An inventory model for increasing demand under two levels of trade credit linked to order quantity

During the growth stage of a product life cycle especially for high-tech products, the demand function increases with time. In this paper, we extend the constant demand to a linear non-decreasing demand function of time and incorporate a permissible delay in payment under two levels of trade credit into the model. The supplier offers a permissible delay linked to order quantity, and the retailer also provides a downstream trade credit period to its customers. The objective is to find the optimal replenishment cycle that minimizes the retailer’s annual total relevant cost per unit time. The condition for an optimal solution to the generalized model is presented and some fundamental theoretical results are established. Finally, numerical examples to illustrate the proposed model are provided. Sensitivity analysis is performed and some relevant managerial insights are obtained.     

Short-term booking of air cargo space

This paper proposes a stochastic dynamic programming model for a short-term capacity planning model for air cargo space. Long-term cargo space is usually acquired by freight forwarders or shippers many months ahead on a contract basis, and usually the forecasted demand is unreliable. A re-planning of cargo space is needed when the date draws nearer to the flight departure time. Hence, for a given amount of long-term contract space, the decision for each stage is the quantity of additional space required for the next stage and the decision planning model evaluates the optimal cost policy based on the economic trade-off between the cost of backlogged shipment and the cost of acquiring additional cargo space. Under certain conditions, we show that the return function is convex with respect to the additional space acquired for a given state and the optimal expected cost for the remaining stages is an increasing convex function with respect to the state variables. These two properties can be carried backward recursively and therefore the optimal cost policy can be determined efficiently.     

A manufacturer's optimal quantity discount strategy and return policy through game-theoretic approach

This study generalised the traditional quantity discount problem with return contracts, in which a manufacturer promises to refund some fraction of the retailer's wholesale price if an item is returned, as a two-stage game. In the first stage the manufacturer and retailer determine the inventory level cooperatively. In the second stage, the manufacturer bargains with the retailer for quantity discount and return schemes to maintain channel efficiency. A menu of discount–return combinations is proposed for the manufacturer to make inventory decisions. The model developed will demonstrate that the return policy can be considered as mirror images of quantity discount strategy. That is, options with more generous return privileges are coupled with higher wholesale prices, whereas the lowest wholesale price comes with very strict limits on returns and a restocking fee for any returned goods.     

Integrated pricing and lot-sizing decisions in a serial supply chain

In this article, we consider a serial supply chain controlled by a decision-maker who is responsible for deciding the amount of raw material to order from the selected suppliers, the amount of product to transfer between consecutive stages in order to avoid any inventory shortages, and the final product's selling price so that the profit per time unit is maximized. Coordinating all these decisions simultaneously is a topic that has been neglected in literature. This integrated process is modeled as a mixed-integer nonlinear programming model. In addition, the model requires the order quantity received from each selected supplier to be an integer multiple of the order quantity delivered to the following stage, which means that a different multiplicative factor can be assigned to each supplier. This coordination mechanism shows an improvement in the objective function compared to existing models that assign the same multiplicative factor to each selected supplier. Moreover, we develop a heuristic algorithm that generates near optimal solutions in a timely manner. Two numerical examples are presented to illustrate the proposed model and the heuristic algorithm.     

MODELING REGIONAL IRRIGATION DECISIONS AND DRAINAGE POLLUTION CONTROL

A regional model is proposed for evaluating policies aimed at controlling the quantity and quality of drainage from agricultural lands. The model includes both physical and economic components. The physical component incorporates a crop-water production function which computes the physical soil-water-crop relationships. The multi-farm economic model is a regional income maximization model. The model includes also physical and institutional constraints for each farm and the region. Policies evaluated in this context are varying taxes and constraints on drainage discharge and surface water use. The model was applied to a region in California. Results for alternative control policies are compared on the basis of resource use, regional income, drainage quality and quantity, and the resulting pollution load.     

整批间隔进货的存储量控制模型与随机局部弹性

利用作者于文[1，2]引入的随机弹性理论，研究了整批间隔进货并不允许缺货的随机存储模型中，一个采购周期内的总费用对最高存储量的弹性．给出了总费用弹性概率密度函数的一般表达式．进一步给出了存在多种需求物资的随机存储模型中总费用对单个物资采购量的弹性分布函数和概率密度函数，讨论了随着采购量的随机变化，总费用的弹性变化规律和变化范围．通过实例研究了当最高库存量的分布特性已知且采购量服从某一分布时，总费用的弹性变化范围及在该变化范围的可信度．     

Optimal investment in setup reduction in manufacturing systems with WIP inventories

A number of models have been proposed to predict optimal setup times, or optimal investment in setup reduction, in manufacturing cells. These have been based on the economic order quantity (EOQ) or economic production quantity (EPQ) model formulation, and have a common limitation in that they neglect work-in-process (WIP) inventories, which can be substantial in manufacturing systems. In this paper a new model is developed that predicts optimal production batch sizes and investments in setup reduction. This model is based on queuing theory, which permits it to estimate WIP levels as a function of the decisions variables, batch size and setup time. Optimal values for batch size and setup time are found analytically, even though the total cost model was shown to be strictly non-convex.     

