快速补货模式下的动态定价与库存联合策略

在供应有限的情况下,研究常规补货和快速补货下商品动态定价问题.首先,建立了动态规划模型,理论证明了最优库存策略是基于(s,S)策略下改进的基本库存策略.其次,提出了一种启发式策略求复杂系统的最优策略,启发式算法能够求出最优价格和最优库存水平.最后,数值算例研究表明,库存管理中采用快速补货提高了零售商的利润;初始库存水平越高零售商的利润越高.     

基于设备状态监控的单级备件库存系统补货策略研究

本文研究一个周期性订货的多设备同备件库存系统,将备件库存策略与设备状态监控相结合,讨论了存在设备状态监控情形下的备件库存策略。针对设备状态自然腐蚀过程和人 为修复过程的复合过程,运用一个新的马尔科夫概率转移矩阵对设备需求概率进行刻画,并在此基础上给出静态订货模型和状态监控下的动态订货模型的最优订货策略。通过对比以上两种订货策略优缺点,本文提出一种新的启发式订货策略: 基于关键状态的订货策略模型。该策略可以有效降低对全部设备实行动态监控的信息成本,且成本节省优于静态订货策略,对于企业的现实问题有着较好的指导意义。     

考虑顾客退货和固定成本的联合补货与定价策略

为了吸引更多顾客,许多电子商务零售商允许顾客在一定时间内退货,导致其利润明显减少。同时,在补货时不仅产生依赖补货量的变动成本,而且会产生与补货量无关的固定成本。基于此,以最大化电子商务零售商的利润为目标,建立考虑顾客退货和固定成本的联合补货与定价模型,其中顾客的退货量与满足的需求呈正比。在一般需求情形下,部分刻画多期问题的最优策略;在特殊需求情形下,证明(s,S,p)策略对单期问题最优,并对多期问题的最优策略进行严格刻画。根据已有刻画为多期问题构造启发式策略。数值结果表明启发式策略近似最优;当初始库存水平足够高/低时,最优补货水平和定价随退货率与固定成本单调变化。关键词:联合补货与定价模型;顾客退货;固定成本;随机动态规划;最优策略     

带短期价格折扣和允许两次特殊补货的库存决策模型

针对供应商提供短期价格折扣且允许零售商两次特殊补货的库存系统, 建立了以零售商库存效益最大化为目标的库存决策模型, 分析了模型的性质, 根据经济订单批量补货决策下补货时间点与折扣时段的关系, 确定了零售商在不同补货策略下的库存效益增值函数. 据此给出零售商相应的最优补货策略函数表达式, 提出了该模型的一个全局优化算法, 并通过数值算例验证了模型和算法的有效性与可行性.     

具有不耐烦顾客的生产服务库存系统最优生产策略

研究了基于(s,S)策略有不耐烦顾客的生产服务库存系统.建立了水平相依的拟生灭过程,通过截尾近似的方法求出系统稳态概率向量,并给出系统一些性能指标.最后,设计遗传算法并通过数值算例求解最优生产策略.     

固定分区策略下SDIRP问题周期性最优策略及其算法

随机需求库存-路径问题(Stochastic Demand Inventory Routing Problem,SDIRP)是典型的NP难题,也是实施供应商管理库存策略过程中的关键所在。文章通过引入固定分区策略(Fixed Partition Policy,FPP),将SDIRP分解为若干个独立的子问题,并采用拉格朗日对偶理论以及次梯度算法确定最优的客户分区。在此基础上证明了各子问题的最优周期性策略由分区内各客户的(T,S)库存策略以及相应的最优旅行商路径构成,进而给出了客户需求服从泊松分布时求解最优(T,S)策略各参数的方程组,并设计了求解算法。最后,通过数值算例讨论了上述策略以及算法对于解决SDIRP的有效性。     

机构投资者的最优变现策略

在投资、变现等大宗交易过程中,资产交易价格与交易策略密切相关,因此,交易的完成过程需要很高的技巧.文章讨论了机构投资者的最优变现策略问题,假设证券价格服从几何布朗运动,以均值方差效用为目标函数,得到了最优变现策略所满足的二阶微分方程,并由差分法得到其数值解.最后,由参数的敏感性分析知:最优变现策略与瞬时冲击、市场波动率及风险厌恶系数等参数有关,但与永久冲击无关,且最优变现策略对市场波动率和瞬时冲击的变化较敏感.     

