周期性需求下基于延迟订货的库存控制

本文在假设产品需求为周期性需求,订货周期为需求周期的整数倍,不能满足的需求在一定的时间间隔内允许缺货的前提下,提出一种确定性库存模型,并在此基础上,以满足单位时间内利润最大化为目标,研究并确定了优化订货批量、订货周期的时间间隔长度和缺货的时间长度的算法,最后给出算例,并验证了算法的可行性.     

基于库存价值改变和随机需求的经济订货批量模型

大多数库存研究的重点都集中在各种复杂的限制条件和模型的变换上.没有考虑到库存本身可能发生贬值或增值的情况,而实际中库存本身常常会发生价值变化.针对以往库存模型中没有考虑库存价值变化的问题,提出了在需求为随机连续分布、库存价值发生变化情况下的单周期经济订货批量模型,给出了最优订货策略.模型中以先进先出为假设条件,基于报童模型的思想,以订货量为决策变量、期望收益为目标函数,结合随机需求的分布情况得到最优订货量和最大期望收益,并给出了相应的数学证明.通过算例对模型进行说明,并对影响最优订货批量和最大期望收益的各个参数进行敏感性分析.     

嵌入Supply-hub的供应链联合生产与订货批量模型研究

考虑JIT环境下基于Supply-hub的供货模式,建立了制造商、Supply-hub和供应商的平均成本函数,提出了供应链分散决策和集中决策情形下的制造商和供应商的生产与订货批量模型。结果表明,制造商的生产批量在供应链分散决策时变化无规律,而在供应链集中决策时随着供应商与Supply-hub之间距离的增加而增加;相比分散决策,供应链集中决策时制造商的成本增加而供应商的成本减少,而整个供应链的总成本减少,供应商可以通过转移支付让制造商和供应商均受益,从而实现整个供应链的帕累托优化,随着供应商与Supply-hub之间的距离增加,协调能够让供应链得到更多的收益。     

采购风险期和零件订货批量之研究

采购风险期和订货批量是零件最优库存控制的两个关键因素.本文以博西威家电有限公司为例,通过设置采购风险期,有效地解决了采购提前期波动导致的缺货,辅之以设定经济订货批量,在确保连续生产的约束条件下控制库存量至最低水平.     

易逝品多销售阶段预防性横向调拨与订货决策模型北大核心CSCDCSSCI

针对易逝品零售商缺货时顾客不愿等待会造成缺货损失的问题,建立了两零售商的多销售阶段预防性横向调拨和订货模型。首先,通过该模型得到了构成预防性横向调拨策略的调入与调出阈值函数,并用以刻画一个零售商库存状态与该零售商剩余销售阶段及对方库存状态的关系。在此基础上,分析了两阈值函数的结构和性质,并证明了两零售商的订货纳什均衡的存在性。研究结果表明,每个零售商无需考虑对方的预防性调拨策略就可以通过事先制定的调入与调出阈值来控制自身库存。当调拨价格变动不大时,预防性横向调拨的存在使得两零售商可a以不对订货量进行调整。另外,订货与预防性横向调拨之间的策略性替代关系使得当调拨价格较低时,零售商倾向于增加订货量来控制库存;当调拨价格较高时,零售商倾向于使用预防性横向调拨控制库存。     

考虑消费者有限理性的体验式商品订购与库存配给策略

退货遗忘与缺货后悔是消费者在现实中普遍存在的有限理性行为。在考虑这两种有限理性行为以及销售商提供无理由退款承诺的基础上,构建了一个两期模型对体验式商品的订购与库存配给策略进行了分析。研究得到如下结论：首先,只有当产品价值的上界大于某个临界值时,消费者才会在两个销售期内购买产品,销售商应该采取库存配给策略以应对消费者的策略行为;其次,销售商的收益随着消费者满意概率、退货遗忘率和缺货后悔程度的增大而增大。数值分析表明：当消费者满意概率或产品满足率感知偏差较大时,消费者策略行为的负面影响在一定程度上得到减轻;虽然有限理性总是可以提高销售商的收益,但是退货遗忘与缺货后悔对销售商收益增长的影响作用是不同的。     

单件小批生产的接受订货决策与计划优化

本文以提高单件小批生产企业的经济效益为目标，应用系统科学理论和接受订货决策与计划优化的方法。此方法克服了过去在研究生产计划优化时，忽视市场因素变化的缺陷，通过接受订货初选－－预排计划－－订货决策－－计划优化这一循环，实现了生产与市场的衔接，使企业的生产计划真正成为可行的优化计划。     

随机需求下单向替代的两产品订货与定价联合决策研究

考虑了存在单向替代的两产品订货和定价联合决策问题。通过构建订货与定价联合决策模型,研究了单周期两产品的最优订货量和价格的存在条件,进一步将研究拓展到多周期的情况,建立了多周期的订货和定价联合决策模型,并进行了模型结构分析。同时,分析了信息对订货与定价联合决策的影响程度。     

