易逝品的动态定价机制与消费者策略行为研究
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摘要
科技的进步和市场竞争的加剧导致产品更新换代的加快,使得越来越多的产品具有易逝品的典型特征。易逝品通常具有较短的生命周期,到一个时点后若还未被出售,则该产品残值很小。同时,像牛奶、面包、蔬菜等这些易逝品,在临近产品生命周期时,消费者的感知风险会明显的增高,将会进一步导致产品滞销。因此,产品的易逝性是目前众多销售商在管理这类物品时所面临的巨大困难。
动态定价作为易逝品收入管理的引擎,在易逝品的收益管理过程中发挥着重要的作用。而且互联网的出现导致信息的传播更为便捷,厂商调整价格的菜单成本大大降低,消费者和厂商的互动性也得到了显著加强。这些都为易逝品动态定价的实施提供了更为有效的环境和保障。与此同时,消费者也可以通过互联网更容易的获得产品的价格、供应数量等市场信息而变得越来越“聪明”,他们会权衡自己在每阶段可能得到的不同利益,而决定最佳的购买时机。
因此,网络环境下动态定价的实施犹如一把“双刃剑”,在增加厂商利润的同时,也不可避免地导致消费者策略行为的发生,这种策略行为会对厂商的动态定价以及相关决策产生重大的影响。因此,考虑厂商如何利用新型的网络技术,以及将消费者的策略行为纳入到价格以及订购数量的决策范畴,实现易逝品的收益管理的最大化,无论在理论界还是实物界,都显得十分必要。
本研究在以前研究的基础上,分别将网络环境下出现的新技术(RFID)以及消费者的策略行为进一步考虑到易逝品的动态定价与订购决策中,综合应用动态规划、博弈论、经济学、计量经济学、试验设计等理论和方法,对上述问题进行了探索,并得到以下结论:
(1)销售商基于RFID技术的应用,可以在对产品价值跟踪的基础上,动态的调整价格,并决定最优的订购数量。与实行固定的价格策略相比,实行动态定价策略总能获得较大的收益,而且这种优势随着易逝品价值递减速率的增加而更为显著。销售商基于RFID技术的最优价格路径和初始的最优订购数量受到产品的成本、产品价值的递减速率、消费者保留价值的分布等因素的影响。
(2)消费者的策略行为会对厂商定价决策产生较大的影响。在确定性需求下,当产品没有数量限制时,厂商的利润随着策略型消费者所占比例的增加而减少,厂商忽略消费者策略行为所造成的损失随着策略型消费者所占比例的增加而增大;在有数量限制时,厂商的定价决策与产品的数量水平显著相关:
当厂商的数量较少时,厂商基本上可以忽略消费者的策略行为;当厂商有相对较多数量时,消费者会对第二阶段以较低价格获得产品的概率形成理性预期,厂商的最优决策是在第一阶段设定较高的价格,在第二阶段设定相对较低的价格,在理性预期均衡的条件下实现总利润最大化;当厂商有充足的数量时,厂商的最优定价决策与消费者的折扣因子有关,第一阶段的价格随着折扣因子的增大而减小,面临消费者的等待行为,厂商应在第二阶段设定适当的价格,尽可能使等待购买的消费者人数最少。
在不确定需求情形下,厂商的定价决策也受到产品数量和消费者保留价格的显著影响:厂商第一阶段的价格随着产品数量的增加而下降。同时,产品数量存在一个临界水平,当该临界水平大于具有高保留价格的消费者的人数时,厂商应该实行动态定价策略;而该临界水平小于具有高保留价格的消费者的人数时,厂商应该实行差价返还策略,以减少消费者的策略行为。
(3)消费者的策略行为会对厂商订购策略产生较大的影响。无论是确定性需求还是不确定需求,厂商的最优订购数量与消费者的价值递减速率、消费者的到达速率、消费者的保留价格以及厂商的折扣水平、厂商打折的时间等众多因素有关。与传统的报童模型相比,当消费者的保留价格较高时,厂商可以适当的减少初始的订购数量,通过制造一定的“缺货”风险,使得具有较高的保留价格的消费者在第一阶段以较高的价格购买,而不会等到第二阶段以较低的价格购买;当消费者的保留价格较低时,厂商可以忽视消费者的策略行为。
同时,消费者的风险厌恶程度对厂商的最优订购数量有一定的影响。消费者的风险厌恶程度越高,厂商的最优订购数量也相应较大。消费者的风险厌恶从一定程度上缓解了消费者的策略行为所带来的不利影响。
(4)面对消费者的策略行为,同时考虑价格和订购数量将会导致厂商收益的显著提高。在消费者同质情形,消费者的策略行为使得厂商的价格低于产品对消费者的保留价值,厂商无法实现完全价格歧视;最优数量也比不考虑这种策略行为时低。通过福利经济学的分析可以得到,与传统的报童模型相比,考虑消费者的策略行为,将会导致了生产者、消费者、社会总福利的显著提高。
在消费者异质的情形,通过机制设计的相关理论,得到厂商在需求不确定时,必须至少拥有恰当的数量使得那些具有高保留价格的消费者不管在何种需求情况下(高需求或者低需求),都能买到该产品。如果需求数量很大时,厂商可以制造一定的缺货风险,使得高保留价格的消费者在第一阶段购买;如果需求数量很小时,厂商会兼顾两阶段的消费者需求。同时,在垄断竞争的情形,存在一个对称的Nash均衡。
(5)通过对消费者策略行为的实证分析,得到消费者会基于厂商提供的价格和数量等产品信息,动态的优化自己的购买决策。当厂商产品的数量较少时,消费者价格敏感性会大大降低,消费者几乎不会等待厂商进一步降价,而马上购买。对厂商而言,可以通过结合目前的市场需求情况和产品的数量,策略性的发布产品的相关信息,以影响消费者的购买决策,从而获得最大利润。
通过以上方面的分析和讨论,本文的主要创新点可以归纳为以下三个方面:
(1)本文运用数学模型,建立易逝性产品的价值与需求之间的函数,分析了在RFID技术条件下,基于产品价值跟踪基础上动态定价的优越性,并给出如何应用RFID技术,得到最优的价格与初始的订购数量的决策。
(2)本文分析了在消费者策略行为下,易逝品的价格、订购数量以及同时将价格和订购数量作为决策变量时的最优决策,具体而言:
考虑易逝品的价格策略时,通过引入消费者折扣因子和理性预期,运用两阶段的动态博弈,分别在确定性需求和不确定需求下,得到厂商的最优价格策略与产品的初始数量显著相关,并提出在适当情形下可以运用差价返还策略来减少消费者策略行为的不利影响;考虑易逝品的订购策略时,运用传统的报童模型和理性预期均衡,得到厂商在确定性需求和不确定需求下的最优订购数量,发现相对于传统的报童模型,厂商在面临消费者策略行为下的最优订购数量要比传统的报童模型所得出的订购数量低;考虑易逝品的价格和订购策略相结合时,分别在消费者同质和消费者者异质的情形下,得到厂商的最优价格和初始的订购决策,同时分析了消费者的风险类型对厂商策略的影响,并通过经济学的福利分析,讨论消费者的策略行为对厂商、消费者以及社会总福利的影响。最后,建立了垄断竞争情形下基于消费者策略行为的定价和订购决策模型,证明了存在一个对称的Nash均衡。
(3)本文通过互联网上的实验设计检验了消费者策略行为的存在性,验证了易逝品的价格、数量等信息会对消费者的购买决策产生较大影响,并为厂商的定价和订购策略提供了指导性建议。
With the development of technology and the fierce of competition in the market, the life cycle of the product is shorter than ever, and the speed product replacement is also accelerated. More and more products have the characteristic of perishability. The perishable goods always have very short life cycle and the salvage value is very small if it can not be sold out in the life cycle. At the same time, the consumer’s perceived risk will be enhanced when it approaches the product life cycle, such as milk, bread, vegetable etc. Therefore, the selling and management of perishable goods is a very difficult problem for many retailers.
