A double-sided multiunit combinatorial auction for substitutes: Theory and algorithms

Combinatorial exchanges have existed for a long time in securities markets. In these auctions buyers and sellers can place orders on combinations, or bundles of different securities. These orders are conjunctive: they are matched only if the full bundle is available. On business-to-business (B2B) exchanges, buyers have the choice to receive the same product with different attributes; for instance the same product can be produced by different sellers. A buyer indicates his preference by submitting a disjunctive order, where he specifies the quantity he wants of each particular good and what limit price he is willing to pay for each good, thus providing a subjective valuation of each attribute. Only the goods with the best prices will be traded. This article considers a doubled-sided multiunit combinatorial auction for substitutes, that is, a uniform price auction where buyers and sellers place both types of orders, conjunctive (AND orders) and disjunctive (XOR orders). We show that linear competitive prices exist. We also propose an algorithm to clear the market, which is particularly efficient when the number of traders is large, and the goods are divisible.     

An integrated inventory model with quality improvement and two-part credit policy

Trade credit financing plays a vital role in current business operations. Vendors extend payment dates to encourage sales, and buyers are not required to pay immediately after receiving products. This is equivalent to receiving a price reduction. Besides, buyers reduce their need for capital from bank loans. In addition, a number of defective products are produced during the production process. The number of defective items influences the on-hand inventory levels of buyers, service levels, and frequency of orders. To ensure that the analysis incorporates a realistic production environment, we developed an integrated inventory model with a two-part trade credit and considered an imperfect production process that can be improved by capital investment. The objective was to determine the optimal ordering, shipping, and quality improvement policies to maximize joint total profit. An iterative algorithm was established to determine the optimal strategy. Furthermore, a sensitivity analysis was conducted to examine the effects of changing parameter values on the optimal solution.     

Optimal order lot sizing and pricing with free shipping

Companies, especially those in e-business, are increasingly offering free shipping to buyers whose order sizes exceed the free shipping quantity. In this paper, given that the supplier offers free shipping, we determine the retailer’s optimal order lot size and the optimal retail price. We explicitly incorporate the supplier’s quantity discount, and transportation cost into the model. We analytically and numerically examine the impacts of free shipping, quantity discount and transportation cost on the retailer’s optimal lot sizing and pricing decisions. We find that free shipping can benefit the supplier, the retailer, and the end customers, and can effectively encourage the retailer to order more of the good, to the extent of ordering a few times of the optimal order lot size without free shipping. The order lot size will increase and the retail price will decrease if the supplier offers proper free shipping.     

供应中断下具有随机需求的闭环供应链最优差别定价模型

研究了在供应中断下具有随机需求的闭环供应链系统的最优差别定价模型.基于博弈论的理论和方法分别在集中式和分散式决策情形下,确定了最优批发价、最优销售价、最优订购量及系统利润.最后通过数值例子对最优差别定价模型进行了实证分析.     

模糊环境下美式看跌期权的定价研究

应用模糊集理论将无风险利率和波动率进行模糊化,以梯形模糊数替代精确值,将美式期权的定价模型扩展到美式期权模糊定价模型.得到了模糊风险中性概率表达式,并在此概率测度下推导出多期二叉树模糊定价模型,以及二叉树上各节点以梯形模糊数表示的模糊期权价值,以数值模拟演示了美式看跌期权的模糊定价过程.最后分析了不同风险偏好投资者在不确定环境下的套利决策行为,结果表明风险偏好大的投资者具有较高的置信水平、较小的主观模糊期权价格以及较大的无风险套利区间.     

The effect of supply uncertainty in price-setting newsvendor models

We consider a price-setting newsvendor model in which a firm needs to make joint inventory and pricing decisions before the selling season. The supply process is uncertain such that the received quantity is the product of the order quantity and a random yield rate. Two cost structures are investigated, the in-house production case in which the firm pays for the input quantity and the procurement case in which the firm pays for the quantity received only. Our objective is to investigate the effect of yield randomness on optimal decisions and expected profit. By using the theory of stochastic comparisons, we find that under both cost structures, a less variable yield rate leads to a lower optimal price and a higher expected profit. Moreover, we show that in the in-house production case, a stochastically larger yield rate also results in a lower optimal price and a higher profit, but this is not true in the procurement case. Examples show that the effect of supply uncertainty on optimal order quantity is not universal.     

