基于Extendsim的多级库存系统仿真

针对多级库存难以控制的问题,利用Extendsim软件对需求和提前期均随机的多级库存系统进行建模和仿真,以供应链的利润最大化为目标,结合该软件的Optimizer模块,对多级库存各节点的订货点、订货量进行遗传算法计算。从而有效地解决了多级库存难以管理的问题。     

Common inventory modeling assumptions that fall short: Arborescent networks, Poisson demand, and single-echelon approximations

Traditional multi-echelon inventory theory focuses on arborescent supply chains that use a central warehouse which replenishes remote warehouses. The remote warehouses serve customers in their respective regions. Common assumptions in the academic literature include use of the Poisson demand process and instantaneous unit-by-unit replenishment. In the practitioner literature, single-echelon approximations are advised for setting safety stock to deal with lead time, demand, and supply variations in these settings. Using data from a U.S. supplier of home improvement products, we find that neither the assumptions from the academic literature nor the approximations from the practitioner literature necessarily work well in practice.In a variation of the strictly arborescent supply chain, the central warehouse at our real company not only replenishes other warehouses but also meets demand from customers in the region near the central warehouse. In this paper, we study this dual-role central warehouse structure, which we believe is common in practice. Using high and low volume product demand data from this company, we use Monte Carlo simulations to study the impact of (1) the use of a dual-role centralized warehouse, (2) common demand assumptions made in multi-echelon research, and (3) single-echelon approximations for managing a multi-echelon supply chain. We explore each of these under both centralized and decentralized control logic. We find that the common assumptions of theoretical models impede their usefulness and that heuristics that ignore the actual supply chain structure fail to account for additional opportunities to utilize safety stock more effectively. Researchers should be aware of the gap between standard assumptions in traditional literature and actual practice, and critically evaluate their assumptions to find a reasonable balance between tractability and relevance.     

基于商品性质的多级分销模式选择及网络库存定位模型的研究

本文以典型的多级分销网络为研究对象,从商品的需求量、需求波动及商品价值等特性对分销网络的服务能力和成本水平的影响出发,对如何根据不同商品特性选择科学的分销模式及如何在多级网络中进行合理的库存定位问题进行了研究,并基于此提出了一个可行的定性分析模型,借助此模型,可帮助企业在设计分销网络和现有网络的优化过程中,根据所经营商品的不同特性进行合理的网络设计和运营,大大提高网络运营效率。     

Capacitated heuristic program for multiactivity, multi-facility system designs

Multi-activity, multi-facility design systems occur in a wide variety of industrial and service applications: e.g.: regional health care centers with configurations of activities at each center, and multi-echelon inventory distribution systems. The problem in designing such systems is to determine from a candidate set of possible locations what facility locations should be opened and what types of activities should be carried out at each of the open facilities. This article presents an efficient heuristic procedure for such a problem.The heuristic procedure described can be used to solve both the uncapacitated and the capacitated problems where capacity is expressed in terms of the number of activities which can be supported by the facilities.Extensive computational experience with the heuristic is presented. The performance of the heuristic is compared against optimal solutions and the results look very encouraging.     

生鲜食品的二级补货系统优化研究

针对连锁超市的配送网络。建立一个两级的库存系统模型，其中包括一个中心仓库和若干门店，每个门店面对的是随机的相互独立的需求，该需求服从正态分布。该库存系统是采用基于连续检测的再订货点方法管理。本文针对超市的生鲜食品补货的特征，增加对于过剩成本的考虑，采用分散库存控制权的方法，按照从门店到配送中心的思路。优化总库存成本，从而得出最佳的订购批量，同时也得出最佳的产品供给水平、再订货点等。     

基于最小费用最大流的集群式供应链库存优化

首先介绍了集群式供应链库存管理存在的问题,据此设计出系统优化的网络结构,引入最小费用最大流的方法并进行了实际的赋值计算,然后提出了集群式供应链库存的优化模型,结尾对建立在集群式供应链基础上的库存管理的实际意义进行了评价。     

可修备件横向供应多级库存模型研究

系统费用最小,可用度最大一直是库存模型研究的核心问题。针对我军可修备件多级装备保障系统现实背景,在研究可修备件多级库存物理流程的基础上,以系统期望短缺数估计值最小为约束,优化可修备件库存配置,建立了可修备件横向供应多级库存模型。并且运用库存优化算法,对模型进行了仿真实验。     

