Closed-loop supply chain inventory management with recovery information of reusable containers

This paper considers a closed-loop supply chain consisting of one-manufacturer and one-retailer. This supply chain provides single-kind products with reusable containers. The main purpose of this study is to explore and evaluate the value of recovery information captured by embedded sensors in the environment of internet of things. The recovery information of containers dynamically monitors recovery status and provides a reliable estimation of return quantity. The value of information is measured by the cost saving performances with full, partial or no recovery information. When the full or partial recovery information is available, the decisions are made based on the known quantities of the usable or total return flow. When no recovery information is available, the decisions are made based on the stationary distribution of the return flow. A periodic inventory model is built with uncertainties of forward and reverse flows. Then, a myopic order policy is proposed for the different levels of information utilization. Through the optimality analysis, we introduce a farsighted inventory control policy. Using the general result of Markov decision processes, the performance of heuristic policies is displayed. The farsighted policy performs better than the myopic policy. In addition, the farsighted policy helps to lessen the convex impact of utilization rate on the expected cost. Afterwards, we extend the model with the selective disposal behavior. A simulation study is used to depict sensitivity and robustness of the farsighted policy. Finally, we extend the simulation experiment with uniformly distributed in-use time for a more general applicability.     

Shelf Life Extending Packaging,Inventory Control and Grocery Retailing

Retailers of perishable goods are often faced with the choice between more expensive packaging that can extend shelf life of their products and less expensive packaging that cannot. Different choices will lead to different sales, costs, and waste, and different choices require different inventory control policies. In this paper, we study the coordination of inventory and packaging decisions in a retailing environment. Items in an active package have a longer lifetime than those in a regular package and cost more. Customers always pick items with a longer lifetime first. When items with a longer lifetime are out of stock, a portion of customers are willing to buy less fresh items as substitutes. Our study shows the optimality of a “separation” policy when the substitution rate is high. In the separation policy, as the initial inventory level increases, the optimal policy changes from using active packaging only, then to using regular packaging only, and finally to ordering nothing. These results are very specific to the way perishable inventories are depleted in retailing. Our numerical study shows that although it may not be optimal in terms of cost, the adoption of active packaging can consistently achieve substantial waste reduction.     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

A coordination mechanism with fair cost allocation for divergent multi-echelon inventory systems

In this paper we study the coordination of inventory control in divergent multi-echelon inventory systems under periodic review and decentralized control. Under decentralized control the installations decide upon replenishment policies that minimize their individual inventory costs. In general these policies do not coincide with the optimal policies of the system under centralized control. Hence, the total cost under decentralized control is larger than under centralized control. We present a simple coordination mechanism that removes this cost inefficiency. The upstream installations increases its base stock level while the downstream installations compensate their supplier for increased costs and provide it with additional side payments. We show that this mechanism coordinates the system; the global optimal policy of the system is the unique Nash equilibrium of the corresponding strategic game. Furthermore, the mechanism results in a fair allocation of the costs; all installations enjoy cost savings.     

The cost of using stationary inventory policies when demand is non-stationary

Non-stationary stochastic demands are very common in industrial settings with seasonal patterns, trends, business cycles, and limited-life items. In such cases, the optimal inventory control policies are also non-stationary. However, due to high computational complexity, non-stationary inventory policies are not usually preferred in real-life applications. In this paper, we investigate the cost of using a stationary policy as an approximation to the optimal non-stationary one. Our numerical study points to two important results: (i) Using stationary policies can be very expensive depending on the magnitude of demand variability. (ii) Stationary policies may be efficient approximations to optimal non-stationary policies when demand information contains high uncertainty, setup costs are high and penalty costs are low.     