Dynamic pricing mechanism as an engine of perishable good assets management, plays a very important role in all the process. With the coming of Internet, the cost of changing the price is very small and the information can spread quickly in the Internet. The interaction between firms and consumers also enhanced. All of these create a good environment for implementing dynamic pricing. At the same time, consumers can get the information of the product from the Internet easily, such as price、amounts etc .They can become cleverer than ever and can weigh their payoff of immediate purchase against the expected payoff of delaying their purchases, then, make the optimal purchasing decisions by the information.
Thus, the implement of dynamic pricing in the Internet environment is a sword with two sides. On one hand, it can increase the profit of the firm, on the other hand, it can not be avoided the strategic behavior of some consumers. This strategic behavior can have a great impact on the firm’s dynamic pricing and other decisions. Therefore, it is very necessary to consider how to realize maximize profit in the perishable goods assets management using the Internet technology, at the same time, incorporating the strategic behavior of consumers in the Internet environment for the enterprisers and the theorists.
This study based on the formers’shoulders, incorporating the new technology RFID in Internet and the consumer’s strategic behavior into the dynamic pricing and ordering decision of perishable goods, using dynamic programming、game theory、economics、econometrics、experiments design etc to explore the problem and get some conclusions below:
(1)The seller can maximize profits of selling perishable food through price adjustment based on real-time product quality and values through RFID technology. Dynamic pricing policy always brings a better profit than fixed pricing policy with a given cycle length; at the same time , the profit difference between dynamic pricing with RFID for tanking and tracking product value and fixed price becomes larger with the larger decay rate . The optimal price and the initial inventory level based on RFID is effected by many factors, such as the cost, the dropping rate of the value, the consumers’reserved price, the consumers’arrival rate etc.
(2)Consumer’s strategic behavior has great effects on the firm’s pricing decision. Under the deterministic demand situations, if the amount of the goods is limitless, the profit of the firm will decease with the proportion of strategic consumer increasing. If the firm makes the price decision ignoring the consumers’strategic behavior, the loss of the profit will increase with the proportion of strategic consumer increasing. If the amount of the good is limit, the firm’s pricing decision is high interrelated with the amounts:
When the amounts is small, the firm can ignore the consumer’s strategic behavior; when the amounts is relative much, the consumers can form a rational expectation of getting the product with a lowed price in the second period. The firm’s optimal decision is setting a high price in the first period and a relative low price in the second period to realize the equilibrium of rational expectation and to get the maximized profit; when the amounts is plenty, the optimal pricing decision is related with the discounted factor of the consumers. The price in the first period decreases with the increasing of consumers’discounted factor. The firm should set a proper price in the second period to make less consumers would wait to purchase the product in the second.
Under the stochastic demand situations, the firm’s pricing decision is also effected by the amounts and the consumers’reserved price: the price in the first period decreases with the increasing of initial amounts. At the same time, it exists a critical amounts, when the high reserved price consumer in the market is lager the critical amounts, the firm should choose dynamic pricing decision; when the high reserved price consumer in the market is less than the critical amounts, the firm should choose a refund policy decision to minimize the consumer’s strategic behavior.
(3) Consumer’s strategic behavior has great effects on the firm’s pricing decision. Whether under the deterministic demand situations or the stochastic demand situations, the optimal ordering decision is effected by the factors such as the magnitude of price discounting over time, the decay in consumers’valuations, and the arrival rate of the consumers. In contrast with traditional newsvendor model, When the market consists of a sufficiently large number of high-value consumers, rationing is optimal that is, to make a rationing risk to make the high reserved price consumer not wait to purchase in the second period with low price, and the firm to serve the market only at the high price in period 1; when the market consists of a sufficiently large number of low-value consumers , the rationing is never optimal , and the firm to serve all the market.
At the same time, the consumer’s risk preference has an effect on the optimal amounts. When the consumer’s risk aversion is high, the initial ordering amounts can be increased. Thus, the consumer’s risk aversion can buffer the adverse impact of the consumer’s strategic behavior.
(4) Faced with the consumer’s strategic behavior, incorporated the price and ordering decision together in the perishable good assets management will enhance the firm’s profit. When the consumers are homogeneous, the optimal price is less than the consumer’s reserved price, that is, the firm can not realize the first price discrimination; the optimal ordering amounts is lower than ignoring the consumer’s strategic behavior; the consumer’s risk aversion can buffer the adverse impact of the consumer’s strategic behavior. By the welfare analyses, we can get that considering the consumer’s strategic behavior will enhance the consumer、the firm and the social welfare.
When the consumers are heterogeneous, using the mechanism theory, we get the firm should choose a proper ordering amount in order that the high reserved price consumers can get the product whether at high demand situations or at low demand situations. At the high demand situations, the firm can make a rationing risk to make the high reserved price consumer purchase at the first period; at the low demand situations , the rationing is never optimal , and the firm to serve all the market.
In the monopolistic competition, it exist a symmetrical Nash equilibrium; all the firms make the identical pricing decision and ordering decision.
(5)By the empirical study of consumer’s strategic behavior, we find that the consumers can make the optimal purchasing decision dynamically based on the information of the price and the amounts ,etc. The firm can incorporate the demand in the market and the amounts of the product together, spreading the information strategically in order to effect the consumer’s purchasing decision.
For the discussion and analysis above, the innovations of this dissertation mainly embody as below:
(1) we first consider a RFID-based system and develop ordering decisions and the dynamic pricing schedule for perishable goods with a deterministic demand function in the Internet environment. Then, we extend the model to a stochastic demand case.
The most of existing research on RFID or RFID-enabled business models has been focusing on the conceptualization and descriptive analyses on its potential benefits of replacing barcode systems. Quantitative analysis on innovative business models based on RFID data has been rare. Using a price and value dependant demand function, we prove that the benefit of RFID-enabled dynamic pricing and tell the firm how to use RFID technology to implement the price and the ordering decision.