基于中心化存储和横向转运的选址——库存联合决策模型

针对单一产品、单周期、单一供应商、多配送中心、供应商和需求点位置已知的情况,假定（Q,R）库存检查策略、给定备选地址、无容量约束、各需求点的需求完全独立且均服从正态分布、配送中心级和需求级同时采取中心化安全库存策略、允许配送中心间及同一配送线路内的需求点间通过横向转运对缺货进行瞬间补充、不计缺货成本和转运时间,构建了二层规划模型。上层规划解决选址及需求点的分配问题;下层规划确定配送中心级和需求级的中心化存储方案。发现如果考虑库存持有成本、转运成本等因素的影响,安全库存的集中存放策略并不是聚集在某一个地方,而是在多个地方进行有限、适度的集聚,其成本更低。     

两级库存供应系统的需求随机型EOQ模型研究

结合军事器材总部-地区两级库存-运输的供应模式以及消耗速率的随机性,基于EOQ模型分析了各级库存的费用结构,采用实例验证的方法探讨了随机需求型两级库存供应系统的订货配送周期优化途径。     

基于库存管理ABC分析的运输与库存整合

运输与库存整合优化是供应链管理中的重要研究课题。论文研究由一个供应商和多个零售商组成的分销系统,在库存实行ABC管理的基础上,零售商采取周期订货策略。在每个零售商处的客户需求是随机的且服从一定的概率分布。我们的目标是在满足给定的客户服务水平的条件下。寻求该供应链运输和库存总费用的最小化。通过对库存水平及它们与客户随机需求变量之间的关系分析,得到最优的订货策略。     

多层次CPFR库存控制与优化研究课题及方法论

在对国内外同类课题研究现状及存在问题进行分析的基础上,提出了协同商务下多层次CPFR库存控制与优化研究课题及方法论。借鉴国际上最新的理论和优化模型,从定性和定量分析相结合的角度,提出一个科学合理的协同商务下多层次CPFR库存控制方法和优化模型。     

碳约束下需求离散随机的库存控制问题研究

文章站在零售商的角度,通过分析需求离散随机库存控制过程中的碳排放因素,设计出基于碳排放成本的随机库存控制模型,对低碳供应链中库存控制的要素进行了分析,并通过算例来说明该模型。模型有利于进一步提升供应链的核心竞争力,推动低碳供应链管理的深入研究。     

慢速流动备件库存管理研究综述

主要研究了国内外慢速流动备件库存管理的相关文献,介绍了备件库存及备件分类的相关知识,主要针对慢速流动备件的需求预测方法及其库存模型算法的研究进展进行了综述。针对需求预测,重点分析了当前所使用的几种预测方法;在库存模型方面研究了慢速流动备件库存管理模式的发展历程,从单级库存模式到多级库存模式,再到联合库存模式和协同库存模式。最后指出了研究存在的不足,明确了慢速流动备件库存管理在未来研究中应加以重视的几个研究方向,为进一步的研究奠定了基础。     

零售企业库存管理决策支持系统研究

本文以零售企业为背景．对其库存管理决策支持系统进行总体分析，并对决策支持系统中模型库系统作重点分析，阐述了库存决策支持系统的决策模型应包含单一产品库存决策模型、多品种产品库存决策模型和多级联合库存决策模型，说明了库存管理决策支持系统的具体实现。     

Het bepalen van bestelniveaus wanneer afname en levertijd gamma-verdeeld resp. normaal-verdeeld zijn

In studies of inventory control problems in industry it is often supposed that there is a variable demand and a constant delivery time.
This assumption is rarely tenable. In this paper rules are developed for determining the reorder level when the demand has a gamma distribution and the delivery time is normally distributed.     