航空公司机上周转品多基地库存优化模型

在民航业日益激烈的市场竞争环境下，航空公司对成本控制的精细化管理显得尤为重要。本文基于航空公司的实际背景，针对为旅客提供的机上周转品，建立了一个多基地库存系统的库存优化模型。该模型以系统的总库存成本最小为目标，在考虑需求、回收、调运、安全库存等因素下，决策一个订货周期内的订货及调运方案。基于最优解的分析，在成本参数满足一定条件下，可以得到系统最优的期初订货量；在此基础上，可以将该模型转化为一个网络流模型，参考最小费用最大流算法，设计了一个多项式求解算法来求解该网络流模型，进而得到各基地之间的最优调运量，并证明该算法的最优性；此外，根据期初订货量可行解的范围，设计了一个求解原库存模型的启发式算法。通过实例分析，验证了该模型与两种算法的有效性。     

部分短缺量拖后下基于顾客敏感度的补货策略适用范围研究

顾客对缺货数量和等待时间的敏感度影响着部分短缺量拖后率。基于此,根据顾客对缺货数量和等待时间的敏感度以及成本结构,对部分短缺量拖后下不同补货策略的适用范围进行了研究,得出以下结论:(s,S)连续性检查策略适用于单位缺货和丢单成本较高的库存系统;(t,S)周期性检查策略和(t,s,S)混合策略适用于单位缺货和丢单成本较低的库存系统;在单位缺货和丢单成本较低的库存系统中,当顾客对缺货数量较敏感时,(t,s,S)混合策略的运作成本更低,否则(t,S)周期性检查策略更适用,而顾客对等待时间的敏感度对补货策略适用范围的影响不明显。     

Generalization of multi-item,multi-retailer distribution system

The procedure developed by Muckstadt and Roundy is generalized. In a multi-echelon inventory system with a single warehouse and multi-retailers, some di erent items with constant demand rates are distributed. Considering set-up and holding costs as well as the cost of joint shipment of items from the warehouse to any retailer, the objective is to minimize the total costs of the system. In the proposed algorithm the assumption of nested policies is relaxed. The procedure introduced works for both nested and non-nested policies.     

Optimal Inventory Control with Retail Pre‐Packs

A pre‐pack is a collection of items used in retail distribution. By grouping multiple units of one or more stock keeping units (SKU), distribution and handling costs can be reduced; however, ordering flexibility at the retail outlet is limited. This paper studies an inventory system at a retail level where both pre‐packs and individual items (at additional handling cost) can be ordered. For a single‐SKU, single‐period problem, we show that the optimal policy is to order into a “band” with as few individual units as possible. For the multi‐period problem with modular demand, the band policy is still optimal, and the steady‐state distribution of the target inventory position possesses a semi‐uniform structure, which greatly facilitates the computation of optimal policies and approximations under general demand. For the multi‐SKU case, the optimal policy has a generalized band structure. Our numerical results show that pre‐pack use is beneficial when facing stable and complementary demands, and substantial handling savings at the distribution center. The cost premium of using simple policies, such as strict base‐stock and batch‐ordering (pre‐packs only), can be substantial for medium parameter ranges.     

The benefit of receding horizon control: Near-optimal policies for stochastic inventory control

In this paper we address the single-item, single-stocking point, non-stationary stochastic lot-sizing problem under backorder costs. It is well known that the (s, S) policy provides the optimal control for such inventory systems. However the computational difficulties and the nervousness inherent in (s, S) paved the way for the development of various near-optimal inventory control policies. We provide a systematic comparison of these policies and present their expected cost performances. We further show that when these policies are used in a receding horizon framework the cost performances improve considerably and differences among policies become insignificant.     

按单装配系统中的协同补货问题研究

多库存品的库存系统中各库存品具有需求相关性在现实中很常见,但许多关于库存的文献并没有考虑这一点.针对按单装配系统的特性,本文研究了不同部件需求具有相关性的情况下的库存控制问题,分析了不同部件独立补货和不同部件协同补货两种补货策略.数值分析表明,按单装配系统中部件协同补货策略比部件独立补货策略具有更低的总成本.     

Rolling horizon master production schedule performance: A policy analysis

In this paper, a simulation experiment has been developed to examine the combined influence of the design, inventory and environmental factors on the cost performance of a rolling horizon master production schedule. Specifically, a 2 5 factorial design was used to examine the effects associated with three rolling schedule design policies, one inventory policy and one environmental condition of forecast error on MPS cost performance. The study was based on actual data from a paint company. Results suggest that the choice of appropriate lot-size and inventory policies have a significant influence on MPS costs and that there are indeed important interactions between these policies and other design factors of a rolling schedule.     