(2)Under the consumer’s strategic behavior, we consider how to make the optimal price、optimal initial ordering decision respective and how to make the optimal price and optimal initial ordering decision together.
The most of existing research on dynamic pricing model or traditional newsvendor model has been ignoring the consumer’s strategic behavior. This dissertation uses a discounted factor of consumer value getting a rational expectation equilibrium and the consumer’s optimal decision. Then, through the Stackelberg game theory, we get the optimal decision of the firm.
When we consider the pricing decision of the firm, we analyze the optimal pricing decision under different amount level in deterministic demand. In the stochastic demand, we get the optimal price and advise that in some situations the firm can use return policy to minimize the consumer’s strategic behavior; when we consider the optimal ordering decision of the firm, based on a model in deterministic demand, we extend the model to the stochastic demand when the consumers arrive the market simultaneously or the consumers arrive the market sequentially; when we consider the optimal price and optimal initial ordering decision together, we get the optimal price and optimal ordering amount, by the way, we analyze the consumer’s risk aversion’effect on the firm’s decision and the consumer’s strategic behavior’s effect on the consumer、the firm and the social welfare. In the end,we extent the model to the monopolistic competition markets.
(3)Through an Internet-based experiments, we confirm the existence of consumer’s strategic behavior. At the same time, we get that the difference of price and the amounts information can have great effect on consumer’s optimal purchasing decision. We also give some suggestion to the firm of implementing the price and ordering decision.
动态定价作为易逝品收入管理的引擎,在易逝品的收益管理过程中发挥着重要的作用。而且互联网的出现导致信息的传播更为便捷,厂商调整价格的菜单成本大大降低,消费者和厂商的互动性也得到了显著加强。这些都为易逝品动态定价的实施提供了更为有效的环境和保障。与此同时,消费者也可以通过互联网更容易的获得产品的价格、供应数量等市场信息而变得越来越“聪明”,他们会权衡自己在每阶段可能得到的不同利益,而决定最佳的购买时机。
因此,网络环境下动态定价的实施犹如一把“双刃剑”,在增加厂商利润的同时,也不可避免地导致消费者策略行为的发生,这种策略行为会对厂商的动态定价以及相关决策产生重大的影响。因此,考虑厂商如何利用新型的网络技术,以及将消费者的策略行为纳入到价格以及订购数量的决策范畴,实现易逝品的收益管理的最大化,无论在理论界还是实物界,都显得十分必要。
本研究在以前研究的基础上,分别将网络环境下出现的新技术(RFID)以及消费者的策略行为进一步考虑到易逝品的动态定价与订购决策中,综合应用动态规划、博弈论、经济学、计量经济学、试验设计等理论和方法,对上述问题进行了探索,并得到以下结论:
(1)销售商基于RFID技术的应用,可以在对产品价值跟踪的基础上,动态的调整价格,并决定最优的订购数量。与实行固定的价格策略相比,实行动态定价策略总能获得较大的收益,而且这种优势随着易逝品价值递减速率的增加而更为显著。销售商基于RFID技术的最优价格路径和初始的最优订购数量受到产品的成本、产品价值的递减速率、消费者保留价值的分布等因素的影响。
(2)消费者的策略行为会对厂商定价决策产生较大的影响。在确定性需求下,当产品没有数量限制时,厂商的利润随着策略型消费者所占比例的增加而减少,厂商忽略消费者策略行为所造成的损失随着策略型消费者所占比例的增加而增大;在有数量限制时,厂商的定价决策与产品的数量水平显著相关:
当厂商的数量较少时,厂商基本上可以忽略消费者的策略行为;当厂商有相对较多数量时,消费者会对第二阶段以较低价格获得产品的概率形成理性预期,厂商的最优决策是在第一阶段设定较高的价格,在第二阶段设定相对较低的价格,在理性预期均衡的条件下实现总利润最大化;当厂商有充足的数量时,厂商的最优定价决策与消费者的折扣因子有关,第一阶段的价格随着折扣因子的增大而减小,面临消费者的等待行为,厂商应在第二阶段设定适当的价格,尽可能使等待购买的消费者人数最少。
在不确定需求情形下,厂商的定价决策也受到产品数量和消费者保留价格的显著影响:厂商第一阶段的价格随着产品数量的增加而下降。同时,产品数量存在一个临界水平,当该临界水平大于具有高保留价格的消费者的人数时,厂商应该实行动态定价策略;而该临界水平小于具有高保留价格的消费者的人数时,厂商应该实行差价返还策略,以减少消费者的策略行为。
(3)消费者的策略行为会对厂商订购策略产生较大的影响。无论是确定性需求还是不确定需求,厂商的最优订购数量与消费者的价值递减速率、消费者的到达速率、消费者的保留价格以及厂商的折扣水平、厂商打折的时间等众多因素有关。与传统的报童模型相比,当消费者的保留价格较高时,厂商可以适当的减少初始的订购数量,通过制造一定的“缺货”风险,使得具有较高的保留价格的消费者在第一阶段以较高的价格购买,而不会等到第二阶段以较低的价格购买;当消费者的保留价格较低时,厂商可以忽视消费者的策略行为。
同时,消费者的风险厌恶程度对厂商的最优订购数量有一定的影响。消费者的风险厌恶程度越高,厂商的最优订购数量也相应较大。消费者的风险厌恶从一定程度上缓解了消费者的策略行为所带来的不利影响。
(4)面对消费者的策略行为,同时考虑价格和订购数量将会导致厂商收益的显著提高。在消费者同质情形,消费者的策略行为使得厂商的价格低于产品对消费者的保留价值,厂商无法实现完全价格歧视;最优数量也比不考虑这种策略行为时低。通过福利经济学的分析可以得到,与传统的报童模型相比,考虑消费者的策略行为,将会导致了生产者、消费者、社会总福利的显著提高。
在消费者异质的情形,通过机制设计的相关理论,得到厂商在需求不确定时,必须至少拥有恰当的数量使得那些具有高保留价格的消费者不管在何种需求情况下(高需求或者低需求),都能买到该产品。如果需求数量很大时,厂商可以制造一定的缺货风险,使得高保留价格的消费者在第一阶段购买;如果需求数量很小时,厂商会兼顾两阶段的消费者需求。同时,在垄断竞争的情形,存在一个对称的Nash均衡。
(5)通过对消费者策略行为的实证分析,得到消费者会基于厂商提供的价格和数量等产品信息,动态的优化自己的购买决策。当厂商产品的数量较少时,消费者价格敏感性会大大降低,消费者几乎不会等待厂商进一步降价,而马上购买。对厂商而言,可以通过结合目前的市场需求情况和产品的数量,策略性的发布产品的相关信息,以影响消费者的购买决策,从而获得最大利润。
通过以上方面的分析和讨论,本文的主要创新点可以归纳为以下三个方面:
(1)本文运用数学模型,建立易逝性产品的价值与需求之间的函数,分析了在RFID技术条件下,基于产品价值跟踪基础上动态定价的优越性,并给出如何应用RFID技术,得到最优的价格与初始的订购数量的决策。
(2)本文分析了在消费者策略行为下,易逝品的价格、订购数量以及同时将价格和订购数量作为决策变量时的最优决策,具体而言:
考虑易逝品的价格策略时,通过引入消费者折扣因子和理性预期,运用两阶段的动态博弈,分别在确定性需求和不确定需求下,得到厂商的最优价格策略与产品的初始数量显著相关,并提出在适当情形下可以运用差价返还策略来减少消费者策略行为的不利影响;考虑易逝品的订购策略时,运用传统的报童模型和理性预期均衡,得到厂商在确定性需求和不确定需求下的最优订购数量,发现相对于传统的报童模型,厂商在面临消费者策略行为下的最优订购数量要比传统的报童模型所得出的订购数量低;考虑易逝品的价格和订购策略相结合时,分别在消费者同质和消费者者异质的情形下,得到厂商的最优价格和初始的订购决策,同时分析了消费者的风险类型对厂商策略的影响,并通过经济学的福利分析,讨论消费者的策略行为对厂商、消费者以及社会总福利的影响。最后,建立了垄断竞争情形下基于消费者策略行为的定价和订购决策模型,证明了存在一个对称的Nash均衡。
(3)本文通过互联网上的实验设计检验了消费者策略行为的存在性,验证了易逝品的价格、数量等信息会对消费者的购买决策产生较大影响,并为厂商的定价和订购策略提供了指导性建议。
With the development of technology and the fierce of competition in the market, the life cycle of the product is shorter than ever, and the speed product replacement is also accelerated. More and more products have the characteristic of perishability. The perishable goods always have very short life cycle and the salvage value is very small if it can not be sold out in the life cycle. At the same time, the consumer’s perceived risk will be enhanced when it approaches the product life cycle, such as milk, bread, vegetable etc. Therefore, the selling and management of perishable goods is a very difficult problem for many retailers.