Computer simulation of lot-sizing algorithms in three-stage multi-echelon inventory systems

This paper evaluates conventional lot-sizing rules in a multi-echelon coalescence MRP system. A part explosion diagram of three levels and twenty-one nodes is simulated using FORTRAN IV Level G. Nine separate lot-sizing methods were evaluated in this analysis. These methods included Lot for Lot, Economic Order Quantity, Periodic Order Quantity, Least Total Cost, Least Unit Cost, Part-Period Balancing, the Silver-Meal Algorithm, and the Wagner-Whitin Algorithm. A hybrid rule using both the Economic Order and the Economic Production Quantity rules was also evaluated.The performance of each lot sizing rule was simulated over nine different sets of market requirements patterns over a twelve month period. The types of demand variation included a constant rate, three different patterns of normally distributed demand, a random pattern, and two cyclic patterns. A hybrid pattern was used which equally weighted components of constant, random, normal, and cyclic demand. Finally, the ninth pattern consisted of actual data obtained from a job shop manufacturing facility.Within the part explosion diagram, ratios of setup cost to carrying cost, “goes into” quantities, and lead times were assigned for each node. Assigned values were selected from uniform distributions with prespecified ranges.A computer model was developed to perform the simulation. It consisted of an executive program, a routine for data generation, and separate routines to exercise each of the different lot-sizing rules. The simulations were conducted under three operational rules. The first rule allowed for the establishment of initial inventories just large enough to “cover” those gross requirements that occurred prior to the time the first order arrived. Carrying costs for this stock were included in the computation of total costs per node. The second rule provided for the delay of application of each lot sizing rule. This avoided receiving an order in a period of zero demand. The third rule addressed the computation of costs. The total cost was computed on the basis of average inventory level and the number of required setups.The analysis required the completion of 1701 separate simulation runs (9 rules X 9 demand patterns X 21 nodes). The performance of each rule was evaluated on the basis of total annual inventory cost. The Periodic Order Quantity (POQ) rule performed best in six of the nine demand patterns analyzed. In two of the remaining three cases, it ranked second on the basis of minimizing costs.The Least Unit Cost (LUC), Least Total Cost (LTC), and Pan-Period Balancing (PPB) algorithms demonstrated identical performance in four of the demand patterns analyzed. Generally, they ranked in the upper half of the rules evaluated. The Economic Order Quantity (EOQ) and the Economic Order/Production Quantity hybrid rules performed only moderately well. On the basis of cost, the consistent worst performers were the Wagner-Whitin (WW), Silver-Meal (SM), and Lot-for-Lot (LFL) rules.It was found that gross requirements tend to occur sporadically in different levels of the system. Order policies of parent nodes often cause the policies in higher level nodes to resemble the lot-for-lot order philosophy, regardless of the rule being used. Because of this phenomenon, those rules that generate fewer orders over the planning horizon for parent nodes often tend to perform better on the basis of total inventory cost.     

Valuing lead time

When do short lead times warrant a cost premium? Decision makers generally agree that short lead times enhance competitiveness, but have struggled to quantify their benefits. Blackburn (2012) argued that the marginal value of time is low when demand is predictable and salvage values are high. de Treville et al. (2014) used real-options theory to quantify the relationship between mismatch cost and demand volatility, demonstrating that the marginal value of time increases with demand volatility, and with the volatility of demand volatility. We use the de Treville et al. model to explore the marginal value of time in three industrial supply chains facing relatively low demand volatility, extending the model to incorporate factors such as tender-loss risk, demand clustering in an order-up-to model, and use of a target fill rate that exceeded the newsvendor profit-maximizing order quantity. Each of these factors substantially increases the marginal value of time. In all of the companies under study, managers had underestimated the mismatch costs arising from lead time, so had underinvested in cutting lead times.     

模糊库存容量约束的多产品库存策略研究

基于模糊数的可信性理论.引入利润率、需求率、库存成本率.根据它们对库存总成本的影响赋予不同的权重得到库存评价指标.据此对产品库存规模进行分类。假定多产品的库存容量为LR型模糊数.根据库存评价指标定义了与单位存储空间相关的额外库存成本.从而确定了最优订货策略。最后通过算例探讨了此模型的有效性和稳定性。     

Application and research on the complex dynamics behaviour under price-discount promotion policy in inventory control system

In this paper, we study the inventory control system of the apparel industry supply chain in which the products are fast-fashion and mass-consuming goods. Retailers tend to promote by price-discount policy to regulate overstock situation caused by fluctuated demand, frequent product upgrading, etc. Here, we discuss the impact of fluctuated demand triggered by price-discount promotion policy on the comprehensive inventory control system in which the final inventory and supply line stock are considered and weighted differently according to corresponding practical operation situations. In this model, stock returns are permissible. Then we calculated the Lyapunov stable region of the decision parameters based on the state space model. These conclusions present the managers a big picture to inspect the inventory control system and serve as reliable references to make correct decisions.     

A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study

In uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.