基于CPFR的多产品分销系统库存优化模型

本文中的分销系统由生产多种产品的多个制造商,一个地区分销中心DC,多个零售商所组成,系统采用基于CPFR来确定订货临界点,并且在假设DC和零售商都实行连续性盘点的(R,Q)库存控制策略,提前期为随机变量,零售商需求为泊松分布的前提下,以整个分销系统的库存成本最小化为目标函数,以DC和零售商的多产品服务水平为约束条件,通过确定最佳订货批量,建立了此多产品分销系统的库存优化模型,从而达到有效控制库存的目的.     

考虑缓冲区库存不足情况下的设备维护与缓冲区库存联合优化研究

针对缓冲区库存不足的两设备流水线生产系统(2M1B系统)设备维护问题,提出了生产设备维护与缓冲库存联合优化模型。首先,采用指数分布描述设备故障规律,表达运行周期的总故障次数;其次,通过分析缓冲区库存量在达到额定库存后的变化,提出了利用条件概率改进库存充足和库存不足两种情况下的设备维护和缓冲库存模型,基于更新酬劳定理,以故障次数和额定库存为决策变量,以总费用为目标函数,建立缓冲区库存不足情况下的设备维护与缓冲库存联合优化模型,并且将生产系统的缺货费用集成到了总费用模型;最后,通过算例分析,计算故障次数和最优缓冲区额定库存量,进行了灵敏度分析,验证了模型有效性,丰富了考虑缓冲库存的设备理论。     

一种考虑原材料库存成本的变质物品EPQ模型

在库存管理研究中,单独实现产成品或原材料库存成本最小,往往无法获得整个库存系统的最优控制策略.本文提出了一种同时考虑产成品和原材料库存成本的变质物品EPQ集成模型.运用迭代寻优法得到模型最优解,得出计划期内最优原材料订购次数,原材料订购周期内的最优生产次数和最优服务水平,并对主要参数进行了灵敏度分析,为生产制造企业的库存管理提供了决策依据.     

新鲜产品跨季销售中的动态库存管理策略研究

新鲜产品在跨季销售过程中不可避免地发生变质，给分销商带来较大的风险。本文基于一个两阶段决策模型来帮助分销商动态管理库存，在考虑产品质量随机变化的情形下更好地匹配供给与需求。具体来说，在采摘季节确定采购的总数量；在销售季节结合产品新鲜度的演变以及剩余库存水平的变化动态地决策每周期的销售量，以实现期望利润的最大化。我们采用动态规划模型分析销售期的库存管理决策，刻画了利润函数和最优决策的结构性质，并考察了库存量、新鲜度水平和价格等参数对最优库存决策的影响。数值实验结果表明，当产品变质风险大、需求不确定性低时，动态库存管理策略相比静态策略的利润改进效果更为显著。     

需求替代情形下反应能力的价值分析

本文通过对竞争报童模型加以拓展以研究需求替代情形下企业运用反应能力产生的价值。文中考虑了两种不同的需求结构:第一种中企业总需求是竞争双方库存量的分段线性函数,第二种中企业总需求的均值是双方库存水平任意形式的函数。根据需求结构的不同,建立了不同的库存竞争模型并提出了相应的均衡库存策略。基于这些均衡结论,进一步探讨了反应能力的价值。分析表明运用反应能力能在降低企业库存水平的同时提高顾客服务水平。此外,在不同的需求结构下,运用反应能力产生的价值均随可用反应能力以及缺货惩罚成本递增,而随单位反应能力的使用成本递减。     

部分延迟订购的Weibull分布变质品库存策略研究

研究需求依赖于即时库存、变质服从Weibull分布、部分延迟订购且延迟订购率与等待时间相关的变质品库存优化策略.当需求依赖于库存水平时,利润最大化问题不能简化为成本最小化问题,所以以利润最大化为目标构建库存模型,并证明了最优解的存在性和唯一性,同时给出应用牛顿迭代法求解模型的具体步骤.最后,通过算例给出了模型的应用并对参数的灵敏度进行了分析.结果表明:延迟订购阻力、缺货成本或机会成本的增加会降低系统的利润同时要求提升系统的服务水平;系统的平均利润对购买成本比较敏感,而服务水平对库存持有成本非常敏感.     