产能和需求不确定情形下ATO系统最优库存和生产决策研究

通过将产成品的最终组装环节延迟到观察到实际需求以后进行,按订单组装(Assemble-to-Order,ATO)策略能够有效避免按库存生产(Make-to-Stock)策略下因为生产过剩而带来的损失和风险。然而,在未来产能有限的情形下,生产商必须提前组装部分产成品,以最大化自身收益。本文考虑一个未来需求和组装产能同时具有不确定性的单周期ATO系统:在观察到实际需求以前,生产商必须准备好所有的零部件库存,并组装适量的产成品;观察到实际需求和实际可用组装能力以后,则根据需要进一步追加产成品产量,以尽可能满足客户需求,从而最大化自身利益。通过构建一个两阶段规划模型,我们研究了生产商的最优零部件库存和最优生产决策;并进一步考察了生产商可以通过紧急外包来获取额外组装产能时的最优决策。研究结果为随机环境下生产商合理采购零部件库存和合理安排生产提供了有益的管理启示。     

基于业务规则的面向订单生产的计划管理模型研究

目前广泛采用的计划排产系统ERP和APS在计划柔性上仍存在一些问题,固化的约束分析模型很难适应企业多元化和易变的需求,尤其是企业业务规则的变化.为了适应这种变化,笔者引入规则引擎,建立了基于业务规则的MTO订单计划管理模型.该模型既保留了APS约束资源优化、层次性计划体系等优点,同时又克服了传统APS适应性不强、柔性不足的缺点.且易于实现和推广应用.     

Inventory Rationing in a Make‐to‐Stock System with Batch Production and Lost Sales

We address an inventory rationing problem in a lost sales make‐to‐stock (MTS) production system with batch ordering and multiple demand classes. Each production order contains a single batch of a fixed lot size and the processing time of each batch is random. Assuming that there is at most one order outstanding at any point in time, we first address the case with the general production time distribution. We show that the optimal order policy is characterized by a reorder point and the optimal rationing policy is characterized by time‐dependent rationing levels. We then approximate the production time distribution with a phase‐type distribution and show that the optimal policy can be characterized by a reorder point and state‐dependent rationing levels. Using the Erlang production time distribution, we generalize the model to a tandem MTS system in which there may be multiple outstanding orders. We introduce a state‐transformation approach to perform the structural analysis and show that both the reorder point and rationing levels are state dependent. We show the monotonicity of the optimal reorder point and rationing levels for the outstanding orders, and generate new theoretical and managerial insights from the research findings.     

Capacity rationing in rental systems with two customer classes and batch arrivals

Service differentiation is an emerging method to improve profit and to better serve high-priority customers. Such an approach has recently been introduced by one of Europe's leading rail cargo companies. Under this approach, customers can choose between classic and premium services. Premium service is priced above classic service and premium customers receive a service guarantee which classic customers do not receive. The company has to decide under which conditions it should ration its fleet capacity to classic customers in order to increase service of premium customers. We model such a situation as a batch-arrival queuing loss system. We describe the model, solve it optimally, and derive quantities of interest such as service probabilities. We further analyze it by performing numerical experiments based on the data from the company that motivated our research. We show that the potential of capacity rationing can be substantial in situations like the one we analyzed. We also derive conditions under which rationing is especially beneficial, such as under high unit fleet holding costs or in the presence of batch arrivals compared to single arrivals.     

Optimal Production and Admission Policies in Make‐to‐Stock/Make‐to‐Order Manufacturing Systems

In this article, we study optimal production and admission control policies in manufacturing systems that produce two types of products: one type consists of identical items that are produced to stock, while the other has varying features and is produced to order. The model is motivated by applications from various industries, in particular, the automobile industry, where a part supplier receives orders from both an original equipment manufacturer and the aftermarket. The product for the original equipment manufacturer is produced to stock, it has higher priority, and its demands are fully accepted. The aftermarket product is produced to order, and its demands can be either accepted or rejected. We characterize the optimal production and admission policies with a partial‐linear structure, and using computational analysis, we provide insights into the benefits of the new policies. We also investigate the impact of production capacity, cost structure, and demand structure on system performance.     

多类顾客环境下报童模型中库存分配策略研究

考虑一个报童模型中多类顾客的库存分配问题,将顾客按照他们愿意支付价格的高低划分为不同级别。零售商在销售期初决定产品订货量,并在销售期内决定接受或者拒绝不同顾客的需求,以最大化销售期内的期望总利润。将销售期分成大量足够小的时间单位,通过建立一个反向Bellman动态规划方程,以优化每个时间单位内的库存分配策略,并得到了零售商最优的期初订货量。通过与没有库存分配策略下零售商的期望利润进行比较,算例分析得出库存分配策略可以大幅提高零售商的利润。这主要是因为通过库存分配可以使得零售商从高端顾客中获取更多利润,同时能够减小期初的订货量,以节约采购成本和库存持有成本。     

A hybrid inventory management system responding to regular demand and surge demand

This paper proposes a hybrid policy for a stochastic inventory system facing regular demand and surge demand. The combination of two different demand patterns can be observed in many areas, such as healthcare inventory and humanitarian supply chain management. The surge demand has a lower arrival rate but higher demand volume per arrival. The solution approach proposed in this paper incorporates the level crossing method and mixed integer programming technique to optimize the hybrid inventory policy with both regular orders and emergency orders. The level crossing method is applied to obtain the equilibrium distributions of inventory levels under a given policy. The model is further transformed into a mixed integer program to identify an optimal hybrid policy. A sensitivity analysis is conducted to investigate the impact of parameters on the optimal inventory policy and minimum cost. Numerical results clearly show the benefit of using the proposed hybrid inventory model. The model and solution approach could help healthcare providers or humanitarian logistics providers in managing their emergency supplies in responding to surge demands.     

