A note on the deterministic EPQ with partial backordering

In a recent paper published in Omega [Pentico DW, Drake MJ, Toews C. The deterministic EPQ with partial backordering: a new approach. Omega 2009; 37(3):624–36], the authors proposed an EPQ model with partial backordering. In the EPQ model, a critical value of backordering rate was developed, below which the optimal production policy is either to lose all sales or meet all demand, above which the optimal policy is to allow stockouts with partial backordering and meet fractional demand. However, there exists a flaw in the critical value of backordering rate, which will be amended in this note.     

The EPQ with partial backordering and phase-dependent backordering rate

In previous work we established a closed-form optimal stocking strategy for an EPQ model with partial backordering at a constant rate β. Here we extend this work to allow for the possibility that the percentage of demand backordered will increase when production starts again. We show how our previous model can be adapted to find the optimal decision variable values for this new assumption and develop the condition that the initial value of β must meet for partial backordering to be optimal.     

The deterministic EPQ with partial backordering: A new approach

Several authors have developed models for the EOQ when only a percentage of stockouts will be backordered. Most of these models are complicated, with equations unlike those for the EOQ with full backordering. In this paper we extend work by Pentico and Drake [The deterministic EOQ with partial backordering: a new approach. European Journal of Operational Research 2008; in press] that developed equations for the EOQ with partial backordering that are more like those for the EOQ with full backordering to develop a comparable model for the EPQ with partial backordering.     

On the optimality of the optimal policies for the deterministic EPQ with partial backordering

In a note published in Omega [Zhang RQ. A note on the deterministic EPQ with partial backordering. Omega 2009;37(5):1036–8], the amended decision procedure for the Pentico et al.'s EPQ with partial backordering (EPQ-PBO) is proposed, by developing another critical value of the backordering rate. However, there is a case when a decision made with this amended procedure is not optimal, which will be shown in this paper. A new decision procedure will be proposed based on the derived necessary and sufficient conditions for considering the policy of losing all sales or the policy to meet all demand as possible optimal decisions. The proposed decision procedure is adapted for one of the extensions of the EPQ-PBO.     

A supplement to the EPQ with partial backordering and phase-dependent backordering rate

In this note, we extend Pentico et al. (2011)'s [1] EPQ model where the authors considered two different backordering rates during shortage period; they assumed the backordering rate increases only from the start of production phase. We propose two models that allow the backordering rate to increase anytime during the production phase, and thus provide more flexibility for decision-makers to vary the backordering rate.     

A partial backordering approach to inventory control

We study the effects of using a partial backordering approach to controlling inventories under deterministic and stochastic demands, respectively. With a deterministic demand, our model is built with the objective of minimizing the total cost of ordering, holding, backordering and lost sales. The conditions for the partial backordering policy to be feasible are identified and a pair-wise comparison among the no-backordering, complete backordering, and partial backordering doctrines is conducted. In the stochastic case, we focus on a make-to-stock system with a Poisson demand and exponential production time, which allows us to establish a queuing model to examine the cost-effectiveness of using partial backorders. The conditions under which the partial backordering policy outperforms the complete backordering policy are identified.     

Optimal inventory model for items with imperfect quality and shortage backordering

In this study, we develop an optimal inventory model for items with imperfect quality and shortage backordering. Such implicit assumption is reasonable in view of the fact that poor-quality items do exist during production. They are usually picked up during the screening process and are withdrawn from stock instantaneously. It is assumed that all customers are willing to wait for new supply when there is a shortage. The analysis shows that our model is a generalization of the models in current literatures. An algorithm and numerical analysis developed show that our model always results in better performance.     

Analysis of an EOQ inventory model with partial backordering and non-linear unit holding cost

In this paper, an economic order quantity inventory model is analyzed, considering that the unit cumulative holding cost has two significant components: a fixed cost which represents the cost of accommodating the item in the warehouse and a variable cost given by a potential function of the length of time over which the item is held in stock. Shortages are allowed and, during the stockout period, only a fraction of demand is partially backordered. The backordering cost includes a fixed cost and a cost linearly dependent on the length of time for which backorder exists. A solution procedure is developed for determining the optimal inventory policy. Moreover, to illustrate the effects of some parameters on the optimal policy and the minimum total inventory cost, a numerical study is developed.     

