时变需求环境下周转库存优化策略研究

本文研究了供应链管理领域的一个热点问题——库存优化问题。针对周转库存的优化问题,本文在需求量未必能被时间解析表示的条件下给出了一个能够求得局部最优解的方法。方法要求先将周转库存的优化问题转化为一个求集合最优划分的问题,然后用最快下降法求出一个带条件的最优解,最后再给出一个进一步调整这个带条件的最优解的算法。     

需求依赖于库存的易变质品在线采购问题研究

根据现实生活中常见的库存量越大越吸引消费者购买兴趣的现象,本文建立了需求依赖于库存的易变质品采购模型。在不假设价格服从任何分布的条件下,提出了采购价格不确定背景下的易变质品在线采购策略。本文采用在线问题及其竞争分析的方法来进行建模分析,设计出有效的在线竞争采购价格驱动的(s,S)策略,与离线最优策略进行比较,得出理论竞争比和最优经济采购数量和采购时间间隔。最后,通过数值算例说明该策略在现实中具有较好的实际竞争性能比,且在多个价格序列下都表现良好,从而说明了该策略是鲁棒的,可以为企业提供有价值的决策建议。     

连锁企业多品种库存分布研究

近几年来,连锁经营在我国得到了迅猛发展.本文以我国零售企业开展连锁经营迫切需要解决的多品种库存分布问题为出发点,从供应链管理的角度,在分析和借鉴前人研究成果的基础上,提出一整套适合我国连锁经营配送业务的算法模型.同时,根据配送中心实际运作的需要,采用联合订(补)货与联合配送结合的方式,有效解决了多品种库存分布问题.     

具有多元马氏需求的多产品多阶段库存优化模型

本文研究一类新的多产品库存控制策略,即具有多元马氏需求特征的多产品多阶段的订货点订货量(Q, R, SS)策略,该策略考虑市场需求在不同产品之间具有多元马氏转移特征,并考虑缺货因素设置安全库存。论文首先建立了多产品多阶段的多元马氏需求预测模型,并通过该模型确定了各种产品需求之间的关系。同时,在该模型的理论基础上,提出了多产品多阶段的总期望成本模型及其最优(Q, R, SS)策略,进而结合算例给出模型的最优策略的数值解。     

多类顾客环境下报童模型中库存分配策略研究

考虑一个报童模型中多类顾客的库存分配问题,将顾客按照他们愿意支付价格的高低划分为不同级别。零售商在销售期初决定产品订货量,并在销售期内决定接受或者拒绝不同顾客的需求,以最大化销售期内的期望总利润。将销售期分成大量足够小的时间单位,通过建立一个反向Bellman动态规划方程,以优化每个时间单位内的库存分配策略,并得到了零售商最优的期初订货量。通过与没有库存分配策略下零售商的期望利润进行比较,算例分析得出库存分配策略可以大幅提高零售商的利润。这主要是因为通过库存分配可以使得零售商从高端顾客中获取更多利润,同时能够减小期初的订货量,以节约采购成本和库存持有成本。     

基于CPFR的多产品分销系统库存优化模型

本文中的分销系统由生产多种产品的多个制造商,一个地区分销中心DC,多个零售商所组成,系统采用基于CPFR来确定订货临界点,并且在假设DC和零售商都实行连续性盘点的(R,Q)库存控制策略,提前期为随机变量,零售商需求为泊松分布的前提下,以整个分销系统的库存成本最小化为目标函数,以DC和零售商的多产品服务水平为约束条件,通过确定最佳订货批量,建立了此多产品分销系统的库存优化模型,从而达到有效控制库存的目的.     