Dynamic pricing mechanism as an engine of perishable good assets management, plays a very important role in all the process. With the coming of Internet, the cost of changing the price is very small and the information can spread quickly in the Internet. The interaction between firms and consumers also enhanced. All of these create a good environment for implementing dynamic pricing. At the same time, consumers can get the information of the product from the Internet easily, such as price、amounts etc .They can become cleverer than ever and can weigh their payoff of immediate purchase against the expected payoff of delaying their purchases, then, make the optimal purchasing decisions by the information.
Thus, the implement of dynamic pricing in the Internet environment is a sword with two sides. On one hand, it can increase the profit of the firm, on the other hand, it can not be avoided the strategic behavior of some consumers. This strategic behavior can have a great impact on the firm’s dynamic pricing and other decisions. Therefore, it is very necessary to consider how to realize maximize profit in the perishable goods assets management using the Internet technology, at the same time, incorporating the strategic behavior of consumers in the Internet environment for the enterprisers and the theorists.
This study based on the formers’shoulders, incorporating the new technology RFID in Internet and the consumer’s strategic behavior into the dynamic pricing and ordering decision of perishable goods, using dynamic programming、game theory、economics、econometrics、experiments design etc to explore the problem and get some conclusions below:
(1)The seller can maximize profits of selling perishable food through price adjustment based on real-time product quality and values through RFID technology. Dynamic pricing policy always brings a better profit than fixed pricing policy with a given cycle length; at the same time , the profit difference between dynamic pricing with RFID for tanking and tracking product value and fixed price becomes larger with the larger decay rate . The optimal price and the initial inventory level based on RFID is effected by many factors, such as the cost, the dropping rate of the value, the consumers’reserved price, the consumers’arrival rate etc.
(2)Consumer’s strategic behavior has great effects on the firm’s pricing decision. Under the deterministic demand situations, if the amount of the goods is limitless, the profit of the firm will decease with the proportion of strategic consumer increasing. If the firm makes the price decision ignoring the consumers’strategic behavior, the loss of the profit will increase with the proportion of strategic consumer increasing. If the amount of the good is limit, the firm’s pricing decision is high interrelated with the amounts:
When the amounts is small, the firm can ignore the consumer’s strategic behavior; when the amounts is relative much, the consumers can form a rational expectation of getting the product with a lowed price in the second period. The firm’s optimal decision is setting a high price in the first period and a relative low price in the second period to realize the equilibrium of rational expectation and to get the maximized profit; when the amounts is plenty, the optimal pricing decision is related with the discounted factor of the consumers. The price in the first period decreases with the increasing of consumers’discounted factor. The firm should set a proper price in the second period to make less consumers would wait to purchase the product in the second.
Under the stochastic demand situations, the firm’s pricing decision is also effected by the amounts and the consumers’reserved price: the price in the first period decreases with the increasing of initial amounts. At the same time, it exists a critical amounts, when the high reserved price consumer in the market is lager the critical amounts, the firm should choose dynamic pricing decision; when the high reserved price consumer in the market is less than the critical amounts, the firm should choose a refund policy decision to minimize the consumer’s strategic behavior.
(3) Consumer’s strategic behavior has great effects on the firm’s pricing decision. Whether under the deterministic demand situations or the stochastic demand situations, the optimal ordering decision is effected by the factors such as the magnitude of price discounting over time, the decay in consumers’valuations, and the arrival rate of the consumers. In contrast with traditional newsvendor model, When the market consists of a sufficiently large number of high-value consumers, rationing is optimal that is, to make a rationing risk to make the high reserved price consumer not wait to purchase in the second period with low price, and the firm to serve the market only at the high price in period 1; when the market consists of a sufficiently large number of low-value consumers , the rationing is never optimal , and the firm to serve all the market.
At the same time, the consumer’s risk preference has an effect on the optimal amounts. When the consumer’s risk aversion is high, the initial ordering amounts can be increased. Thus, the consumer’s risk aversion can buffer the adverse impact of the consumer’s strategic behavior.
(4) Faced with the consumer’s strategic behavior, incorporated the price and ordering decision together in the perishable good assets management will enhance the firm’s profit. When the consumers are homogeneous, the optimal price is less than the consumer’s reserved price, that is, the firm can not realize the first price discrimination; the optimal ordering amounts is lower than ignoring the consumer’s strategic behavior; the consumer’s risk aversion can buffer the adverse impact of the consumer’s strategic behavior. By the welfare analyses, we can get that considering the consumer’s strategic behavior will enhance the consumer、the firm and the social welfare.
When the consumers are heterogeneous, using the mechanism theory, we get the firm should choose a proper ordering amount in order that the high reserved price consumers can get the product whether at high demand situations or at low demand situations. At the high demand situations, the firm can make a rationing risk to make the high reserved price consumer purchase at the first period; at the low demand situations , the rationing is never optimal , and the firm to serve all the market.
In the monopolistic competition, it exist a symmetrical Nash equilibrium; all the firms make the identical pricing decision and ordering decision.