Exact closed-form solutions for “optimal inventory model for items with imperfect quality and shortage backordering”

Wee et al. [Optimal inventory model for items with imperfect quality and shortage backordering. Omega 2007;35(1):7–11] recently contributed an optimal inventory model for items with imperfect quality and shortage backordering. This article revisits their study and applies the well-known renewal-reward theorem to obtain a new expected net profit per unit time. We derive the exact closed-form solutions to determine the optimal lot size, backordering quantity and maximum expected net profit per unit time, specifically without differential calculus. We also solve the same model algebraically from another direction, which has been mentioned, but the process has not been finished yet. The problem parameter effects upon the optimal solutions are examined analytically and numerically.     

An EOQ model with partial delayed payment and partial backordering

In many transactions concerning selling and buying, a specified delay of payment is offered or accepted by the seller. This can be regarded as a kind of discount and has potential consequences for the order size. These kinds of effects are not explicitly incorporated in the classical formulas for economic order quantities (EOQ). In this research we consider an EOQ problem under partial delayed payment. A fraction of the purchasing cost must be paid at the beginning of the period and the remaining amount can be paid later. Shortages are permitted and occur as a combination of backorders and lost sales. The aim of this paper is to determine the order and shortage quantities.     

Multi-product lot scheduling with backordering and shelf-life constraints

In this paper, we revisit the economic lot scheduling problem (ELSP), where a family of products is produced on a single machine, or facility, on a continual basis. Our focus is on the determination of a feasible production schedule, including the manufacturing batch size of each item. We assume that total backordering is permissible and that each of the products has a limited post-production shelf life. Several studies examining this problem have suggested a rotational common cycle approach, where each item is produced exactly once every cycle. To ensure schedule feasibility, we resort to the technique of reducing individual production rates and allow the flexibility of producing any item more than once in every cycle, in conjunction with appropriate timing adjustments. In order to solve this more generalized model, which is NP hard, we suggest a two-stage heuristic algorithm. A numerical example demonstrates our solution approach.     

提前期、构建成本和短缺量拖后率均可控的EOQ模型

传统库存模型通常将提前期和构建成本视为不可控制。事实上可以通过追加投资缩短提前期和降低构建成本。缺货期间,为减少订单丢失量和补偿顾客的损失,供应商会给予一定的价格折扣。现实库存系统中,容易得到需求的期望值和标准差,但较难得到其分布规律。基于此,考虑短缺量拖后率与价格折扣和缺货期间库存水平相关,提出了一种需求为任意分布且提前期和构建成本均可控的EOQ模型,证明了模型存在唯一最优解,给出了一种寻优算法。数值仿真分析表明,一般情况下,压缩提前期和降低构建成本能降低订购批量和安全库存,降低库存总成本;短缺量拖后系数和缺货概率对库存总成本影响较大,企业应尽量降低缺货概率,尤其在短缺量拖后系数较小时。     

风险质检行为下的模糊生产-库存决策

本文研究在风险质检行为下需求与生产率具有模糊属性的由一个制造商与一个零售商构成的生产-库存问题。在假设制造商生产的产品存在质量缺陷,零售商在质检过程中存在质检风险的基础上,分别建立了需求模糊下的生产-库存总成本模型,需求与生产率模糊下的总成本模型,运用符号距离法进行了逆模糊化处理,将模糊化的生产-库存总成本转化为确定性成本。证明了两类情形下的总成本均是关于最优订购量与最优缺货量的联合凸函数。数值分析结果表明：随着期望缺陷率的增加,最优订购量增加,最优缺货量减少,最优成本增加,且最优成本增加的速度越来越快。一类质检风险增大引起总成本增加,二类质检风险增大引起总成本减少。最优订购策略对质检一类风险敏感,对质检二类风险不敏感。     

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.     

考虑质量相关性需求与完全预防性维修的EPQ决策

在经典EPQ模型基础上，考虑质量相关性需求和周期性完全预防性维修策略，以单位时间利润最大化为目标，构建经济生产批量的决策模型。在企业单位时间利润模型的构建中，结合质量相关性需求以及设备退化这一实际问题，进行动态预防性维修成本、恢复成本、次品修复成本以及产品需求率的构建。考虑目标函数的复杂性，本文采用遗传算法对模型进行数值求解，并通过与不考虑质量相关性需求的EPQ决策的比较分析，验证其合理性。     