基于系统柔性的MTS-MTO混合生产决策

研究生产商采用MTS、MTO混合作业的方式为不同客户提供产品和服务的策略。计划利用一组可灵活控制的动态设备处理那些不同需求的MTS和MTO生产业务,为此,我们开发了一个多服务台的排队模型,利用拟生灭过程和相位型分布得到了MTS、MTO排队系统平衡条件和稳态概率矩阵几何解。通过求解分块矩阵方程组,给出了系统队列长度、平均等待队长、顾客服务水平等绩效测度指标。建立了系统运作成本最优化的数学模型,采用搜索算法,确定了关键参数的边界值,找到了混合系统运作的最优策略。数值模拟和系统绩效比较分析结果显示：（1）动态切换策略能够更快速的帮助MTS恢复目标库存量,控制系统缺货风险,降低库存持有成本;（2）找到了满足顾客服务水平的最少的设备配置数量和库存成本最低的生产切换时间,且动态系统的平均队列长度低于静态系统;（3）混合运作策略减少了约2/3的静态系统平均队列长度,企业在队列长度减小的窗口期内可以接受更多订单和缩短MTO订单交货时间。     

基于UML的钢铁企业APS生产能力计划系统建模

钢铁企业APS生产能力计划系统是实现产销一体化需求的重要途径.本文对生产能力计划系统的需求进行分析,同时考虑产能、库存、市场等约束条件建立数学模型.在此基础上,分析了系统的体系结构和功能需求,并利用统一建模语言(UML)对系统的用例模型、静态模型和动态模型进行了详细的分析和设计.     

基于新产品的供应链最优生产策略

商业竞争与技术进步导致产品生命周期的缩短,要求企业能不断地推出新产品。而新产品的生产与需求是一个逐步适应和熟悉的过程,具有明显的学习效应。本文运用Bass模型来描述新产品的需求扩散效应,建立了新产品供、产、销三级供应链的生产存储优化模型,并通过算例,计算机模拟出最优生产批量。该研究改进了传统的一般产品的生产存储计划,对面向市场需求的供应链的新产品投产具有重要的指导意义。     

基于备件需求优先级的随机库存控制模型研究

根据所安装设备发生故障时对生产过程的影响,备件的需求可划分为关键需求和非关键需求。基于此,研究了需求服从泊松分布、提前期等于常数、基于(S-1,S)订货策略的随机库存模型,提出当库存降低到预定水平,则预留存货以满足关键需求的控制策略,并给出了不同优先级备件服务水平的计算方法。最后结合某核电站库存管理中的一个实例,验证了本文所设计模型的优点和准确性。     

回收物流库存控制研究

本文探讨了包含回收品的库存系统。回收物流库存中既有一般生产过程的新产品,也有经再制造的回收品,使其最优库存策略非常复杂,论文推导出不同控制方式下的EOQ模型,以确定最优生产和再制造批量。此外,还对求得的最优批量进行适当的调整,以确保在循环周期中的定货次数为整数。该模型形式、结构简单易于在实践中应用。     

Dynamic Pricing and Inventory Management with Regular and Expedited Supplies

We consider a periodic‐review inventory system with regular and expedited supply modes. The expedited supply is faster than the regular supply but incurs a higher cost. Demand for the product in each period is random and sensitive to its selling price. The firm determines its order quantity from each supply in each period as well as its selling price to maximize the expected total discounted profit over a finite or an infinite planning horizon. We show that, in each period if it is optimal to order from both supplies, the optimal inventory policy is determined by two state‐independent thresholds, one for each supply mode, and a list price is set for the product; if only the regular supply is used, the optimal policy is a state‐dependent base‐stock policy, that is, the optimal base‐stock level depends on the starting inventory level, and the optimal selling price is a markdown price that decreases with the starting inventory level. We further study the operational impact of such supply diversification and show that it increases the firm's expected profit, reduces the optimal safety‐stock levels, and lowers the optimal selling price. Thus that diversification is beneficial to both the firm and its customers. Building upon these results, we conduct a numerical study to assess and compare the respective benefit of dynamic pricing and supply diversification.     

Optimal Planning Quantities for Product Transition

The replacement of an existing product with a new one presents many challenges. In particular, uncertainties in a new product introduction often lead to extreme cases of demand and supply mismatches. This paper addresses inventory planning decisions for product upgrades when there is no replenishment opportunity during the transition period. We allow product substitution: when a company runs out of the old product, a customer may be offered the new product as a substitute. We show that the optimal substitution decision is a time‐varying threshold policy and establish the optimal planning policy. Further, we determine the optimal delay in a new product introduction, given the initial inventory of the old product.     

PRODUCTION SCHEDULING OF CONTINUOUS FLOW LINES: MULTIPLE PRODUCTS WITH SETUP TIMES AND COSTS

The problem of production planning and setup scheduling of multiple products on a single facility is studied in this paper. The facility can only produce one product at a time. A setup is required when the production switches from one type of product to another. Both setup times and setup costs are considered. The objective is to determine the setup schedule and production rate for each product that minimize the average total costs, which include the inventory, backlog, and setup costs. Under the assumption of a constant production rate, we obtain the optimal cyclic rotation schedule for the multiple products system. Besides the decision variables studied in the classical economic lot scheduling problem (ELSP), the production rate is also a decision variable in our model. We prove that our solutions improve the results of the classical ELSP.     