折扣下多阶段易腐品库存问题的无概率解决方法北大核心CSCDCSSCI

The classic perishable inventory problem assumes that the retailer should not be affected by upstream suppliers in the ordering process. However, real-world operation and management entail a relationship of competition and cooperation between retailers and suppliers. Suppliers therefore influence retailers′ decision-making processes. Suppliers often use discount marketing, which is effective in attracting retailers and increasing the sales volume of products. This also allows retailers to order products at lower cost, creating a win-win scenario. Value discounts and quantity discounts are commonly used in discount marketing. Value discounts allow a retailer to enjoy a cash discount when the retailer′s total order value reaches a certain value, and quantity discounts refer to a price discount offered by the supplier for all products when the retailer′s single order quantity exceeds a fixed value. Most studies in the literature on inventory management assume that the demand or demand distribution is known;however, in reality, it is difficult to obtain accurate demand and demand distribution. Historical demand data are usually the only information that is available. Developments in theoretical computer science allow online learning algorithms to completely depart from the assumption of statistical information and rely only on historical data for decision-making. The Weak Aggregating Algorithm (WAA), an online learning algorithm, constructs online strategy by learning from different experts′ advice and aggregating it;the online strategy can thus track the best expert advice. This assists online decision makers in effectively integrating various types of information and making decisions on such information. This study expands the research on the single-product, multi-period perishable inventory problem in the context of value discounts and quantity discounts without any assumption of statistical information. This article applies the WAA to learning from different experts′ advice in studying the perishable inventory problem when value discounts and quantity discounts can be used. The online ordering strategies (probability-free solutions) are constructed as follows. First, at the beginning of every period, experts recommend their static advice for the specific perishable inventory decision problem;such expert advice is fixed and will not be changed throughout all periods. Second, in the case of value discounts or quantity discounts, the online decision maker applies the WAA to assign different degrees of trust to different experts′ advice and then integrates the advice to make a decision. Last, the actual decision result of the decision problem is obtained, and cumulative gains are computed to evaluate the performance of the experts and the online decision maker. This paper constructs online ordering strategies in different situations. For real-valued order quantities, we aggregate experts′ advice to propose an online order strategy that considers value discounts in detail. We analyze a more practical case in which the order quantity is an arbitrary integer by constructing an online ordering strategy under value discounts through learning from expert advice. We then approach the matter from continuous and discrete angles to propose two explicit online order strategies under quantity discounts. This paper further verifies the performance of the proposed online ordering strategies through theoretical analysis and the use of numerical examples. Competitive performance is measured as the difference between the cumulative gains from using the online ordering strategies and the cumulative gains from following the best expert advice. The theoretical results show that the cumulative gains of the given online ordering strategies are as large as those of the best expert advice and that they outperform their benchmark strategies. The numerical examples indicate that the cumulative gains achieved by the given online ordering strategies fluctuate slightly around those achieved by the optimal expert advice. The competitive performance of the discrete online ordering strategies outperforms that of the continuous online ordering strategies. The theoretical results are therefore effectively validated. The numerical examples also show that different initial probability distributions have little impact on strategies′ competitive performance. In the case of value discounts, the online ordering strategy has better competitive performance for perishable products with a higher unit price, a lower unit cost, and higher value discounts. Regarding quantity discounts, the online ordering strategy performs better when the perishable products have a higher unit price, a lower unit cost, lower quantity discounts, and a lower discount threshold. A consideration of the actual situation leads to explicit ordering rules being proposed in this paper. These enable retailers to make ordering decisions quickly and effectively without information on future demand and thus to meet market demand with the optimal order quantity and maximize their profits. This paper thus provides guidance for perishable goods inventory management. © 2021. Journal of Industrial Engineering and Engineering Management. All rights reserved.     

易逝品降价时点设定问题的Cournot博弈模型

本文基于Gallego和van Ryzin的两级价格策略,构建了两种竞争性易逝品降价时点设定问题的Cournot博弈模型,应用图解法求得Cournot均衡点,得出在竞争环境下先动企业会推迟降价时点而后动企业会提前降价的结论,通过实例分析验证了这一结论,同时指出了转移概率对均衡结果的影响.研究结果可为竞争环境下易逝性产品降价策略的制定提供决策支持.     