Sensitivity analysis of the newsvendor model

Quality of decisions in inventory management models depends on the accuracy of parameter estimates used for decision making. In many situations, error in decision making is unavoidable. In such cases, sensitivity analysis is necessary for better implementation of the model. Though the newsvendor model is one of the most researched inventory models, little is known about its robustness. In this paper, we perform sensitivity analysis of the classical newsvendor model. Conditions for symmetry/skewness of cost deviation (i.e., deviation of expected demand–supply mismatch cost from its minimum) have been identified. These conditions are closely linked with symmetry/skewness of the demand density function. A lower bound of cost deviation is established for symmetric unimodal demand distributions. Based on demonstrations of the lower bound, we found the newsvendor model to be sensitive to sub-optimal ordering decisions, more sensitive than the economic order quantity model. Order quantity deviation (i.e., deviation of order quantity from its optimum) is explored briefly. We found the magnitude of order quantity deviation to be comparable with that of parameter estimation error. Mean demand is identified as the most influential parameter in deciding order quantity deviation.     

A comprehensive note on: An economic order quantity with imperfect quality and quantity discounts

Lin [T.Y. Lin, An economic order quantity with imperfect quality and quantity discounts, Appl. Math. Model. 34 (10) (2010) 3158–3165] recently proposed an EOQ model with imperfect quality and quantity discounts, where the lot-splitting shipments policy is adopted. In this note we first rectify the holding cost terms showed in Lin to obtain a new objective function, then resolve the problem and develop an easy to implement algorithm to find the overall optimal solutions for the model. Besides, we present a new model for items with imperfect quality, where lot-splitting shipments and different holding costs for good and defective items are considered. The closed-form formulas for determining the optimal ordering and shipping policies are derived. Also, the results are examined analytically and numerically to gain more insights of the solutions.     

A minimax distribution-free procedure for a newsvendor problem with free shipping

In this paper, we study the optimal policies of retailers who operate their inventory with a single period model (i.e., newsvendor model) under a free shipping offer where a fixed shipping fee is exempted if an order quantity is greater than or equal to a given minimum quantity. Zhou et al. (2009) have explored this model, and we further investigate their analysis for the optimal ordering policies which they did not sufficiently develop. Based on the investigation, we extend the base model in order to deal with the practically important aspect of inventory management when the exact distribution function of demand is not available. We incorporate the aspect into the base model and present the optimal policies for the extended model with a numerical example. Finally, we conduct extensive numerical experiments to evaluate the performance of the extended model and analyze the impacts of minimum free shipping quantity and the fixed shipping fee on the performance.     

不确定箱量下内河集装箱班轮航线动态配载决策

内河集装箱班轮运输中海关抽检可导致外贸箱箱量不断发生变化,班轮航线配载需要动态决策。基于滚动调度策略,将当前港口的配载决策按随机事件划分为多个阶段,以最小化班轮堆栈占用数量和相邻阶段间配载计划偏差为目标,构建单港口单阶段的配载决策模型,进而滚动实现班轮航线动态配载决策。基于大邻域搜索思想设计一种包含整数规划、破坏器与修复器的精确启发式算法,实现港口多阶段滚动配载。基于真实场景的算例研究表明,在优化堆栈占用数量方面,模型与算法之间差异不大,但在考虑相邻阶段间配载计划偏差时,算法的求解结果要优于模型。因此,模型与算法可用来辅助实现不确定箱量下内河集装箱班轮航线动态配载决策,且算法表现更优,可实现配载计划对不确定箱量的鲁棒吸收。     

Fuzzy profit measures for a fuzzy economic order quantity model

Changing economic conditions make the selling price and demand quantity more and more uncertain in the market. The conventional inventory models determine the selling price and order quantity for a retailer’s maximal profit with exactly known parameters. This paper develops a solution method to derive the fuzzy profit of the inventory model when the demand quantity and unit cost are fuzzy numbers. Since the parameters contained in the inventory model are fuzzy, the profit value calculated from the model should be fuzzy as well. Based on the extension principle, the fuzzy inventory problem is transformed into a pair of two-level mathematical programs to derive the upper bound and lower bound of the fuzzy profit at possibility level α. According to the duality theorem of geometric programming, the pair of two-level mathematical programs is transformed into a pair of conventional geometric programs to solve. By enumerating different α values, the upper bound and lower bound of the fuzzy profit are collected to approximate the membership function. Since the profit of the inventory problem is expressed by the membership function rather than by a crisp value, more information is provided for making decisions.     