Combined Pricing and Portfolio Option Procurement

In this paper, we study a single‐product periodic‐review inventory system that faces random and price‐dependent demand. The firm can purchase the product either from option contracts or from the spot market. Different option contracts are offered by a set of suppliers with a two‐part fee structure: a unit reservation cost and a unit exercising cost. The spot market price is random and its realization may affect the subsequent option contract prices. The firm decides the reservation quantity from each supplier and the product selling price at the beginning of each period and the number of options to exercise (inventory replenishment) at the end of the period to maximize the total expected profit over its planning horizon. We show that the optimal inventory replenishment policy is order‐up‐to type with a sequence of decreasing thresholds. We also investigate the optimal option‐reservation policy and the optimal pricing strategy. The optimal reservation quantities and selling price are shown to be both decreasing in the starting inventory level when demand function is additive. Building upon the analytical results, we conduct a numerical study to unveil additional managerial insights. Among other things, we quantify the values of the option contracts and dynamic pricing to the firm and show that they are more significant when the market demand becomes more volatile.     

A multistage approach for production and workforce planning in long-cycle product environments

Aggregate production planning (APP) has been studied extensively for the past two decades. The APP problem, also called production and workforce scheduling, is to determine the optimal workforce and production level in each period of the planning horizon in order to satisfy demand forecasts for these periods. The advantages of the APP are low cost of data collection and computational cost of the running model; the accuracy of data; and, effective managerial understanding of the results. If the product of concern takes longer than one period, it is called a long-cycle product. Examples of long-cycle products are aircraft, ships, buildings and special machines. A detailed model incorporating dynamic productivity and long-cycle products considerations is presented to solve the problem of production and workforce planning. Using a multistage production system approach, a search technique is developed to solve this class of problems where the objective function is linear and some of the constraint coefficients are dynamically nonlinear. The model provides a better solution than an aggregate production planning model, often used to solve these problems.     

An interactive decision support system for an aggregate production planning model based on multiple criteria mixed integer linear programming

In this paper, we present an aggregate production planning (APP) model applied to a Portuguese firm that produces construction materials. A multiple criteria mixed integer linear programming (MCMILP) model is developed with the following performance criteria: (1) maximize profit, (2) minimize late orders, and (3) minimize work force level changes. It includes certain operational features such as partial inflexibility of the work force, legal restrictions on workload, work force size (workers to be hired and downsized), workers in training, and production and inventory capacity. The purpose is to determine the number of workers for each worker type, the number of overtime hours, the inventory level for each product category, and the level of subcontracting in order to meet the forecasted demand for a planning period of 12 months. Additionally, a decision support system (DSS) based on the MCMILP model is proposed. It will help practitioners find the “best” solution for an APP problem without having to familiarize themselves with the mathematical complexities associated with the model. An example to illustrate the use of the DSS is also included.     

Dynamic Pricing and Inventory Management with Dual Suppliers of Different Lead Times and Disruption Risks

It is common for a firm to make use of multiple suppliers of different delivery lead times, reliabilities, and costs. In this study, we are concerned with the joint pricing and inventory control problem for such a firm that has a quick‐response supplier and a regular supplier that both suffer random disruptions, and faces price‐sensitive random demands. We aim at characterizing the optimal ordering and pricing policies in each period over a planning horizon, and analyzing the impacts of supply source diversification. We show that, when both suppliers are unreliable, the optimal inventory policy in each period is a reorder point policy and the optimal price is decreasing in the starting inventory level in that period. In addition, we show that having supply source diversification or higher supplier reliability increases the firm's optimal profit and lowers the optimal selling price. We also demonstrate that, with the selling price as a decision, a supplier may receive even more orders from the firm after an additional supplier is introduced. For the special case where the quick‐response supplier is perfectly reliable, we further show that the optimal inventory policy is of a base‐stock type and the optimal pricing policy is a list‐price policy with markdowns.     

Decentralized planning with an interdependent marketing-production system

An interdependent marketing-production planning model based on control theory is described. The interdependent model is a composition of the Vidale-Wolfe model relating advertising rates to sales rates, and the Holt, Modigliani, Muth & Simon (HMMS) production inventory planning model. An overall optimal marketing-production plan is identified using the interdependent model. This overall optimal plan (resulting from centralized planning) is then used as a reference point to measure the effectiveness of decentralized planning approaches. It is found that in some cases almost no coordination is necessary, in some cases the use of a transfer price leads to good decentralized planning, and in other cases centralized planning must be employed to achieve good results. Several examples are presented to illustrate the cases in which decentralized planning does and does not work well.