,证明了各区域的最优库存策略为(s,S)形式,分区内各客户的库存策略为order-up-to形式,进而设计了求解FPP下SDIRP最优策略的算法.最后,通过数值算例验证了该算法的有效性以及FPP的适用性.     

Hold Safety Inventory Before,At, or After the Fan‐Out Point?

We consider a product sold in multiple variants, each with uncertain demand, produced in a multi‐stage process from a standard (i.e., generic) sub‐assembly. The fan‐out point is defined as the last process stage at which outputs are generic (outputs at every subsequent stage are variant‐specific). Insights gained from an analytical study of the system are used to develop heuristics that determine the stage(s) at which safety inventory should be held. We offer a relatively‐simple heuristic that approaches globally‐optimal results even though it uses only two relatively‐local parameters. We call this the VAPT, or value‐added/processing time heuristic, because it determines whether a (local) stage should hold inventory based only on the value added at that local stage relative to its downstream stage, along with the processing time at that local stage relative to its downstream stage. Another key insight is that, contrary to possible intuition, safety inventory should not always be held at the fan‐out point, although a fan‐out point does hold inventory under a wider range of conditions. We also explore when postponement is most valuable and illustrate that postponement may often be less beneficial than suggested by Lee and Tang (1997).     

Bayesian Inventory Management with Potential Change‐Points in Demand

We consider the inventory management problem of a firm reacting to potential change points in demand, which we define as known epochs at which the demand distribution may (or may not) abruptly change. Motivating examples include global news events (e.g., the 9/11 terrorist attacks), local events (e.g., the opening of a nearby attraction), or internal events (e.g., a product redesign). In the periods following such a potential change point in demand, a manager is torn between using a possibly obsolete demand model estimated from a long data history and using a model estimated from a short, recent history. We formulate a Bayesian inventory problem just after a potential change point. We pursue heuristic policies coupled with cost lower bounds, including a new lower bounding approach to non‐perishable Bayesian inventory problems that relaxes the dependence between physical demand and demand signals and that can be applied for a broad set of belief and demand distributions. Our numerical studies reveal small gaps between the costs implied by our heuristic solutions and our lower bounds. We also provide analytical and numerical sensitivity results suggesting that a manager worried about downside profit risk should err on the side of underestimating demand at a potential change point.     

COMPONENT COMMONALITY: MODELS WITH PRODUCT‐SPECIFIC SERVICE CONSTRAINTS*

Common components are used extensively for reasons including product postponement and expediting new product development. We consider a two‐stage assemble‐to‐order system with two products having uniformly distributed demand, one common component, and product‐specific components. We develop optimization models in which the cost‐minimizing inventory of the components must be determined and allocated to products in order to meet product‐specific service level constraints. We compare two different commonality models based on whether or not the products are prioritized. A distinctive feature of our study is the use of product‐specific service levels. We compare our results with models using aggregate service levels.     

Inventory Rationing for Multiple Class Demand under Continuous Review

We consider how a firm should ration inventory to multiple classes in a stochastic demand environment with partial, class‐dependent backlogging where the firm incurs a fixed setup cost when ordering from its supplier. We present an infinite‐horizon, average cost criterion Markov decision problem formulation for the case with zero lead times. We provide an algorithm that determines the optimal rationing policy, and show how to find the optimal base‐stock reorder policy. Numerical studies indicate that the optimal policy is similar to that given by the equivalent deterministic problem and relies on tracking both the current inventory and the rate that backorder costs are accumulating. Our study of the case of non‐zero lead time shows that a heuristic combining the optimal, zero lead time policy with an allocation policy based on a single‐period profit management problem is effective.     