(5)By the empirical study of consumer’s strategic behavior, we find that the consumers can make the optimal purchasing decision dynamically based on the information of the price and the amounts ,etc. The firm can incorporate the demand in the market and the amounts of the product together, spreading the information strategically in order to effect the consumer’s purchasing decision.
For the discussion and analysis above, the innovations of this dissertation mainly embody as below:
(1) we first consider a RFID-based system and develop ordering decisions and the dynamic pricing schedule for perishable goods with a deterministic demand function in the Internet environment. Then, we extend the model to a stochastic demand case.
The most of existing research on RFID or RFID-enabled business models has been focusing on the conceptualization and descriptive analyses on its potential benefits of replacing barcode systems. Quantitative analysis on innovative business models based on RFID data has been rare. Using a price and value dependant demand function, we prove that the benefit of RFID-enabled dynamic pricing and tell the firm how to use RFID technology to implement the price and the ordering decision.
(2)Under the consumer’s strategic behavior, we consider how to make the optimal price、optimal initial ordering decision respective and how to make the optimal price and optimal initial ordering decision together.
The most of existing research on dynamic pricing model or traditional newsvendor model has been ignoring the consumer’s strategic behavior. This dissertation uses a discounted factor of consumer value getting a rational expectation equilibrium and the consumer’s optimal decision. Then, through the Stackelberg game theory, we get the optimal decision of the firm.
When we consider the pricing decision of the firm, we analyze the optimal pricing decision under different amount level in deterministic demand. In the stochastic demand, we get the optimal price and advise that in some situations the firm can use return policy to minimize the consumer’s strategic behavior; when we consider the optimal ordering decision of the firm, based on a model in deterministic demand, we extend the model to the stochastic demand when the consumers arrive the market simultaneously or the consumers arrive the market sequentially; when we consider the optimal price and optimal initial ordering decision together, we get the optimal price and optimal ordering amount, by the way, we analyze the consumer’s risk aversion’effect on the firm’s decision and the consumer’s strategic behavior’s effect on the consumer、the firm and the social welfare. In the end,we extent the model to the monopolistic competition markets.
(3)Through an Internet-based experiments, we confirm the existence of consumer’s strategic behavior. At the same time, we get that the difference of price and the amounts information can have great effect on consumer’s optimal purchasing decision. We also give some suggestion to the firm of implementing the price and ordering decision.
引文
[1]AdachiY ,N oseT ,KuriyamaS . Optimal inventory control policy subject to different selling prices of perishable commodities [J].International Journal of Production Economics, 1999, 60-61:389-394.
[2] Amman.H.M, Kendric.D .Nonconvexities in stochastic control models [J]. International Economic Review, 1995, 36(2)455-475.
[3]Anthes GH .The price had better be right [J]. Computerworld December1998, 21: 65-66.
[4] Anderson, Wilson. Wait or Buy? The Strategic Consumer: Pricing and Profit Implications.2003, working paper.
[5] Arndorfer, J.B., M. Creamer. Best Buy taps Rapp to weed out angels, devils [M]. Advertising Age. 2005, 76(20): 8.
[6] Arnold, M.A., S.A. Lippman. The analytics of search with posted prices[J]. Econ. Theory .2001, 17:447-466.
[7] Ausubel, L.M., R.J. Deneckere.Reputation in bargaining and durable goods monopoly [J].Econometrica. 1989, 57(3): 511-531.
[8] Ausubel, L.M., R.J. Deneckere.Durable goods monopoly with incomplete information [J]. Rev.Econ. Stud. 1992, 59(4): 795-812.
[9] Aviv, Y., A. Pazgal.Optimal pricing of seasonal products in the presence of forward-looking consumers. 2003, Working paper.
[10] Aviv, Y. and A. Pazgal.Pricing of short- cycle products through active learning. 2002, Working paper, Washington University.
[11] Besanko, D., W.L. Winston.Optimal price skimming by a monopolist facing rational consumers [J]. Mgmt. Sci. 1990, 36(5): 555-567.
[12]Biller, S., ea al.Dynamic pricing and the direct-to-customer model in the automotive industry. 2005, working paper
[13] Bitran, G.R., S.V. Mondschein.Periodic pricing of seasonal products in retailing [J]. Mgmt. Sci. 1997, 43(1): 64-79.
[14]Badinelli, R alph D.An optimal dynamic policy for hotel yield management [J]. European Journal of Operational Research. 2000, 121:476- 503.
[15]Balakrishnan, N .,S .V .S ridharan,J .W .Patterson . Rationing capacity between two product classes [J ].Decision Sciences. 1996, 27(2):18 5-214.
[16]Bell P C. Revenue management: That's the ticket [J]. OR/MS Today, 1998, April: 430-439.
[17]Belobaba.Airline Yield Management: An overview of seat inventory control [J].Transportation Science, 1987,21:63-73.
[18] Besanko, D., W.L. Winston.Optimal price skimming by a monopolist facing rational consumers [J]. Mgmt. Sci. 199036(5): 555-567.
[19]Biller, S., ea al. Dynamic pricing and the direct-to-customer model in the automotive industry.2005, working paper.
[20]Bernard R,Delfo M.Measuring the impact of revenue management [J].Journal of revenue and pricing management, 2003, 2(3):261-270.
[21]Bertsmias D ,de Boer S V Simulation based optimization for airline network yield management[R].Working Paper, MIT,Cambridge,MA,2000.
[22]Bertsimas D., G. Perakis. Dynamic pricing: a learning approach. Working paper , MIT,2001.
[23]Bertsimas.D and Sanne D B ,Dynamic pricing and inventory control for multiple products[J].Journal of Revenue and Pricing Management ,2005,Vo1.3(4):303-319.
[24]Bitran G. R. ,H. K. Wadhwa. A methodology for demand learning with an Application of the optimal pricing of seasonal products. Working paper, MIT Sloan School of Management,1996:1-23.
[25]Bitran G. R. ,H. K. Wadhwa. Some structural properties of the seasonal product pricing problem .Working paper, MIT Sloan School of Management, 1996:1-33.
[26]Bitran, G., R Caldentey. An overview of pricing models for revenue management [J]. Manufacturing & Service Operations Management, 2003, 5( 3), 203-229.
[27]Bitran, G., S Mondschein. An application of yield management to the hotel industry considering multiple stays [J].Operation Research, 1995, 43:427- 443.
[28]Bitran, S. Monschein. Periodic pricing of seasonal product in retailing [J].Management Science, 1997, 43(1):427-443.
[29]Bitran, R. Caldentey, S. Mondschein. Coordinating clearance markdown sales of seasonal products in the retail chains[J ].Operation Research,1 998, 46 (5):60 9- 62 4.
[30]Bitran, G. R. and S.M. Gilbert .Managing hotel reservations with uncertain arrivals. 1992,Working Paper
[31] Blackburn, J. and G. Scudder .Supply Chain Strategies for Perishable Products: The Case of Fresh Produce.2003, OGSM Working Paper #03-14, Vanderbilt University, Nashville.