Fuzzy profit measures for a fuzzy economic order quantity model

Changing economic conditions make the selling price and demand quantity more and more uncertain in the market. The conventional inventory models determine the selling price and order quantity for a retailer’s maximal profit with exactly known parameters. This paper develops a solution method to derive the fuzzy profit of the inventory model when the demand quantity and unit cost are fuzzy numbers. Since the parameters contained in the inventory model are fuzzy, the profit value calculated from the model should be fuzzy as well. Based on the extension principle, the fuzzy inventory problem is transformed into a pair of two-level mathematical programs to derive the upper bound and lower bound of the fuzzy profit at possibility level α. According to the duality theorem of geometric programming, the pair of two-level mathematical programs is transformed into a pair of conventional geometric programs to solve. By enumerating different α values, the upper bound and lower bound of the fuzzy profit are collected to approximate the membership function. Since the profit of the inventory problem is expressed by the membership function rather than by a crisp value, more information is provided for making decisions.     

Integrated inventory model with quantity discount and price-sensitive demand

Quantity discount has been a subject of study for a long time; however, little is known about its effect on integrated inventory models when price-sensitive demand is placed. The objective of this study is to find the optimal pricing and ordering strategies for an integrated inventory system when a quantity discount policy is applied. The pricing strategy discussed here is one in which the vendor offers a quantity discount to the buyer. Then, the buyer will adjust his retail price based on the purchasing cost, which will influence the customer demand as a result. Consequently, an integrated inventory model is established to find the optimal solutions for order quantity, retail price, and the number of shipments from vendor to buyer in one production run, so that the joint total profit incurred has the maximum value. Also, numerical examples and a sensitivity analysis are given to illustrate the results of the model.     

一般均衡下基于产品市场和资本市场的单名CDS定价

次贷危机呼吁新的信用衍生品定价模型, 因此为存在产品市场和资本市场的经济结构建立一般均衡的单名CDS定价模型, 使用最优化求解一般均衡下的商品价格和CDS价格. 可以发现一般均衡的CDS定价具有资本市场和产品市场的因素, 这表示CDS的价格不再是由单纯的资本市场因素决定的, 而是由无风险利率、资本产出弹性、违约率、回收率同时决定的. 通过数量约束用模拟的方式研究多个均衡的动态变化, 发现违约风险的增加使得价格剧烈波动且市场交易萎缩. 在为以中国工商银行为参考资产的CDS定价过程中, 发现各种因素在不同的时期都可能成为定价的主要影响因素. 可以发现, 次贷危机的定价体系存在着信用调整问题和定价与实体经济脱节的问题. 可以认为, 一般均衡下基于产品市场和资本市场的单名CDS定价可以囊括多个市场的交叉影响, 为衍生品定价提供一个新的方向.     

Particle swarm optimization for bi-level pricing problems in supply chains

With rapid technological innovation and strong competition in hi-tech industries such as computer and communication organizations, the upstream component price and the downstream product cost usually decline significantly with time. As a result, an effective pricing supply chain model is very important. This paper first establishes two bi-level pricing models for pricing problems with the buyer and the vendor in a supply chain designated as the leader and the follower, respectively. A particle swarm optimization (PSO) based algorithm is developed to solve problems defined by these bi-level pricing models. Experiments illustrate that this PSO based algorithm can achieve a profit increase for buyers or vendors if they are treated as the leaders under some situations, compared with the existing methods.     