A demand classification scheme for spare part inventory model subject to stochastic demand and lead time

In this study, we aim to develop a demand classification methodology for classifying and controlling inventory spare parts subject to stochastic demand and lead time. Using real data, the developed models were tested and their performances were evaluated and compared. The results show that the Laplace model provided superior performance in terms of service level, fill rate (FR) and inventory cost. Compared with the current system based on normal distribution, the proposed Laplace model yielded significant savings and good results in terms of the service level and the FR. The Laplace and Gamma optimisation models resulted in savings of 82 and 81%, respectively.     

需求预测综合模型及其与库存决策的集成研究

准确的预测有助于企业做出有效的决策,包括生产计划、定价和促销决策等,以减少库存、提高客户满意度和企业竞争力． 以快速消费品为研究对象,分析其需求影响因素,建立了时间序列分析与多元回归整合的需求预测综合模型; 将此预测模型引入到库存决策中,构建了基于库存成本最小的需求预测与库存决策集成模型,并借助变邻域搜索算法获得模型的参数值; 最后,选用实际数据,验证了所构建的需求预测综合模型、物流需求预测与库存决策集成模型及其求解方法的有效性．     

Pricing and Replenishment of Competing Perishable Product Variants under Dynamic Demand Substitution

I consider pricing and ordering decisions faced by a retailer selling a perishable product with a two‐period shelf life over an infinite horizon. In the first period, the product is “new”; in the next, it becomes “old.” The new product is perceived by customers to have a higher quality than the old product. Every period, the retailer makes three decisions: prices for the new and old products and how much new product to order. I first show, with some simple cases, that demand uncertainty can make the sale of the old product profitable. I then consider a more realistic case with dynamic demand substitution among customers. I recognize that the retailer's decisions may be constant or may vary across different periods, under different contexts. For instance, varying the price of the new product can sometimes be difficult due to the negative impact it generates among customers. I find that (i) the benefit obtained from selling the old product with constant decisions is much higher than the benefit from allowing all the decisions to vary; (ii) the former benefit increases with a higher procurement cost, a higher quality of the new product, and higher demand volatility; however, the latter benefit is non‐monotone in these parameters; (iii) most of the latter benefit can be obtained by just changing the order quantity; and (iv) as the inventory of the old product increases, when all the decisions vary, the optimal price of the new product may increase or decrease.     

服务多类需求串行供应链的最优控制策略

研究由一系列生产环节采用串行方式组成多级供应链的最优控制策略.在此供应链中,原材料经过各级生产环节顺序加工形成最终产品.各级生产环节的加工时间服从随机分布.对最终产品存在多类随机需求.在每个时刻,管理者需要决定:1)是否该启动某个生产环节的生产;2)当有需求到达时,是否该满足此需求.管理者期望系统运行的总期望折扣成本最小.构造了该系统的马尔可夫决策模型并深入研究了其最优控制策略及其动态协同特性.在生产策略方面,证明系统的最优生产策略就是对各级生产环节采用动态的基本库存策略.该策略的动态协同特性主要体现在各级生产环节的最优基本库存水平受其他生产环节的库存水平影响.在产品分配方面,证明系统的最优分配策略是动态配给策略.该策略的动态协同特性主要体现在每类随机需求最优配给水平受各级生产环节的库存水平影响.     

Managing Production and Distribution for Supply Chains in the Processed Food Industry

We develop and evaluate a modeling approach for making periodic review production and distribution decisions for a supply chain in the processed food industry. The supply chain faces several factors, including multiple products, multiple warehouses, production constraints, high transportation costs, and limited storage at the production facility. This problem is motivated by the supply chain structure at Amy's Kitchen, one of the leading producers of natural and organic foods in the United States. We develop an enhanced myopic two‐stage approach for this problem. The first stage determines the production plan and uses a heuristic, and the second stage determines the warehouse allocation plan and uses a non‐linear optimization model. This two‐stage approach is repeated every period and incorporates look‐ahead features to improve its performance in future periods. We validate our model using actual data from one factory at Amy's Kitchen and compare the performance of our model to that of the actual operation. We find that our model significantly reduces both inventory levels and stockouts relative to those of the actual operation. In addition, we identify a lower bound on the total costs for all feasible solutions to the problem and measure the effectiveness of our model against this lower bound. We perform sensitivity analysis on some key parameters and assumptions of our modeling approach.