供应商管理库存环境下的分销网络连续近似模型

本文在现有连续近似(CA)模型的基础上,从供应商管理库存(VMI)角度出发,引入了PowerofTwo(POT)周期配送策略,构建了VMI环境下分销网络设计的CA模型。该模型的目标函数不仅包括了分销中心的建设 运营费用、运输费用,而且引进了实际中关注的存储费和订货费;该模型在确定分销网络结构的同时,也确定了各分销中心的库存策略和对其客户的配送策略。文中给出了模型的解法,利用此解法对算例进行求解,并分析了求解的结果,得出各参数变化对解的影响。     

在弹性需求和物品易变质条件下数量折扣定价模型

研究在弹性需求和易变质物品条件下,当供应商和零售商独自决策时,供应商如何确定最优数量折扣问题。基于Stackelberg博奕建立了数学模型,证明了零售商的出售价格随供应商出售价格的降低而降低,从而需求量增大。当供应商给予价格折扣时,零售商的利润是增加的。在此基础上,给出了最优数量折扣的计算方法。对供应商和零售商单独决策时,供应商利用数量折扣对供应链进行协调产生的系统利润与供应商和零售商联合决策时的系统利润做了数值分析和比较。结果表明,供应商采用数量折扣的方法使供应链协调是有效的;价格折扣随价格敏感系数的增大而增大,随变质率的增大而减小。     

Optimization Model and Algorithm for Crew Management During Airline Irregular Operations

Airline irregular operations have long been a realm where human experience and judgement are the most important tools to utilize. Crew management during irregular operations is usually the bottleneck of the whole system-recovering process due to complicated crew schedules and restrictive crew legalities as well as the size and scope of the hub-and-spoke networks adopted by major carriers. A system-wide multi-commodity integer network flow model and a heuristic search algorithm for the above purpose are presented and discussed in this paper. The computational experiences show that the algorithm is efficient enough to solve problems of realistic size and also has the flexibility to accommodate practical business requirements.     

基于神经网络的备件库存风险级别预测

本文提出了一种基于神经网络的备件库存风险级别分类方法,在对备件的供货来源、重要性、易损程度、标准化程度、供货周期等指标进行模糊评价的基础上,建立了多层前向神经网络模型,利用BP训练算法,确定神经网络模型的连接权系数。将某测井服务公司100种备件的历史数据作为样本,进行了BP训练仿真,并利用模型预测了该公司60种备件的库存风险级别,预测结果与实际结果的符合率为84%。     

Lead Time Management through Expediting in a Continuous Review Inventory System

We consider a continuous review inventory system where delivery lead times can be managed by expediting in‐transit orders shipped from the supplier. First, we propose an ordering/expediting policy and derive expressions for evaluating the operating characteristics of such systems. Second, using extensive numerical experiments, we quantify the benefits of such an expediting policy. Third, we investigate a number of managerial issues. Specifically, we analyze the impact of the number of expediting hubs and their locations along the shipment network on the performance of such systems and offer insights into the design of the shipment network. We show (i) a single expediting hub that is optimally located in a shipment network can capture the majority of cost savings achieved by a multi‐hub system, especially when expediting cost is not low or demand variability is not high; (ii) when expediting time is proportional to the time to destination, for small‐enough or large‐enough demand variations, a single expediting hub located in the middle of the shipment network can capture the majority of cost savings of an optimally located hub; and (iii) in general, hubs close to the retailer significantly drive down costs, whereas hubs close to the supplier may not offer much cost savings.     