Responsive pricing under supply uncertainty

Consider a retailer orders a seasonal product from a supplier and sells the product over a selling season. While the product demand is known to be a linear function of price, the supply yield is uncertain and is distributed according to a general discrete probability distribution. This paper presents a two-stage stochastic model for analyzing two pricing policies: No Responsive Pricing and Responsive Pricing. Under the No Responsive Pricing policy, the retailer would determine the order quantity and the retail price before the supply yield is realized. Under the Responsive Pricing policy, the retailer would specify the order quantity first and then decide on the retail price after observing the realized supply yield. Therefore, the Responsive Pricing policy enables the retailer to use pricing as a response mechanism for managing uncertain supply. Our analysis suggests that the retailer would always obtain a higher expected profit under the Responsive Pricing policy. In addition to examining the impact of yield distribution and system parameters on the optimal order quantities, retail prices, and profits under these two pricing policies, we analyze two issues arising from responsive pricing. The first issue deals with the case in which the retailer can place an emergency order with an alternative source after observing the realized yield, while the second issue deals with a situation in which the retailer has to allocate his order among multiple suppliers.     

A committed delivery strategy with fixed frequency and quantity

We analyze a supply chain environment in which a distributor facing price-sensitive demand has the opportunity to contractually commit to a delivery quantity at regular intervals over a finite horizon in exchange for a per-unit cost reduction for units acquired via committed delivery. Supplemental orders needed to meet demand are purchased at an additional unit cost. For normally distributed demand, we use a simulation-based approximation to develop models yielding closed-form solutions for the optimal order quantity and resell price for the distributor. Inventory, ordering and pricing implications for this “committed delivery strategy” are investigated.     

混合分数布朗运动下欧式期权模糊定价研究

本文采用混合分数布朗运动来刻画标的股票价格的动态变化,以此体现金融市场的长记忆性特征。在混合分数Black-Scholes模型的基础上,基于标的股票价格、无风险利率和波动率均是模糊数的假定下,构建了欧式期权模糊定价模型。其次,分析了金融市场长记忆性的度量指标Hurst指数H对欧式期权模糊定价模型的影响。最后,数值实验表明：考虑长记忆性特征得到的欧式期权模糊定价模型更符合实际。     

基于腐损程度的季节性产品动态定价与订单量的集成优化

为了对易腐季节性产品的销售价格和订单量进行最优决策,考虑产品在不同腐损程度的情形下,需求与价格和时间同时相关的一类季节性产品的动态定价和订单量的集成优化问题.建立该类产品的价格制订次数、每次制订的价格和订单量的集成优化模型,并对模型进行求解,最后结合数例验证模型的实用性和可操作性,并分析产品腐损程度对价格制订次数、价格大小、订单量和利润的影响.结果表明,随着产品腐损程度的提高,零售商在销售季节内的产品价格最优制订次数保持不变;零售商在销售季节内所制订的最优价格逐渐微降;产品的最优订单量和所产生的最优利润逐渐微升.     

A fuzzy EOQ model with demand-dependent unit cost under limited storage capacity

A fuzzy EOQ model is developed with limited storage capacity where demand is related to the unit price and the setup cost varies with the quantity produced/purchased. Here fuzziness is introduced in both objective function and storage area. It is solved by both fuzzy nonlinear and geometric programming techniques for linear membership functions. The model is illustrated with a numerical example and a sensivity analysis is made. Generalisation to a multi-item problem is also presented and its numerical results are compared with those of the crisp model.     

Optimal inventory policies for profit maximizing EOQ models under various cost functions

In this paper, we establish and analyze three EOQ based inventory models under profit maximization via geometric programming (GP) techniques. Through GP, we find optimal order quantity and price for each of these models considering production (lot sizing) as well as marketing (pricing) decisions. We also investigate the effects on the changes in the optimal solutions when different parameters are changed. In addition, a comparative analysis between the profit maximization models is conducted. By investigating the error in the optimal price, order quantity, and profit of these models, several interesting economic implications and insights can be observed.     

