价格不确定情形的原材料滚动采购策略模型

针对价格不确定情形的原材料采购问题,描述了原材料滚动采购策略的基本思想,在此基础上,针对原材料采购计划周期的每个采购时刻点,在未来多期原材料价格预测结果的基础上,综合考虑原材料成本、一次性采购费用和库存费用,构建了使采购综合成本最小的多阶段最优采购模型,并通过模型求解可确定当期的原材料最优采购量。通过B公司原材料镍采购的实例分析说明了给出采购策略模型的可行性和有效性。     

考虑随机需求的供应链合作广告与存货策略

基于单一制造商与单一零售商所组成的渠道结构,放宽了市场需求为确定性的假设,引入了存货因子这一决策变量,先后考察了Stackelberg主从博弈和Nash合作博弈时制造商与零售商的最优广告策略及零售商的最优存货策略,比较了不同博弈结构下的均衡策略及相应期望利润,并利用Nash讨价还价模型对合作利润进行了分配.通过一个数学算例验证了有关结论.     

价格增长下两阶段存货影响销售率EOQ模型

考虑产品采购价随时间指数增长和两阶段存货影响销售,建立了一个价格增长和两阶段存货影响销售率EOQ模型。采用实际调查数据对模型进行了验算,分析了农产品和食品价格大幅度上涨对经销商订货策略的影响。研究结果表明：初始采购价和采购价增长因子对订购策略和各项成本影响较大,存货影响销售率因子和存货影响销售率临界点对订购策略和各项成本有一定影响。     

基于离散时间的短生命周期产品动态定价

研究了零售商销售短生命周期产品时的最优定价策略。零售商试图在一个有限计划期内将固定的存货全部售出,剩余存货在销售季节结束时价值变为零。提出了一个以顾客到达为泊松过程的连续时间最优定价模型。考虑到连续最优定价策略在实际中难以执行,提出了一种简化的离散时间最优定价策略。该策略允许零售商按预定的间隔周期调整价格,与实际生活中时装类商品零售商定期降价做法非常相似。分析了零售商最优初始存货水平决策问题,该问题可通过前面的连续时间模型较容易求解。     

回购与缺货成本下的连续时间报童模型

对带回购策略与缺货成本的连续时间报童模型,研究了其最优订购策略与批发价策略,使得生产商和零售商期望收益最大化。在零售价格依赖需求过程与零售价格外生的两种情形下,讨论了带回购策略和缺货成本对连续时间报童均衡策略的共同影响。运用随机最大值原理研究了以上问题均衡策略的存在唯一性,讨论了均衡策略满足的条件,在价格外生情形下得出结论：回购策略与缺货成本的共同作用,使得零售商的最优订购量和供应商的最优批发价格均提高很多。最后,通过数值算例做了最优批发价格与最优订购策略的敏感度分析,具体例子验证了已得结论。     

资金约束下的双物料库存决策优化

通过构建库存优化模型,提出一个基于供应商资金短缺前提下采购两种原材料的最优采购策略。首先利用库存水平和初始资金联合决策求解模型,得到在某一特定库存水平之内只需采购易腐原材料,在该库存水平之外需采购两种原材料的采购策略,且两种情况均考虑资金约束情形。然后在拓展中将单周期库存融资决策延伸到多周期动态库存决策。最后运用控制变量法解决决策过程中涉及到的各种成本参数和采购策略的关系。通过算例验证模型和结论的合理性。     

电力物资复合泊松需求下的最优订货量

以上海电力公司的物资供应体系为背景,给出了（T,Q）库存策略下服从复合泊松分布的电力物资最优订货量的算法。建立复合泊松分布下的库存成本模型,对此模型用闭式算法求得其最小库存成本下的最优订货量,将该算法应用于电力物资进行实证分析。数值结果表明,与传统的报童模型相比,本文提出的最优订货量算法更适用于间断需求。     

Globe Metallurgical准备关闭阿拉巴马州的硅厂

美国铁合金厂商Globe Metallurgical准备关闭其阿拉巴马州Selma硅厂，这是因为原材料成本特别是能源成本的飙升造成的。     

基于经评审的最低投标价法下的投标报价博弈模型

本文在研究经评审的最低投标价法的基础上,运用博弈模型,从投标方的角度分析其报价策略来建立多人博弈模型,通过模拟其他投标人的成本分布函数,在实现其自身利益最大且中标的可能性最大的条件下,给出了在成本函数下的最优投标报价策略,得到投标报价的经验公式,从而得到投标人的最优报价。     