[32]Boyd E A, Bilegan I C. Revenue Management and E-Commerce[J]. Management Science, 2003, 49(10):1363-1386.
[33]Brent W L .Retail revenue management competitive strategy for grocery retails [J]. Journal of revenue and pricing management,2003,2(3):229-233.
[34]Burnetas, AX, C. E. Smith. Adaptive ordering and pricing for perishable products [J] .Operation Research, 2000, 48(3):436-443.
[35]Bridges, E., C.K. Yim, and R.A. Briesch.A High-Tech Product Market Share Model with Customer Expectations[J].Marketing Science, 1995, 14(1), 61–81.
[36] Bulow, J.I. Durable-goods monopolists[J]. J. Polit. Econ.1982, 90(2): 314-332.
[37]Carvalho and M.L. Puttman. Dynamic pricing and reinforcement learning, url: gg.nwu.edu/academic/deptprog/ meds-dep/or-seminars/puterman.pdf, 2003
[38]Burnetas.A.N., et al.Adaptive ordering and pricing for perishable products.Operations Reasarch.2000,48(3)436-443.
[39] Chatwin, R.E. Optimal dynamic pricing of perishable products with stochastic demand and a finite set of prices [J]. Eur. J. Oper. Res. 2000, 125(1): 149-174.
[40] Coase, R.H.Durability and monopoly[J]. J. Law & Econ. 1972, 15(1): 143-149.
[41] Conlisk J., E. Gerstner, J. Sobel.Cyclic pricing by a durable goods monopolist [J]. Quar. J.Econ. 1984, 99(3): 489-505.
[42] Cooper, W.L., T. Homem-de-Mello, A.J. Kleywegt.Models of the spiral-down effect in revenue management. 2004, Working paper.
[43]Caldentey R and Wein L M.Revenue Management of a Make-to-Stock Queue [R]. Working Paper, Stem School of Business, New York University, New York,2004.
[44]Campbell, K., E Morlok .Rail freight service flexibility and yield management [J]. Transportation Research Forum Proceedings,1994,2 :529- 548.
[45]Chatwin, R.E. Optimal dynamic pricing of perishable products with stochastic demand and a finite set of prices[J].European Journal of Operation Research,2000,12 5:14 9-174.
[46] Chande et al.Perishable inventory management and dynamic pricing using RFID technology[J].2005,Sadhana,30, Parts 2 & 3, April/June 2005, pp. 445–462.
[47]Cooper W. L.. Asymptotic behavior of an allocation policy for revenue Management [J ].Operation Research,2002,50(4):720-727.
[48]Cross, R. G. Revenue Management -Hard-core Tactics f or Market Domination [C]. Broadway books, New York, 1997.
[49]Curry, R. E..Optimal airline seat allocation with fare classes nested by origins and destinations[J].Tr ansportationS cience,1990,24(3):193-204,
[50]Desiraju, Ramarao ,Steven M. Shugan . Strategic Service Pricing and Yield Management, Journal of Marketing, 1999,63 :44 -56.
[51]Donaghy K, McMahon U, et al. Yield management: an overview [J].International Journal of Hospitality M anagement, 1995, 14 (2):139-150.
[52]Dasgupta and R. Das.Dynamic pricing with limited competitor information in a multi-agent economy. In Conf. on Cooperative Information Systems (London: SpringerVerlag) 2000, 299–310.
[53] Dana, J.D., Jr. Advance-purchase discounts and price discrimination in competitive markets [J]. J. Polit. Econ. 1998, 106(2): 395-422.
[54] Dana, J.D., Jr. Using yield management to shift demand when the peak time is unknown [J].RAND J. Econ. 1999(a), 30(3): 456-474.
[55] Dana, J.D., Jr.Equilibrium price dispersion under demand uncertainty: the roles of costly capacity and market structure [J]. RAND J. Econ. 1999(b), 30(4): 632-660.
[56] Dana, J.D., Jr.Monopoly price dispersion under demand uncertainty [J]. International Econ.Rev.2001, 42(3): 649-670.
[57]Das Varma, G., N. Vettas.Optimal dynamic pricing with inventories [J]. Econ. Letters. 2001, 72:335-340.
[58] Dolan R J, Jeuland A P. Experience curves and dynamic demand models: Implications for optimalpricing strategies [J].Journal of Marketing, 1981, 45: 52～73.
[59] Dutta, P., A. Matros, J.W. Weibull.Betrand competition with intertemporal demand. 2004, Working paper.
[60]Easley.D,et al .Controlling a stochastic progress with unknown parameters[J]. Econometrica 1988,56(5)1045-1064.
[61] Elmaghraby, W., P. Keskinocak.Dynamic pricing in the presence of inventory considerations: research overview, current practices and future directions [J]. Mgmt. Sci 2003, 49(10): 1287-1309.
[62] Elmaghraby, W., A. Gulcu, P. Keskinocak.Optimal markdown mechanisms in the presence of rational customers with multi-unit demands.2004,Working paper.
[63]Erdem, T., S. Imai, and M. Keane. A Model of Consumer Brand and Quantity Choice Dynamics under Price Uncertainty [J]. Quantitative Marketing and Economics 2003, 1(1), 5–64.
[64]Erdem, T. and M.P. Keane.Decision-making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets [J]. Marketing Science 1996,15(1), 1–20.
[65] Federgruen, A., A. Hetching.Combined pricing and inventory control under uncertainty [J].Oper. Res.1999, 47(3): 454-475.
[66] Feng, Y., G. Gallego.Optimal starting times for end-of-season sales and optimal stopping times for promotional fares [J]. Mgmt. Sci. 1995, 41(8): 1371-1391.
[67] Feng, Y., G. Gallego.Perishable asset revenue management with Markovian time dependent demand intensities [J]. Mgmt. Sci. 2000, 46(7): 941-956
[68] Feng, Y., B. Xiao.Optimal policies of yield management with multiple predetermined prices[J]. Oper. Res. 2000(a) 48(2): 332-343.
[69] Feng, Y., B. Xiao. A continuous-time yield management model with multiple prices and reversible price changes [J]. Mgmt. Sci. 2000(b), 46(5): 644-657.
[70]Feng, Y., G. Gallego. Perishable asset revenue management with markovian time dependent demand intensities[J]. Management Science,2000 ,46 (7 ):9 41-956.
[71]Feng, Y., B. Xiao. Maximizing revenues of perishable assets with a risk factor [J]. Operation Research, 1999, 47(2):33 7-341.
[72]Feng, Y., B. Xiao. Optimal policies of yield management with multiple predetermined prices[J].Management Science,2000.48 (2):332-343.
[73]Feng,Y., B .Xiao. A Combined Risk and Revenue Analysis for Managing Perishable Products [R].2004, Working paper, The Chinese University of HongKong.
[74]Feng,Y .,B .X iao.A continuous-time yield management model with multiple prices and reversible price changes[J].Management Science,2000,4 6(5): 644- 657 .