Inventory routing problems in a context of vendor-managed inventory system with consignment stock and transshipment

The inventory routing problem involves the integration and the coordination of two components of the logistics value chain: the inventory management and the vehicle routing decisions. In fact, the aim is to jointly decide on the distribution tour, from a distribution centre to a set of locations, and on the inventory policy for each location. Although many research investigations show great interest in policies such as transshipment or dynamic routings on the distribution system performances, these approaches are often criticised in practice as being too restrictive. In this article, we consider the inventory routing framework in a supplier integration context, i.e. a vendor-managed inventory with a consignment stock policy. Under such framework, we show that the transshipment brings more benefits than the classical context. In particular, we consider the case of static routings and we numerically show that transshipment permits to better optimise the distribution tours and to improve the global performance of the supply network.     

基于需求更新的民航收入管理动态存量控制模型

本文对民航收入管理存量控制研究中具有里程碑意义的EMSR(Expected Marginal Seat Revenue)模型进行了评述,分析其存在的缺陷,提出将销售过程中获取的最新销售信息与需求的历史先验分布相结合,运用二维正态分布下的贝叶斯模型对需求分布进行更新,并将综合考虑新的需求预测、No-Show和取消订票等因素得到的新的需求限制与座位总数C相比较,给出更为通用的、市场反应更为灵敏的民航收入管理动态存量控制模型。     

一种考虑原材料库存成本的变质物品EPQ模型

在库存管理研究中,单独实现产成品或原材料库存成本最小,往往无法获得整个库存系统的最优控制策略.本文提出了一种同时考虑产成品和原材料库存成本的变质物品EPQ集成模型.运用迭代寻优法得到模型最优解,得出计划期内最优原材料订购次数,原材料订购周期内的最优生产次数和最优服务水平,并对主要参数进行了灵敏度分析,为生产制造企业的库存管理提供了决策依据.     

Revenue Management of Reusable Resources with Advanced Reservations

We consider a revenue management problem wherein the seller is endowed with a single type resource with a finite capacity and the resource can be repeatedly used to serve customers. There are multiple classes of customers arriving according to a multi‐class Poisson process. Each customer, upon arrival, submits a service request that specifies his service start time and end time. Our model allows customer advanced reservation times and services times in each class to be arbitrarily distributed and correlated. Upon arrival of each customer, the seller must instantaneously decide whether to accept this customer's service request. A customer whose request is denied leaves the system. A customer whose request is accepted is allocated with a specific item of the resource at his service start time. The resource unit occupied by a customer becomes available to other customers after serving this customer. The seller aims to design an admission control policy that maximizes her expected long‐run average revenue. We propose a policy called the ε‐perturbation class selection policy (ε‐CSP), based on the optimal solution in the fluid setting wherein customers are infinitesimal and customer arrival processes are deterministic, under the restriction that the seller can utilize at most (1 − ε) of her capacity for any ε ∈ (0, 1). We prove that the ε‐CSP is near‐optimal. More precisely, we develop an upper bound of the performance loss of the ε‐CSP relative to the seller's optimal revenue, and show that it converges to zero with a square‐root convergence rate in the asymptotic regime wherein the arrival rates and the capacity grow up proportionally and the capacity buffer level ε decays to zero.     

大型医院血液库存系统订货点决策的仿真研究

血液是一种典型的易变质物品。本文以国内某大型医院的血液库存系统为对象,依照其运作流程,基于该系统运行的历史数据建立了一个离散事件系统仿真模型对这一系统进行研究,得到了该系统的最优订货点。本文的研究有助于血液库存管理措施的制定与改进,对其它易变质物品库存系统的管理也有一定的借鉴作用。     

不确定需求环境下多级库存系统优化与协调模型研究

论文首先将模糊随机变量扩展为混合随机变量,并用来描述不确定需求.然后对多级单周期库存系统在模糊随机和混合随机需求环境下的优化与协调问题作了研究,提出了通过集成库存管理使库存系统整体收益期望值最大化的优化模型和协调合作伙伴利益的数量折扣契约模型,并根据遗传算法理论和计算机模拟技术设计了求解模型的智能算法.最后通过实例对模型进行了仿真分析.