基于产品生命周期的闭环供应链定价和协调策略研究

研究产品生命周期条件下一个制造商、一个零售商组成的闭环供应链的定价和协调策略:制造商第一周期只生产新品,从第二周期开始生产再制造品,并负责回收。建立了两周期、多周期和无限周期下的分散决策模型和多周期下的集中决策模型,得到最优定价和生产策略。结果表明:两周期中,制造商和零售商应根据不同的成本节约额制定不同的批发价、零售价和生产量。多周期中,当成本节约额比较小时,除第一和最后一个周期外,制造商和零售商应采取相同的定价和生产策略;当成本节约额比较大时,除第一和最后两个周期外,制造商和零售商应采取相同的定价和生产策略。无限周期中,除第一周期以外,制造商和零售商都应该采取相同的定价和生产策略。在有限周期情况下,制造商和零售商应通过在前期制定比较低的批发价和零售价,以提高生产量和销售量,使得后期的回收量增大,达到降低成本的目的。通过引入收益共享契约制定合适的批发价和销售收入及再制造收益共享比率可以协调整个闭环供应链。算例验证了上述结论。     

A fractional programming approach for retail category price optimization

We present a new mixed-integer programming (MIP) approach to study certain retail category pricing problems that arise in practice. The motivation for this research arises from the need to design innovative analytic retail optimization techniques at Oracle Corporation to not only predict the empirical effect of price changes on the overall sales and revenue of a category, but also to prescribe optimal dynamic pricing recommendations across a category or demand group. A multinomial logit nonlinear optimization model is developed, which is recast as a discrete, nonlinear fractional program (DNFP). The DNFP model employs a bi-level, predictive modeling framework to manage the empirical effects of price elasticity and competition on sales and revenue, and to maximize the gross-margin of the demand group, while satisfying certain practical side-constraints. This model is then transformed by using the Reformulation–Linearization Technique in tandem with a sequential bound-tightening scheme to recover an MIP formulation having a relatively tight underlying linear programming relaxation, which can be effectively solved by any commercial optimization software package. We present sample computational results using randomly generated instances of DNFP having different constraint settings and price range restrictions that are representative of common business requirements, and analyze the empirical effects of certain key modeling parameters. Our results indicate that the proposed retail price optimization methodology can be effectively deployed within practical retail category management applications for solving DNFP instances that typically occur in practice.     

A decision support system for order acceptance/rejection in hybrid MTS/MTO production systems

In this paper, we present a novel decision support system for order acceptance/rejection in a hybrid Make-to-Stock/Make-to-Order production environment. The proposed decision support system is comprised of five steps. At the first step, the customers are prioritized based on a fuzzy TOPSIS method. Rough-cut capacity and rough-cut inventory are calculated in the second step and in case of unavailability in capacity and materials, some undesirable orders are rejected. Also, proper decisions are made about non-rejected orders. At the next step, prices and delivery dates of the non-rejected orders are determined by running a mixed-integer mathematical programming model. At the fourth step, a set of guidelines are proposed to help the organization negotiate over price and due date with the customers. In the next step, if the customer accepts the offered price and delivery date, the order is accepted and later considered in the production schedule of the shop floor, otherwise the order is rejected. Finally, numerical experiments are conducted to show the tractability of the applied mathematical programming model.     

Multi-item inventory models with price dependent demand under flexibility and reliability consideration and imprecise space constraint: A geometric programming approach

In this paper, multi-item economic production quantity (EPQ) models with selling price dependent demand, infinite production rate, stock dependent unit production and holding costs are considered. Flexibility and reliability consideration are introduced in the production process. The models are developed under two fuzzy environments–one with fuzzy goal and fuzzy restrictions on storage area and the other with unit cost as fuzzy and possibility–necessity restrictions on storage space. The objective goal and constraint goal are defined by membership functions and the presence of fuzzy parameters in the objective function is dealt with fuzzy possibility/necessity measures. The models are formed as maximization problems. The first one—the fuzzy goal programming problem is solved using Fuzzy Additive Goal Programming (FAGP) and Modified Geometric Programming (MGP) methods. The second model with fuzzy possibility/necessity measures is solved by Geometric Programming (GP) method. The models are illustrated through numerical examples. The sensitivity analyses of the profit function due to different measures of possibility and necessity are performed and presented graphically.