电子商务环境下两级供应链库存管理决策研究

本文利用order up to level策略建立需求不确定时两级供应链供应商的库存策略模型并进行参数求解,给出最优库存水平以及管理绩效考核指标的求解方法。并通过实例计算,给出电子商务环境下基于两级供应链的供应商库存最优解决方案,提出随着零售商需求波动程度、库存水准的变动,对于供应商最优库存水平、库存成本、缺货成本的相应策略。     

An integrated robust replenishment/production/distribution policy under inventory inaccuracy

In this paper, a centralised production/distribution system is studied, in which the manufacturer controls the processes of raw material ordering, production and final goods distribution. The inventory records of raw materials, work-in-process, final goods in the manufacturer and the retailers are inaccurate. The objective of this research is to develop an integrated policy that can hedge against the negative impact of inventory inaccuracy at all stages of a supply chain. To achieve this aim, a backward recursive integrated robust policy is constructed. First, the time, quantity and vehicle route of the next distribution are forecasted by a distribution sub-policy; then the forecasted distribution time and quantity are used as the objective of the production process control, which is implemented by a production control sub-policy. Raw materials are ordered by a replenishment sub-policy according to the forecasted raw materials consumption in the production process. Numerical experiments are conducted to verify the feasibility and robustness of the proposed policy, which shows that besides of deploying radio frequency identification (RFID) devices in inventory management systems for raw materials, WIPs, final goods of the manufacturer and the retailers, integrated robust policies also can be employed to hedge against the impact of inventory inaccuracy.     

Production and inventory policies for an integrated manufacturing firm

The coordination of production and inventory decisions for raw materials, intermediate products and finished goods is important. We compare some decision rules for a ‘firm that uses a single raw material to manufacture n different parts which are then assembled into the final product. Demand for the final product is probabilistic and there are costs of holding inventory and of ordering, manufacture and assembly. These characteristics are an idealization of the problem faced by a manufacturer of scaffolds in which the major processes are the manufacture of different sheet metal parts and their assembly.

We develop production rules that use system-wide information. We use simulation to evaluate the performance of these rules and compare system behaviour to that under production rules that are derived separately for each item and use only local information about that item's status.     

Quick response procurement cost control strategy for fabric manufacturing

This paper considers a material management decision making problem with information revision of a fabric manufacturer, facing highly uncertain material supply and quick response demand. We construct a model by analysing the decision process, derive the optimal solution and study the interaction among factors through a practical data based computational simulation. The demand information, in terms of estimated volume, modifiable order, and order confirmation respectively, is provided by the final buyer to the fabric manufacturer in consecutive time periods. We investigate normal and urgent raw material (yarn) purchasing costs, holding cost, overstock salvage cost, as well as availability of raw materials for urgent order. The model for a single item is then extended to that for multiple items with the capacity constraint. The research shows that a fabric manufacturer plays a critical role in operations efficiency and overall cost control in a typical apparel supply chain, and reveals the cost trade offs between purchasing and inventory under this special business environment.     

Optimal replenishment cycle for perishable items facing demand uncertainty in a two-echelon inventory system

We consider a two-echelon supply chain with an upstream manufacturer and a downstream retailer for a single perishable product. The manufacturer processes raw materials into finished products, which are purchased by the retailer in each replenishment cycle. The raw materials of the manufacturer are highly perishable (i.e. perishing within hours or days), and the finished goods at the retailer face demand uncertainty and obsolescence. We model the manufacturer–retailer relationship as a Stackelberg game where the retailer is the leader and decides the replenishment cycle that minimises its mismatch cost between supply and uncertain demand. The manufacturer is the follower and decides its processing rate to minimise its unit cost for finished goods. Our results show that the raw material and finished goods lifetimes, which are interrelated through the duration of the replenishment cycle, have a significant impact on supply chain costs. Although raw material spoilage cost by itself is low, we show that short raw material lifetimes have a significant impact on the costs of both parties. Additionally, we find that while high manufacturer markups increase retailer costs, they reduce the manufacturer's costs due to large production batches.     

Operations planning in a supply chain system with fixed-interval deliveries of finished goods to multiple customers

Raw material ordering policy and the manufacturing batch size for fixed-interval deliveries of finished goods to multiple customers play a significant role in economically managing the supply chain logistics. This paper develops an ordering policy for raw materials and determines an economic batch size for a product at a manufacturing center which supplies finished products to multiple customers, with a fixed-quantity at a fixed time-interval to each of the customers. In this model, an optimal multi-ordering policy for procurement of raw materials for a single manufacturing system is developed to minimize the total cost incurred due to raw materials and finished goods inventories. The carried over inventory of finished goods from the previous cycle is used as initial finished goods inventory, resulting in shifting the production schedule ahead for the next cycle. A closed-form solution to the problem is obtained for the minimal total cost. The algorithm is demonstrated for multiple customer systems.     