基于(Q,r)补货策略的寄售库存模式研究

本文运用数学建模和数值分析的方法研究了基于(Q,r)补货策略的寄售库存模式中合同参数的优化问题。在该模式下,供应商确定Q、r值,并维持(z,Z)的库存水平,零售商对低于下限z或超过上限Z的库存收取罚金b。研究结果表明,零售商可以通过设置合理的罚金b来激励供应商选择合理的r和Q,使得最低的期望库存水平高于z,并且使成本达到最优。     

动态价格和需求下两级生产系统的最优策略研究

本文研究了动态价格和需求下两级生产系统的最优管理策略问题.文章根据最优控制理论分析了最优生产库存策略的必要和充分条件,得到了特定情形下的最优生产策略,并且把结果推广到包含N个库存的情形。     

医院医疗器械—麻醉包库存策略

麻醉包对于医院来说是一种重要的医疗物品,具有需求量大,一旦缺货则损失严重等特点,为此研究了黑龙江省某医院麻醉包的库存问题.以存储费用为标准来评价和优选库存策略,共建立了四大库存模型,分别是经济批量EOQ库存模型;需求离散条件下多周期有准备成本库存模型;需求服从正态分布条件下多周期有准备成本库存模型和蒙特卡罗仿真库存控制模型.其中,三个模型得出了结果,通过证明,一个模型不符合医院实际情况.通过比较上述三个模型求得的最优化库存策略与医院实际使用的库存策略,建议采取蒙特卡罗仿真库存控制模型求解得出的结果作为医院方最优库存策略.     

Open-loop policies in Bayesian dynamic pricing: Some counter-intuitive observations and insights

In this paper, we show some counter-intuitive observations regarding the open-loop policies in a Bayesian dynamic pricing problem. Specifically, while the common intuition that a policy incorporating more information performs better continues to hold under ample inventory for sale, it breaks down in the case where inventory is limited. This can be explained by the unique feature of selling a limited amount of inventory: information updating may stop prematurely when inventory is depleted, rendering certain demand information being inconsequential.     

Inventory management for dual sales channels with inventory-level-dependent demand

This paper studies the inventory management problem of dual channels operated by one vendor. Demands of dual channels are inventory-level-dependent. We propose a multi-period stochastic dynamic programming model which shows that under mild conditions, the myopic inventory policy is optimal for the infinite horizon problem. To investigate the importance of capturing demand dependency on inventory levels, we consider a heuristic where the vendor ignores demand dependency on inventory levels, and compare the optimal inventory levels with those recommended by the heuristic. Through numerical examples, we show that the vendor may order less for dual channels than those recommended by the heuristic, and the difference between the inventory levels in the two cases can be so large that the demand dependency on inventory levels cannot be neglected. In the end, we numerically examine the impact of different ways to treat unmet demand and obtain some managerial insights.     

Managing transshipments in a multi-retailer system with approximate policies

Using the right transshipment policy is important when transshipments are exercised under demand uncertainty. Optimal transshipment policy can be quite complex in a multi-firm system as optimal actions depend on all system variables. Moreover, both how to select requested retailer and how to respond to requests are in question. We introduce simple, close-to-optimal heuristic transshipment policies for multiple retailers. We first show that heuristic policies may perform even better than self-optimal policy, which is explained by Braess’s paradox. Then we test the performances of various heuristics with respect to centrally optimal policy. When retailers can observe others’ inventory levels, more effective transshipments can be made. Otherwise, a random selection performs quite well. We also observe that although always-accept respond policy is quite close to centrally optimal in small systems, the performance of pairwise-optimal holdback levels to respond requests is more clear and consistent for larger systems.     

Optimal resource allocation across related channels

In this paper, we consider resource allocation strategies of a limited resource across two related channels in a multi-period setting. We study a stochastic control problem where the objective is to determine the optimal limited resource allocation policy across two related channels and optimal transshipment policy between these two channels. We characterize some structural results of the optimal resource allocation policy and show that it is determined by three monotone curves.     

