考虑需求率变化与延期支付的临时订货模型

供应商给予临时价格折扣、并针对采购商的临时订货给予优惠的延期支付条件;同时考虑到采购商的自身需求即为终端需求并对价格敏感,由于考虑需求量变化的同时考虑到时间因素,因此需求率实际上发生了改变。在这样的背景下,采购商在价格变化时刻面临两种选择:以低价购进大量货物或者放弃这个机会。本文以EOQ模型单位产品成本为参考,从成本节约最大的角度出发,分析了采购商临时订货量的确定过程,并用数例分析了参数变化对最优订货以及成本节约情况的影响。     

需求随价格变化的具有折扣的易变质物品的库存模型

本文研究了在需求随价格变化及物品易变质的条件下，当供应商给予数量折扣时的库存问题。证明了当供应商给予数量折扣时，零售商的需求量是增大的，并给出了供应商给予数量折扣时零售商的订货量和订货周期的计算方法。对物品变质率和需求价格敏感系数对零售商的订货量、订货周期、出售价格和单位时间利润的影响进行了数值分析，并给出了数值算例。     

模糊随机需求报童问题的Stackelberg-Nash均衡策略

针对模糊随机需求下的分布控制型报童问题,建立了无数量折扣和有数量折扣情况下的利润最大化两层规划模型,并结合模糊随机模拟技术和遗传算法设计了模型求解的混合智能算法.解决了上层制造商制定包括折扣区间和折扣价格的最优数量折扣策略,以及下层多零售商确定各自的最优订货量的Stackelberg-Nash均衡策略问题.     

一个带有增加量价格折扣的两货栈库存模型

已有的确定性两货栈(其中一个是自己货栈(OW);另一个是租用货栈(RW))库存模型通常不考虑增加量价格折扣,然而在实际生活中,增加量价格折扣却是促使库存管理者加大订货量的一个重要原因.本文通过考虑增加量价格折扣而将两货栈系统作了进一步扩展,在采用间隔式运输模式运送RW的物品到OW的情形下,建立了一个带有增加量价格折扣并允许短缺的两货栈库存模型,提供了一种寻求最优库存策略的简单方法.     

考虑消费者低碳偏好的可替代产品订货决策

基于产品的可替代性,考虑零售商有无能力限制两种情况,订购环境质量和价格不同的两种产品,建立利润最大化模型,确定两种产品的最优订货量和消费者低碳偏好对其的影响.理论分析表明:库存能力的提高使得订货量增大,订单数量和利润随环境质量的变化而变化;算例计算的结果验证了模型的合理性,数值分析表明:提高库存容量和订购替代率大的两种产品有利于利润的增加,当库存无容量限制时,订货量和销售商利润水平不再受容量变化的影响.     

基于不对称需求信息的数量折扣设计

通过建立数量折扣设计优化模型,利用委托代理理论分别分析了完全和不完全需求信息条件下,商品供应商如何为一群异质的零售商制定价格政策,实现其中望利润的最大化.在完全需求信息条件下,单一价格就能使供应商实现利润最大化;而在需求信息为不对称条件下,逆向选择使单一价格失效,此时,数量折扣是一种能够有效增加零售商订货量的激励方式.     

碳排放交易机制下的供应链运作策略及协调研究

碳排放权交易是控制和减少碳排放的有效工具,同时也使得企业的运作成本增加,供应链的决策管理更复杂。考虑由零售商和制造商组成的二阶段供应链,研究不同碳排放权交易政策(供应链成员内部碳排放权交易、外部市场碳排放权交易)下基于数量折扣契约的供应链协调问题,并与无碳排放约束的情形进行比较。研究结果表明:在不同碳排放权交易政策下,数量折扣契约能够实现供应链的协调;无碳排放约束的供应链最优订货量大于外部碳排放权交易的供应链最优订货量,成员内部碳排放交易下的供应链最优订货量和供应链期望利润随着碳排放配额的增加最终等于无碳排放约束下的供应链最优订货量和供应链期望利润;外部市场碳排放交易政策下供应链的期望利润随着碳排放配额的增加而增加。     