交叉销售下基于支持向量聚类的数据驱动多产品库存鲁棒优化模型

针对单周期环境下考虑交叉销售的多产品库存决策问题,在市场需求不确定条件下,建立了带有预算约束的交叉销售多产品库存鲁棒优化模型。针对不确定市场需求,采用支持向量聚类(SVC)方法构建了满足一定置信水平的数据驱动不确定集。进一步,运用拉格朗日对偶方法将所建模型等价转化为易于求解的线性规划问题。最后,通过数值计算对比分析了SVC不确定集下及传统不确定集下的零售商利润绩效,并评估了SVC数据驱动鲁棒优化方法导致的绩效损失,进而分析了预算及交叉销售系数对零售商利润绩效的影响。结果表明,SVC数据驱动鲁棒优化方法具有良好的鲁棒性,能够有效抑制需求不确定性对从事多产品销售的零售商利润绩效的影响。特别地,需求分布信息的缺失虽然会给零售商带来一定的绩效损失,但损失值很小,表明文中提出的基于SVC的数据驱动鲁棒优化方法可以为管理者在需求不确定性环境下制定库存策略提供有效决策借鉴。     

持有成本和变质率时变的非立即变质品库存策略

本文主要研究非立即变质品的库存策略设计问题。以平均利润最大化为决策目标,构建了一个一般化的库存模型,假设需求受即时库存水平影响且在保鲜期和变质期内库存水平对需求的影响系数不同,持有成本和变质期内的变质率均随时间发生变化,系统允许缺货且短缺量部分延迟订购。进一步证明了当参数满足一定条件时,模型存在唯一的最优解。最后,采用牛顿法给出一些具体的数值算例,并对模型中主要参数的灵敏度进行了分析。结果显示:延长物品的保鲜期将有助于增加系统的平均利润;增加变质期内的需求比增加保鲜期内的需求更有利于增加系统的平均利润;对于变质率高的产品来说,其变质处理成本对最优策略和平均利润的影响不容忽视。此外,本文所构建的模型具有一般性,因此,其应用范围更加广泛。     

Contracting Under Vendor Managed Inventory Systems Using Holding Cost Subsidies

Vendor managed inventory systems are becoming increasingly popular. An important issue in implementing a vendor managed inventory scheme is the contracting terms that dictate the ownership of the inventory and the responsibility of inventory replenishment decisions. Thus the performance of a vendor managed system crucially depends on these terms and on how inventory‐related costs are shared in a supply chain. We consider a system where a manufacturer supplies a single product to a retailer who faces random demand in a competitive market. The retailer incurs a fixed cost per order, inventory holding cost, and a penalty cost for a stockout (unsatisfied demand is back‐ordered). Further, the manufacturer incurs a penalty cost when there is a stockout at the retailer and a fixed replenishment cost. We assume that the players are rational and act noncooperatively. We compare the performance of retailer managed inventory systems, where the retailer places orders and makes replenishment decisions, with vendor managed inventory systems, wherein the vendor or manufacturer makes inventory and replenishment decisions. Specifically, in the vendor managed inventory system, we propose and evaluate holding cost subsidy‐type contracts on inventories offered by the retailer to improve system performance. We evaluate this contract in the context of three widely used inventory systems—deterministic economic order quantity, continuous review (Q, r) policies, and periodic review policies—and show when such contracts may improve channel performance.     

End‐of‐Life Inventory Problem with Phaseout Returns

We consider the service parts end‐of‐life inventory problem of a capital goods manufacturer in the final phase of its life cycle. The final phase starts as soon as the production of parts terminates and continues until the last service contract expires. Final order quantities are considered a popular tactic to sustain service fulfillment obligations and to mitigate the effect of obsolescence. In addition to the final order quantity, other sources to obtain serviceable parts are repairing returned defective items and retrieving parts from phaseout returns. Phaseout returns happen when a customer replaces an old system platform with a next‐generation one and returns the old product to the original equipment manufacturer (OEM). These returns can well serve the demand for service parts of other customers still using the old generation of the product. In this study, we study the decision‐making complications as well as cost‐saving opportunities stemming from phaseout occurrence. We use a finite‐horizon Markov decision process to characterize the structure of the optimal inventory control policy. We show that the optimal policy consists of a time‐varying threshold level for item repair. Furthermore, we study the value of phaseout information by extending the results to cases with an uncertain phaseout quantity or an uncertain schedule. Numerical analysis sheds light on the advantages of the optimal policy compared to some heuristic policies.     