Production-inventory control policy under warm/cold state-dependent fixed costs and stochastic demand: partial characterization and heuristics

The motivation for our study comes from some production and inventory systems in which ordering/producing quantities that exceed certain thresholds in a given period might eliminate some setup activities in the next period. Many examples of such systems have been discussed in prior research but the analysis has been limited to production settings under deterministic demand. In this paper, we consider a periodic-review production-inventory model under stochastic demand and incorporate the following fixed-cost structure into our analysis. When the order quantity in a given period exceeds a specified threshold value, the system is assumed to be in a “warm” state and no fixed cost is incurred in the next period regardless of the order quantity; otherwise the system state is considered “cold” and a positive fixed cost is required to place an order. Assuming that the unsatisfied demand is lost, we develop a dynamic programming formulation of the problem and utilize the concepts of quasi-K-convexity and non-K-decreasing to show some structural results on the optimal cost-to-go functions. This analysis enables us to derive a partial characterization of the optimal policy under the assumption that the demands follow a Pólya or uniform distribution. The optimal policy is defined over multiple decision regions for each system state. We develop heuristic policies that are aimed to address the partially characterized decisions, simplify the ordering policy, and save computational efforts in implementation. The numerical experiments conducted on a large set of test instances including uniform, normal and Poisson demand distributions show that a heuristic policy that is inspired by the optimal policy is able to find the optimal solution in almost all instances, and that a so-called generalized base-stock policy provides quite satisfactory results under reasonable computational efforts. We use our numerical examples to generate insights on the impact of problem parameters. Finally, we extend our analysis into the infinite horizon setting and show that the structure of the optimal policy remains similar.     

Optimal ordering policies for periodic-review systems with replenishment cycles

In this paper, we consider inventory models for periodic-review systems with replenishment cycles, which consist of a number of periods. By replenishment cycles, we mean that an order is always placed at the beginning of a cycle. We use dynamic programming to formulate both the backorder and lost-sales models, and propose to charge the holding and shortage costs based on the ending inventory of periods (rather than only on the ending inventory of cycles). Since periods can be made any time units to suit the needs of an application, this approach in fact computes the holding cost based on the average inventory of a cycle and the shortage cost in proportion to the duration of shortage (for the backorder model), and remedies the shortcomings of the heuristic or approximate treatment of such systems (Hadley and Whitin, Analysis of Inventory Systems, Prentice-Hall, Englewood Cliffs, NJ, 1963). We show that a base-stock policy is optimal for the backorder model, while the optimal order quantity is a function of the on-hand inventory for the lost-sales model. Moreover, for the backorder model, we develop a simple expression for computing the optimal base-stock level; for the lost-sales model, we derive convergence conditions for obtaining the optimal operational parameters.     

基于排队的库存服务系统最优控制策略

研究一个连续盘点的(s,Q)补货的库存服务系统。基于排队理论建立库存水平状态平衡方程,并推导出库存水平稳态概率分布以及作为库存控制的系统稳态性能指标。以库存成本最小化为目标,构建服务水平约束的库存控制模型。针对模型的非线性约束与整数型变量的特征,采用一种改进的遗传算法(IGA)用于决策变量的寻优。数值实验表明,当目标服务水平大于库存系统内生的服务水平时,实施服务水平约束能够降低库存控制成本。     

Managing stochastic inventory systems with free shipping option

In many industries, customers are offered free shipping whenever an order placed exceeds a minimum quantity specified by suppliers. This allows the suppliers to achieve economies of scale in terms of production and distribution by encouraging customers to place large orders. In this paper, we consider the optimal policy of a retailer who operates a single-product inventory system under periodic review. The ordering cost of the retailer is a linear function of the ordering quantity, and the shipping cost is a fixed constant K whenever the order size is less than a given quantity – the free shipping quantity (FSQ), and it is zero whenever the order size is at least as much as the FSQ. Demands in different time periods are i.i.d. random variables. We provide the optimal inventory control policy and characterize its structural properties for the single-period model. For multi-period inventory systems, we propose and analyze a heuristic policy that has a simple structure, the (s, t, S) policy. Optimal parameters of the proposed heuristic policy are then computed. Through an extensive numerical study, we demonstrate that the heuristic policy is sufficiently accurate and close to optimal.     

Determining near optimal base-stock levels in two-stage general inventory systems

This paper proposes an easily implementable, scalable decomposition heuristic for determining near optimal base stocks in two-level general inventory systems. In this heuristic, the general system is decomposed into assembly systems—one for each end product. For these assembly systems, the base-stock levels are calculated separately, taking into account risk-pooling effects for the common components. Our numerical analyses yield two main insights: First, the base-stock levels determined by the heuristic are close-to-optimal. Second, considerable improvements can be obtained compared to common-sense heuristics.