Multiproduct single-machine production system with stochastic scrapped production rate, partial backordering and service level constraint

In this paper, a multiproduct single-machine production system under economic production quantity (EPQ) model is studied in which the existence of only one machine causes a limited production capacity for the common cycle length of all products, the production defective rates are random variables, shortages are allowed and take a combination of backorder and lost sale, and there is a service rate constraint for the company. The aim of this research is to determine the optimal production quantity, the allowable shortage level, and the period length of each product such that the expected total cost, including holding, shortage, production, setup and defective items costs, is minimized. The mathematical model of the problem is derived for which the objective function is proved to be convex. Then, a derivative approach is utilized to obtain the optimal solution. Finally, two numerical examples in each of which a sensitivity analysis is performed on the model parameters, are provided to illustrate the practical usage of the proposed methodology.     

A generalized discount structure and some dominance rules for selecting the price-break EOQ

This paper introduces a generalized discount structure that combines the features of incremental and all-units quantity discount policies. General properties of the EOQ model under this discount structure are studied, and dominance rules for comparing order quantity intervals under either type of discount policy are established. In addition, procedures for developing an iso-cost function and the minimal feasible set for optimal order quantity are proposed.     

Optimal inventory policies for an economic order quantity model with decreasing cost functions

In this paper, three total cost minimization EOQ based inventory problems are modeled and analyzed using geometric programming (GP) techniques. Through GP, optimal solutions for these models are found and sensitivity analysis is performed to investigate the effects of percentage changes in the primal objective function coefficients. The effects on the changes in the optimal order quantity and total cost when different parameters of the problems are changed is also investigated. In addition, a comparative analysis between the total cost minimization models and the basic EOQ model is conducted. By investigating the error in the optimal order quantity and total cost of these models, several interesting economic implications and managerial insights can be observed.     

Optimal pricing and ordering policy for non-instantaneous deteriorating items under inflation and customer returns

This paper deals with an economic production quantity inventory model for non-instantaneous deteriorating items under inflationary conditions considering customer returns. We adopt a price- and time-dependent demand function. Also, the customer returns are considered as a function of both price and demand. The effects of time value of money are studied using the Discounted Cash Flow approach. The main objective is to determine the optimal selling price, the optimal replenishment cycles, and the optimal production quantity simultaneously such that the present value of total profit is maximized. An efficient algorithm is presented to find the optimal solution. Finally, numerical examples are provided to solve the presented inventory model using our proposed algorithm, which is further clarified through a sensitivity analysis. The results of analysing customer returns provide important suggestions to financial managers who use price as a control to match the quantity sold to inventory while maximizing revenues. The paper ends with a conclusion and an outlook to future studies.     

Economic order quantity model and Taguchi’s cost of poor quality

In this paper we consider defective products and Taguchi’s cost of poor quality in the economic order quantity (EOQ) model. We assume that the product quality performs a normal distribution function, and the Taguchi’s poor quality cost has been involved. From our analysis, it has been found that the annual profit will be decreased if the poor quality of product and Taguchi’s quality cost are involved in the model. It has also been found that economic order quantity in our model is larger than that in a traditional EOQ model.     

基于存货质押的库存管理研究

针对基于存货质押的库存管理问题进行分析并构建模型,从还贷和不还贷两个角度,研究模型的最佳订货批量和质押量.模型以利润函数为目标函数,利润函数包括了销售额、缺货损失、剩余品价值、产品成本以及贷款本息和.通过对利润函数进行求解,得出最佳订购批量、存货质押量和最大期望利润,最后根据利润的大小决定是否偿还贷款.     

A Bayesian approach for multiple criteria decision making with applications in Design for Six Sigma

Linking end-customer preferences with variables controlled at a manufacturing plant is a main idea behind popular Design for Six Sigma management techniques. Multiple criteria decision making (MCDM) approaches can be used for such purposes, but in these techniques the decision-maker's (DM) utility function, if modelled explicitly, is considered known with certainty once assessed. Here, a new algorithm is proposed to solve a MCDM problem with applications to Design for Six Sigma based on a Bayesian methodology. At a first stage, it is assumed that there are process responses that are functions of certain controllable factors or regressors. This relation is modelled based on experimental data. At a second stage, the utility function of one or more DMs or customers is described in a statistical model as a function of the process responses, based on surveys. This step considers the uncertainty in the utility function(s) explicitly. The methodology presented then maximizes the probability that the DM's or customer's utility is greater than some given lower bound with respect to the controllable factors of the first stage. Both stages are modelled with Bayesian regression techniques. The advantages of using the Bayesian approach as opposed to traditional methods are highlighted.     

Economic production quantity model with repair failure and limited capacity

We develop an economic production quantity (EPQ) model with random defective items and failure in repair. The existence of only one machine results with limited production capacity and shortages. The aim of this research is to derive the optimal cycle length, the optimal production quantity and the optimal back ordered quantity for each product so as to minimize the total expected cost (holding, shortage, production, setup, defective items and repair costs). The convexity of the model is derived and the objective function is proved convex. Two numerical examples illustrate the practical usage of the proposed method.