The Effect of Distribution Processes on Replenishment Lead Time and Inventory

We investigate the interrelationship of distribution center picking policies and supply chain inventory performance. In particular, we show how the picking sequence in the upstream supply chain link affects the inventory levels of items being replenished to a downstream link for a common “ship‐when‐full” trailer dispatching policy. Perturbing the picking sequence affects items’ inventory levels asymmetrically which causes the aggregate system inventory to vary. We separate the items in replenishment into those units in transit and those awaiting shipment from the upstream distribution step: we call the latter the residual replenishment. We show that the process governing aggregate residual replenishment is Markov and has a stationary distribution that is discrete uniform. Computing the item‐level residual distribution is intractable and so we develop analytical models from which we derive hypotheses for the effectiveness of stable vs. random picking sequences, how item residual replenishment varies with stable picking sequences, and how the aggregate inventory level changes with picking sequence. These suggest a heuristic sequencing algorithm for minimizing inventory, which performs well in simulation tests over a large testbed of parameter sets.     

A new EPQ model: integrating lower pricing,rework and reject situations

The economic production quantity (EPQ) is a well-known and commonly used inventory control technique. It has been used for well over 50 years to optimize lot sizes in transportation/production. The standard results are easy to apply but are based on a number of assumptions. A common assumption in the EPQ model is that all units produced are of perfect quality, this will underestimate the actual required quantity. Many researchers have studied the effects after relaxing this assumption on the EPQ model. The previous studies had considered that imperfect quality and defective items are either to be reworked instantaneously and kept in stock or rejected at a cost. The objective of this paper is to provide a framework to integrate lower pricing, rework and reject situations into a single EPQ model. A 100% inspection is performed in order to identify the amount of good quality items, imperfect quality items and defective items in each lot. This model assumes that items of imperfect quality, not necessarily defective, could be used in another production situation or sold to a particular purchaser at a lower price. The electronic and clothing industries give good examples for such situations. A mathematical model is developed and a numerical example is presented to illustrate the solution procedures. It is found that the time factor of when to sell the imperfect items is critical, as this decision will affect the inventory cost and the batch quantities.     

An echelon inventory-based single-stage cost function for a two-station tandem system

We propose an alternative cost-accounting function for inventory control problems on a make-to-stock setting. Our proposal is based on observing that the traditional holding and backlog parameters introduce some odd short term distortions on the inventory state space. Our single-stage cost function accounts for echelon inventories and possesses a pair of cost parameters for each echelon inventory variable, depending on whether it is positive or negative. With the modified cost-accounting function, we study a twostation tandem system producing a single product, and investigate how it compares with the performances obtained with the usual single-stage cost function. The results available so far show that the optimal policies approach a multi-echelon base stock structure for each machine. Also, the service levels achieved are better under the modified function without increasing the levels of finished goods inventory.     

Analysis and Management of Periodic Review,Order‐Up‐To Level Inventory Systems with Order Crossover

In this article, we investigate the (R, S) periodic review, order‐up‐to level inventory control system with stochastic demand and variable leadtimes. Variable leadtimes can lead to order crossover, in which some orders arrive out of sequence. Most theoretical studies of order‐up‐to inventory systems under variable leadtimes assume that crossovers do not occur and, in so doing, overestimate the standard deviation of the realized leadtime distribution and prescribe policies that can inflate inventory costs. We develop a new analytic model of the expected costs associated with this system, making use of a novel approximation of the realized (reduced) leadtime standard deviation resulting from order crossovers. Extensive experimentation through simulation shows that our model closely approximates the true expected cost and can be used to find values of R and S that provide an expected cost close to the minimum cost. Taking account of, as opposed to ignoring, crossovers leads, on average, to substantial improvements in accuracy and significant cost reductions. Our results are particularly useful for managers seeking to reduce inventory costs in supply chains with variable leadtimes.