A postponement model to determine the customisation degree applied to the notebook computer industry

Facing an increasingly intense and competitive environment, the information industry must design a global service chain for its self-development and also to make its global development more efficient. The application of the postponement concept has become an efficient method to help enterprises reach this goal. With the aim of building and analysing a postponement structure from the viewpoint of added value and demand uncertainty, this study constructed a multi-objective postponement model. The model was then analysed, using three quantifying objects: the overall cost, product types and the average assembly time; and two decision variables: the variety of parts and inventory quantity. In an indeterminate function, both the application demanding frequency and demanded quantity submit to a Poisson distribution and Normal distribution. Considering the profit model, the manufacturer can decide on the most suitable degree of customisation according to market situations, suppliers at the time of production and most important, the manufacturer's profit. This study uses a notebook computer manufacturer as an example to carry out empirical analysis. The manufacturer was permitted to decide the optimal product types depending on its financial status. Postponement techniques were then provided as a reference to the manufacturer to help it maximise profits.     

Configuring a manufacturing firm's supply network with multiple suppliers

Consider a supply network consisting of a manufacturer and its suppliers. The manufacturer produces different types of products, using a common set of inputs (e.g., raw materials and/or component parts) from the suppliers: but, each product needs a different mix of these inputs. The manufacturer sells its finished products to the market at the end of the current decision horizon, facing an uncertain market demand. In situations where a manufacturer has outsourced its parts production to contract manufacturers, the contract manufacturer's capacity available for the given manufacturer in a particular time period may be limited by “capacity reservation” agreements made in advance. Thus, in making production mix decisions for the current planning horizon, the manufacturer has to take into account both its own and the component suppliers' capacity restrictions. We develop a mathematical model and an iterative algorithm that helps the manufacturer solve its supply configuration problem, that is, how much of each raw material and/or component part to order from which supplier, given capacity limits of suppliers as well as the manufacturer. The model takes into account such factors as market demand uncertainty, costs and product characteristics. We present an numerical example to illustrate the interacting effects among critical parameters in the model, and apply the model to a real-world case of a computer manufacturer.     

Two-echelon spare parts inventory system subject to a service constraint

In this paper, we consider a spare parts inventory problem faced by a manufacturer of electronic machines with expensive parts that are located at various customer locations. The parts fail infrequently according to a Poisson process. To serve customers when a failure occurs, the manufacturer operates a central warehouse and many field depots that stock spare parts. The central warehouse acts as a repair facility and replenishes stock at the field depots. There is a centralized decision-maker who manages the inventory in both the central warehouse and the field depots. We develop a continuous review, base stock policy for this two-echelon, multi-item spare parts inventory system. We formulate a model to minimize the system-wide inventory cost subject to a response time constraint at each field depot. We present an efficient heuristic algorithm and study its computational effectiveness.     

A manufacturer’s production and pricing strategies with a salvage channel

We study a manufacturer’s production quantity and pricing decisions when the manufacturer has an opportunity to sell surplus inventory through a salvage channel. Before sales begin, the manufacturer determines the production quantity without knowing customer demand. After demand is realised, the manufacturer first satisfies the demand through primary channel while charging a fixed price or adjusting price to maximise profit. At the end of the selling season, the manufacturer resells surplus inventory through the salvage channel, which can be either integrated with or independent of the manufacturer. The manufacturer’s optimal production quantity and expected profit are investigated under different salvage channel structures. We show that the salvage channel improves the manufacturer’s profitability as well as the availability of the product to potential customers through both primary and salvage channels.     

An inventory model with emergency orders under explicit energy cost considerations

The inventory model in this paper is targeted to production systems with constant production rates but underlying possibilities for undesirable circumstances to threaten the production schedule. The inventory policy proposed explicitly considers energy cost when determining optimal size for order quantity, safety stock and inventory cycle length such that the total expected cost per unit time is minimised. The results are compared to a traditional inventory policy that does not consider the direct impact of energy cost. An analysis of the model reveals three production environment characteristics in which inventory policies are most significantly affected by changes in energy cost: heavy product weight, high regular product demand or high emergency product demand. If any one of the three key factors increases, then changes of the inventory decisions or related logistics costs become more significant. The cost effectiveness of implementing the proposed inventory policy also becomes more significant as any one of the three key factors increase with respect to energy cost.