一种随机需求下的供应链激励机制

研究了在随机需求条件下，供应链中用价格折扣策略协调供需双方利益的问题。考虑随机性需求有可能造成分销商的库存积压，本提出了生产商给予分销商的积压商品价格折扣的策略，分析了其激励机制，给出了最优价格折扣的模型和算法。最后用数值方法验证了这种价格折扣策略能够给生产商和分销商带来利益改善，而且需求波动越大，该策略的协调效果越好。     

需求不确定的供应链两阶段订货模型

销售商如何在不确定需求的市场环境下根据制造商提供的订货条件进行合理订货是供应链管理的一个核心问题。本文利用信号博弈的原理从销售商的角度研究在不确定需求且传统需求预测方法失效的情况下，允许调整订货量的短生命周期产品两阶段订货模型，得到了在两次订货条件下销售商应该采取的最优订货量与调整策略以及制造商对契约灵活性限制的成本函数。     

模糊随机需求下多产品报童问题的均衡策略

针对模糊随机需求下单制造商多零售商的分布控制型多产品报童问题, 建立了含资金约束的期望利润最大化两层规划模型.结合模糊随机模拟技术与遗传算法, 设计了求解模型的混合智能算法.该算法不仅可获得上层制造商的最优折扣批发价及下层零售商的最优订购量,亦可求得该折扣形式的起始折扣点(折扣区间).算例分析表明,当制造商采取最优数量折扣策略时:1)促使零售商订货量增加至资金约束上限;2)部分产品订货量可达模糊随机市场需求的最大可能值:3)零售商和制造商的利润均增加.     

A Backlog Inventory Model During Restricted Sale Periods

Optimal purchasing policies are derived for a continuous review inventory system in which backlogs are permissible and temporary price discounts are available. This paper derives such policies assuming that the sale price is not available at the regularly-scheduled replenishment time. Results indicate that, under specific conditions, maximum positive cost savings are generated when a special order is placed at the end of the sale period.     

Dynamic lot-sizing with price changes and price-dependent holding costs

This paper proposes a new formulation of the dynamic lot-sizing problem with price changes which considers the unit inventory holding costs in a period as a function of the procurement decisions made in previous periods. In Section 1, the problem is defined and some of its fundamental properties are identified. A dynamic programming approach is developed to solve it when solutions are restricted to sequential extreme flows, and results from location theory are used to derive an O(T²) algorithm which provides a provably optimal solution of an integer linear programming formulation of the general problem. In Section 2, a heuristic is developed for the case where the inventory carrying rates and the order costs are constant, and where the item price can change once during the planning horizon. Permanent price increases, permanent price decreases and temporary price reductions are considered. In Section 3, extensive testing of the various optimal and heuristic algorithms is reported. Our results show that, in this context, the two following intuitive actions usually lead to near optimal solutions: accumulate stock at the lower price just prior to price increase and cut short on orders when a price decrease is imminent.     

Scheduling batches in flowshop with limited buffers in the shampoo industry

In this paper we address the problem of planning a temporary storage area in a real production system. This temporary storage area is composed of parallel temporary storage units with distinct capacities. The storage operation of a job, also called a batch, has to answer time restrictions such as release dates, due dates, restricted family dependent setup times and time lags, and also a space constraint which is the capacity of the temporary storage unit. The goal is to schedule the batches on the storage units in order to minimize the total setup times and the maximum lateness. First, we model the problem on a single storage unit as a two-machine flowshop problem with a limited buffer capacity and we show that it is NP-hard. We also show that the particular case in which no lateness is allowed is solvable in polynomial time under special conditions on the buffer capacity, both for single or parallel temporary storage units. Next we provide three heuristics: a greedy algorithm, a hybrid heuristic based on Ant Colony Optimization and Simulated Annealing and finally a dedicated heuristic. The latter strongly exploits the structural properties shown in this paper. We provide experimental results which highlight the efficiency of the dedicated heuristic in comparison with the two other heuristics.     