An adaptation of the EOQ formula for JIT quasi-pull system production

This research demonstrates for the first time that in a just-in-time (JIT) quasi-pull production environment, a model adapted from the Economic Order Quantity and made up of two specific formulas can be employed for calculating an optimised lot size. The assumptions of our model are: a low level of defects, and insignificant shortage backordering and failure of the equipment. JIT quasi-pull production is the state typically reached by many manufacturing companies because it is almost impossible to achieve zero set-ups and zero work-in-process (WIPs) even in the best Lean Production implementations. In this model, finished products are shipped at once in a JIT way. The two developed formulas are specialised: one for balanced stations/processes and the other for unbalanced stations; the formulas allow calculation of an optimised lot size. Several applications of the formulas have been tested and discussed through a case study. Findings demonstrated that the optimised quantity of a lot size has to be smaller than the customer’s daily demand for the product. Moreover, in case of unbalanced stations with long cycle times, the formula forces the company to reduce its set-up times and the cycle times. The new model will be of interest to practitioners who are seeking a way to balance the costs of set-ups and the cost of holding some WIPs and offers interesting avenues for new research.     

SETUP REDUCTION AND MACHINE AVAILABILITY

This paper is an extension of Billington, who used the framework of the economic production quantity (EPQ) to model setup cost reduction. In the present paper, we use the EPQ model as a starting point to investigate the nature of setup costs and the effect of setup time reduction on the increase in available capacity. Reducing setup is vital to a company's success because a lengthy changeover of machinery is expensive: it demands long production runs to justify its cost, and these, in turn, lead to excessive inventory and to a slow response to customer needs. As in Billington, setup reduction is modeled as a function of an annual amortized investment. The paper examines the behavior of the setup time, the inventory cost, the lot size, and the freeing up of machine time in the face of a capacity constraint. A solution algorithm is provided to find setup times that minimize the sum of setup and holding cost, subject to a constraint on machine availability. The analysis sheds light on the true nature of setup cost and on the opportunity cost of not reducing setups. In the constrained optimization, the Lagrangian multiplier gives an estimate of the marginal value of adding one time unit of machine capacity, or, alternatively, of reducing one unit of setup time.     

A note on an economic production quantity model and just-in-time production

In this paper an economic production quantity (EPQ) model in which the production rate is variable is studied. An analysis is presented of the impact of a variable production rate on the optimal production quantity and the total relevant cost. It is observed that this EPQ production and inventory system, in which the production rate is close to the demand rate, possesses many characteristics that are similar to a just-in-time (JIT) production system. It is shown that the normal prerequisites and benefits of JIT production can be identified from an analysis of such an EPQ system.     

Heuristics for Base‐Stock Levels in Multi‐Echelon Distribution Networks

We study inventory optimization for locally controlled, continuous‐review distribution systems with stochastic customer demands. Each node follows a base‐stock policy and a first‐come, first‐served allocation policy. We develop two heuristics, the recursive optimization (RO) heuristic and the decomposition‐aggregation (DA) heuristic, to approximate the optimal base‐stock levels of all the locations in the system. The RO heuristic applies a bottom‐up approach that sequentially solves single‐variable, convex problems for each location. The DA heuristic decomposes the distribution system into multiple serial systems, solves for the base‐stock levels of these systems using the newsvendor heuristic of Shang and Song (2003), and then aggregates the serial systems back into the distribution system using a procedure we call “backorder matching.” A key advantage of the DA heuristic is that it does not require any evaluation of the cost function (a computationally costly operation that requires numerical convolution). We show that, for both RO and DA, changing some of the parameters, such as leadtime, unit backordering cost, and demand rate, of a location has an impact only on its own local base‐stock level and its upstream locations’ local base‐stock levels. An extensive numerical study shows that both heuristics perform well, with the RO heuristic providing more accurate results and the DA heuristic consuming less computation time. We show that both RO and DA are asymptotically optimal along multiple dimensions for two‐echelon distribution systems. Finally, we show that, with minor changes, both RO and DA are applicable to the balanced allocation policy.     

Dual Sourcing Inventory Systems: On Optimal Policies and the Value of Costless Returns

We study dual sourcing inventory systems with backordering and with stationary, stochastic demands. The two supply sources differ in their unit prices and lead times. We focus on the option of making costless returns to the cheaper, longer leadtime supplier. We show that the value of this option is zero. Our analysis leading to this result includes the derivation of several structural properties of the optimal policies for dual sourcing systems with and without the return option.