Class‐Based Storage with a Finite Number of Items: Using More Classes is not Always Better

Class‐based storage is widely studied in the literature and applied in practice. It divides all stored items into a number of classes according to their turnover. A class of items with higher turnover is allocated to a region closer to the warehouse depot. In the literature, it has been shown that the use of more storage classes leads to a shorter travel time for storing and retrieving items. A basic assumption in this literature is that the required storage space for all items equals their average inventory level, which is valid only if an infinite number of items can be stored in each storage region. This study revisits class‐based storage by considering each storage space to contain only a finite number of items. We develop a travel time model and an algorithm that can be used for determining the optimal number and boundaries of storage classes in warehouses. Different from the conventional research, our findings illustrate that commonly a small number of classes is optimal. In addition, we find the travel time is fairly insensitive to the number of storage classes in a wide range around the optimum. This suggests that a manager can select a near‐optimal number of storage classes in an easy way and need not be worried about the impact of storage‐class reconfigurations. We validate our findings for various cases, including different ABC‐demand curves, space‐sharing factors, number of items, storage rack shapes, discrete storage locations, and stochastic item demand.     

An Entropy‐Based Methodology for Valuation of Demand Uncertainty Reduction

We propose a distribution‐free entropy‐based methodology to calculate the expected value of an uncertainty reduction effort and present our results within the context of reducing demand uncertainty. In contrast to existing techniques, the methodology does not require a priori assumptions regarding the underlying demand distribution, does not require sampled observations to be the mechanism by which uncertainty is reduced, and provides an expectation of information value as opposed to an upper bound. In our methodology, a decision maker uses his existing knowledge combined with the maximum entropy principle to model both his present and potential future states of uncertainty as probability densities over all possible demand distributions. Modeling uncertainty in this way provides for a theoretically justified and intuitively satisfying method of valuing an uncertainty reduction effort without knowing the information to be revealed. We demonstrate the methodology's use in three different settings: (i) a newsvendor valuing knowledge of expected demand, (ii) a short life cycle product supply manager considering the adoption of a quick response strategy, and (iii) a revenue manager making a pricing decision with limited knowledge of the market potential for his product.     

Retailer Transshipment versus Central Depot Allocation for Supply Network Design

We consider a supply chain structure with shipments from an external warehouse directly to retailers and compare two enhancement options: costly transshipment among retailers after demand has been realized vs. cost‐free allocation to the retailers from the development of a centralized depot. Stochastic programming models are developed for both the transshipment and allocation structures. We study the impact of cost parameters and demand coefficient of variation on both system structures. Our results show an increasing convex relationship between average costs and demand coefficient of variation, and furthermore that this increase is more pronounced for the allocation structure. We employ simulation and nonlinear search techniques to computationally compare the cost performance of allocation and transshipment structures under a wide range of system parameters such as demand uncertainty and correlation; lead times from the external warehouse to retailers, from warehouse to central depot, and from depot to retailers; and transshipment, holding, and penalty costs. The transshipment approach is found to outperform allocation for a broad range of parameter inputs including many situations for which transshipment is not an economically sound decision for a single period. The insights provided enable the manager to choose whether to invest in reducing lead times or demand uncertainty and assist in the selection of investments across identical and nonidentical retailers.     

Economic order quantity model with trade credit financing for non-decreasing demand

Researchers in the past have established their inventory lot-size models under trade credit financing by assuming that the demand rate is constant. However, from a product life cycle perspective, it is only in the maturity stage that demand is near constant. During the growth stage of a product life cycle (especially for high-tech products), the demand function increases with time. To obtain robust and generalized results, we extend the constant demand to a linear non-decreasing demand function of time. As a result, the fundamental theoretical results obtained here are suitable for both the growth and maturity stages of a product life cycle. In addition, we characterize the optimal solutions and obtain conclusions on important and relevant managerial phenomena. Lastly, we provide several numerical examples to illustrate the proposed model and its optimal solution.     

面向产品生命周期的部分柔性技术选择

本文研究随机需求下产品生命周期不同阶段的部分柔性技术选择与生产能力规划问题。部分柔性技术是相对于完全柔性技术而言的,一种生产技术的柔性程度定义为能生产一产品类中产品个数多少的能力。不同柔性强度的生产技术,其投资成本和运行成本不同。本文首先建立了以计划期上总成本最小为目标的技术选择和生产能力规划模型,然后根据产品在其生命周期不同阶段的特点与市场需求的特点,应用所建立的模型进行仿真并总结产品导入期和成熟期技术选择的特点。