Order Quantities with Temporary Price Reductions

The temporary price-change problem is studied, in which the objective is to minimize discounted cash flows. As pointed out by Goyal in an earlier paper, only the cash transactions at purchase times (i.e. the payments for the goods and the ordering costs) were considered. The cash flows associated with `inventory maintenance' costs which occur more or less continuously over time were neglected, which changes the structure of the model. Examples of these costs include storage, insurance, record-keeping, deterioration and obsolescence costs. In this paper, these continuously generated cash flows are included in the analysis, thereby making the new model more applicable to practical situations. This model is of interest because order-quantity decisions often must be made under conditions of both temporary price reductions and/or imminent price increases. These changes occur frequently in practice.     

The impact of illiquidity on the asset management of insurance companies

This paper investigates optimal asset management strategies for property and casualty insurance companies in illiquid markets. Using a cash-flow based liquidation model of an insurance company, we consider the effects of permanent and temporary price impact as well as commonality in price impact. Focusing on the interaction of a single large investor with the financial market makes the main results generally applicable for any institutional investor with stochastic future liabilities and restrictions on short-sales and financial leverage. Our analysis reveals a clear diversification benefit in illiquid markets apart from the one introduced by Markowitz [Markowitz, H., 1952. Portfolio selection. J. Financ. 7, 77-91]. In the presence of commonality, cash-flow matching is shown to be the optimal strategy for a large investor.     

An economic order quantity model with partial backordering and a special sale price

A constant unit purchase cost is one of the main assumptions in the classic economic order quantity model. In practice, suppliers sometimes offer special sale prices to stimulate sales or decrease inventories of certain items. In this paper we develop an EOQ model with a special sale price and partial backordering. We prove the convexity of the cost-reduction function if a special order is placed at the special sale price. A solution method is proposed and numerical examples are presented.     

Management Adopts Operational Research: Session Report

Quantitative methods are derived to assist buyers purchasing commodities in fluctuating price markets. Demand is known whilst price is a stochastic variable which may contain trends or seasonal fluctuations. The essential feature of the problem is that the buyer has many opportunities to make a purchase.Mathematical models are formulated to describe particular commodity buying problems. The optimal purchasing policy is derived by using dynamic programming. It consists of a set of discrete price breaks at each buying opportunity together with the associated stock levels the buyer should aim to achieve at each price break with his purchase at this opportunity. The price breaks are dependent on the probability density functions of future prices and the number of future buying opportunities. Recurrence relations are derived to calculate these price breaks. The case of restrictions on the purchase quantity at each price offer, either because of supply limitations or by the buyer as a policy decision, and price discounts are also considered.A case study illustrating the techniques is given and the methods are extended to purchasing for a blending problem with substitutable commodities.     

Temporary price increase during replenishment lead time

Pricing and inventory management make up together revenue management, which is a significant effort to boost revenues out of available resources. Firms use various forms of dynamic pricing, including personalized pricing, markdowns, promotions, coupons, discounts, and clearance sales, to respond to market fluctuations and demand uncertainty. In this paper, we study a temporary price increase policy, a form of dynamic pricing, for a non-perishable product, a practice used by several giant retailers such as Amazon, Walmart, and Apple. We develop a continuous review inventory model that allows for joint replenishment and pricing decisions, where the lead time is not zero. A replenishment decision controls supply, while a pricing decision controls demand. A manager exercises a temporary price increase to slow demand and avoid a stock-out situation while waiting for a shipment, which may not necessarily increase revenues, but decrease stock-out costs. The problem is to solve for the optimal replenishment and the pricing policy parameters that maximize the long-run expected profit. That is, when and how much to order and when to raise the price. In this paper, the inventory level and time trigger a price increase. We solve many numerical examples and perform extensive sensitivity analyses. Our results show that compared to a model that focuses on fixed pricing, our model brings an additional increase in profit of about 13%.