[75]Fisher M., A. Raman. Reducing the cost of demand uncertainty through accurate response to early sales [J].Operation Research,19 96,44 :87 -99.
[76]FrederickH .deB.Harris,JonathanP .Pinder.A revenue management approach to demand management and order booking in assemble-to-order manufacturing [ J]. Joural of 0perations Management, 1995,13:299-309.
[77]FujiwaraO,SoewandiH ,S edarageD .An optimal ordering policy for a two-stage inventory system for perishable products [J].European Journal of Operational Research,19 97(99):412-424
[78] Gallego, G., G. van Ryzin.Optimal dynamic pricing of inventories with stochastic demand over finite horizons [J]. Mgmt. Sci.1994, 40(8): 999-1020.
[79] Gallego, G., G. van Ryzin. A multi product dynamic pricing problem and its applications to network yield management [J].Oper. Res. 1997, 45(1): 24-41.
[80] Gallien, J. Dynamic mechanism design for online commerce.2004,Working paper.
[81]Gary.L.L.Optical Methods of Engineering Analysis[B].Cambridge/New York: Cambridge University Press, 1995.
[82]Gihman, Skorohod, A.V. Introduction to the Theory of Random Processes [B]. Springer, 1979.
[83]Gupta, K. Ravikumar, and M. Kumar. Adaptive strategies for price markdown in a multiunit descending price auction: A comparative study. In Proc. IEEE Conf. Systems, Man, and Cybernetics, 2002, 373–378
[84]Gupta, Sunil.Impact of Sales Promotions on When, What, and How Much to Buy [J]. Joumal of Marketing Research. 1988, 25 (November), 342-355.
[85]Gupta D. and Cooper W .L. Stochastic Comparisons in Production Yield Management and Airline Revenue Management[R], Working Paper, Department of MechanicalEngineering University of Minnesota, Minneapolis, 2002.
[86]Gupta D and Wang L. Manufacturing Capacity Revenue Management[R] .Departmen t of Mechanical Engineering University of Minnesota,Minneapolis ,2004.
[87]Gurnani,H.,Tang .Optional ordering decisions with uncertain cost and demand forecast updating[J]. Management Science .1999, 45(10) 1456-1462.
[88]Golabi, Kamal, Optimal Inventory Policies When Ordering Prices Are Random[J]. Operations Research, 1985, 33(3), 575-588.
[89]Gonul, F. and K. Srinivasan. Estimating the Impact of Consumer Expectations of Coupons on Purchase Behavior: A Dynamic Structural Model [J]. Marketing Science 1996,15(3), 262–279.
[90]Geraghty, M. K. and E. Johnson .Revenue management saves national car rental [J] .Interfaces,1997, 27(1):107-127.
[91]Gob C H, Greenberg B S and Matsuo H. Two-stage perishable inventory models [J] Management Science, 1993,39(5):633-649.
[92]Gould, J. P..Inventories and stochastic demand: equilibrium models of the firm and industry [J]. Journal of Business, 1978, 51(1):1-42.
[93]Harris, R. and P. Peacock. Ten steps to Yield Management success [J] Marketing M anagement, 1995, 4( 2):45 .
[94]HakseverC and MoussourakisJ .A model for optimizing multi-product inventory systems with multiple constraints [J].International Journal of Production Economics, 2005, 97:18-30.
[95] Hennessy, Terry .Where Category Management Really Counts[J]. Progressive Grocer, 1998, 77 (2), pp. 63–66.
[96]Heching, A., Gallego, G., G. van Ryzin.2002.mark-down pricing: an empirical analysis of policies and revenue potential at one apparel retail . Journal of revenue and retailing management .1(2):139-160.
[97]Heisen, Kristiaan and David Schmittlein, Some Characterizations of Stockpiling Behavior Under Uncertainty.Marketing Letters,1992, 3, 15-18.
[98]Ho, TH., Tang, C., and Bell, D.Rational Shopping Behavior and the Option Value of Variable Pricing. Management Science,1998, 44, 145-160.
[99]Holak, SL, Lehmann, DR, & Sultan, F. The Role of Expectations in the Adoption of. Innovative Consumer Durables: Some Preliminary Evidence. Journal of Retailing, 1987,63(Fall),67-73.
[100]Hu and Yilian Zhang. Online reinforcemcnt learning in multi agent systems, url: cimon.rochester.edu/public-html/papers/priceagent1.pdf, 2002
[101] Kadet, A.The price is right. Smart Money. Dec 2004: 90-94.
[102] Kahn, C.The durable goods monopolist and consistency with increasing costs. Econometrica. 1986, 54(2): 275-294.
[103]Kannan P.K.and Kopalle P.K. Dynamic pricing on the internet: Importance and implications for consumer behavior. Int. J. Electron. Commerce, 2001, 5(3): 63–83
[104]Keane, Michael P. Simulation Estimation for Panel Data Models with Limited Dependent Variables. In G.S. Maddala, C.R. Rao, and H.D. Vinod (eds.), Handbook of Statistics II:Econometrics. 1993,Amsterdam: Elsevier Science Publishers.
[105] Keane, Michael P. Modeling Heterogeneity and State Dependence in Consumer Choice Behavior[J]. Journal of Business and Economic Statistics 1997(a), 15(3), 310–327.
[106]Keane, M. Current Issues in Discrete Choice Modeling[J].Marketing Letters 1997(b), 8(3), 307–322.
[107]Keane, Michael P. and Kenneth I. Wolpin.Solution and Estimation of Dynamic Programming Models by Simulation [J]. Review of Economics and Statistics 1994, 76(4), 648–672.
[108] Keane, Michael P. and Kenneth I. Wolpin. Estimating Welfare Effects Consistent with Forward Looking Behavior, Part I: Lessons from a Simulation Exercise[J]. Journal of Human Resources 2002, 37(3), 570–599.
[109]Kendric.D.Stochastic control for economic models[B].McGraw-Hill Book company,New York.1981.
[110]Krishna, Aradhna.The Normative Impact of Consumer Price Expectations for Multiple Brands on Consumer Purchase Behavior [J]. Marketing Science, 1992, 11 (3), 266–287.
[111]——— The Effect of Deal Knowledge on Consumer Purchase Behavior[J]. Journal of Marketing Research, 1994(a),31 (February),76–91.
[112]——— The Impact of Dealing Patterns on Purchase Behavior[J]. Marketing Science, 1994(b)13(4), 351–73.
[113]———, Imran C. Currim, and Robert W. Shoemaker. Consumer Perceptions of Promotional Activity[J]. Journal of Marketing, 1989,55 (April), 4–16.
[114]Kachani, S., G. Perakis. A fluid model of dynamic pricing and inventory Management for make-to-stock manufacturing systems. Working paper, Operation Research Center,2002
[115]Kimes, S E. Yield Management: A tool for capacity-constrained service firm s [J] , Journal of Operations M anagement.1989.
[116]Kincaid,W.M,D.A., Imram S, Currim and Robert W Shoemaker .Consumer Perceptions of Promotional Activity[J].Journal of Marketing,1991, 55(2) 4-16.
[117]Kunreuther H, Schrage L.Joint pricing and inventory decisions for constant priced items[J].Management Science,1973,19(7):732-738.
[118]Lal, Rajiv and Ram Rao.Supermarket Competition: The Case of Every Day Low Pricing [J].Marketing Science, 1997, 16(1), 60–80.
[119]Lariviere, M.A., E.L.Porteus. Stalking information: Bayesian inventory management with unobserved lost sales [J]. Management Science, 1999, 45(3):346-343.
[120] Lariviere, M.A., J.A. van Mieghem.Strategically seeking service: how competition can generate Poisson arrivals [J]. Manufacturing & Service Oper.Mgmt.2004, 6(1): 23-40.
[121] Lau, H.,. The Newsboy Problem with Price-Dependent Demand Distribution, IIE Transactions, 1998,20: 168-175.
[122] Lazear, E.P. Retail pricing and clearance sales[J]. Amer. Econ. Rev.1986, 76(1): 14-32.
[123] Lilien, et al .Pricing non-storable perishable goods by using a purchase restriction with an application to airline fare pricing. Eur. J. Oper. Res. 1999, 134(3): 631-647.
[124] Liu, Q., G. van Ryzin. Strategic Capacity Rationing to Induce Early Purchases.2005,Working Paper.Columbia Business School.
[125] Litwak, David. So Many Programs, So Little Data.Supermarket Business [J].1997, 52 (9), pp.165–69.
[126]Li, D., Tang, O. Dynamic pricing through AUTOID-enabled product tracing in food supply chains. 2005,Proceedings of The 18th International Conference on Production Research.
[127]McFadden, D., “Conditional Logit Analysis of Qualitative Choice Behavior”, in P. Zarembka (ed.), Frontiers in Econometrics, Academic Press, New York, 1974。
[128] Maglaras, C., J. Meissner.Dynamic pricing strategies for multi-product revenue management problems. 2005, Working paper.
[129]Mantrala, M. K., S. Rao.A decision-support system that helps retailers decide order quantities and markdowns for fashion goods [J]. Interfaces 2001, 31(3) S146–S165.
[130]Meyer, Robert J. and Jo?o Assun??o.The Optimality of Consumer Stockpiling Strategies [J]. Marketing Science, 1990, 9 (1), 18–41.
[131]Meyer, Robert J,et al.Dynamic influence on individual choice behavior[J]. Marketing letters, 1997, 8:3,349-360.
[132]Meyer, Robert J. and Jo?o Assun??o.The Rational effect of price promotions on sales and consumption[J]. Management Science, 1993, 39(5), 517-535.
[133] McAfee, R.P., T. Wiseman. Capacity choice counters the Coase conjecture. 2004, Working paper.
[134] McAfee, R.P., V. Velde.Dynamic pricing in the airline industry. 2005, Working paper.
[135] McWilliams, G. Minding the store: analyzing customers, Best Buy decides not all are welcome. Wall Street Journal. 2004, Nov 8: A1.
[136]Monahan, et al.The dynamic pricing problem from a newvendor’sperpective[J] . M&SOM, 2004,6(1)73-91.
[137] Mccarteny.Integrating RFID with Plastic Products and Packaging in the Retail Supply Chain.2006, working paper
[138] Mitchell, V.-W. A Role for Consumer Risk Perceptions in Grocery Retailing[J], British Food Journal,1998,100 (4), 171–83.
[139] Monahan, et al.The dynamic pricing problem from a newvendor’s perpective [J] . M&SOM, 2004, 6(1)73-91.
[140] Monroe K., Delia Bitta.Models for pricing Decision [J].ournal of Marketing Research, 1978,15,pp.413～428.
[141]Nair.H.SDynamics of Pricing in Durable Good Markets: Application to 32-bit Console Video-Games. 2004, Working paper
[142]Narasimhan, C. Incorporating Consumer Price Expectations in a Diffusion Model [J], Marketing Science1989, 8(4)
[143] Narahari Y. et al .Dynamic pricing models for electronic business [J].Sadhan,2005, pp. 231–256.
[144] Nijs, Vincent R., et al.The Category-Demand Effects of Price Promotions[J], Marketing Science, 2001,20 (1), 1–22.
[145] Ovchinnikov.Strategic Response to Wait-or-Buy: Revenue Management through Last ute Deals in the Presence of Customer Learning.2005, working paper.
[146]MinPhip,H.F,Richard.Quantitative Models in marketing Research [B]. 2001, Cambaridge University press.
[147] Rajan, A., Rakesh, R. Steinberg. Dynamic pricing and ordering decisions by a monopolist [J]. Management Sci. 1992, 38 240–262.
[148] Raju, Y. Narahari, and K. Ravikumar. Learning dynamic prices in multi-seller electronicretail markets with price sensitive customers, stochastic demands, and inventory replenishments.IEEE Trans. Systems, Man, Cybern.: Part C (to appear), 2005
[149] Rao, R. C. Pricing research in marketing: The state of the art [J].J. Bus. 1984,57 S39–S64.
[150]Rust, J. Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher [J], Econometrica 1987,55(5), 999–1033
[151]Shugan ,Jinhong Xie .Electronic tickets,smart cards,and online prepayments:when and how to advace sell.J.Marketing science ,2001,20(3): 219-243.
[152]Smith,S.A.,D .A chabal.Clearance pricing and inventory policies for retail chains [J]. Management Science, 1998, 44:28 5-300.
[153]SmithB C ,L eimkuhlerJ F ,et al .Yield management at American Airlines[J]. Interfaces, 1992,22 (1):8- 31.
[154]Sobel, J. 1991. Durable goods monopoly with entry of new consumers. Econometrica. 59(5): 1455-1485.
[155]Song ,et al(2003)A Micromodel of New Product Adoption with Heterogeneous and Forward-Looking Consumers:Application to the Digital Camera Category. Quantitative Marketing and Economics, 1, 371–407,
[156] Stokey, N.L. 1979. Intertemporal price discrimination. Quar. J.Econ. 93(3): 355-371.
[157] Stokey, N.L. 1981. Rational expectations and durable goods pricing. Bell J. Econ. 12(1): 112-128.
[158]Su (2005) Inter-temporal Pricing with Strategic Customer Behavior. Working Paper
[159]Sun, Baohong, Scott Neslin, and Kannan Srinivasan. (2003). Measuring the Impact of Promotions on Brand Switching under Rational Consumer Behavior. Journal of marketing research,XL,389-405.
[160]Sun, Baohong (2003)Promotion Effect on Endogenous Consumption. working paper , Kenan-Flagler Business School, University of North Carolina.
[161]Talluri,K .,G .van Ryzin. Revenue management under a general discrete choice mod el of consumer behavior [J]. Management Science, 2004, 50 (1):19-33.
[162]Thowsen, G. T. A dynamic non stationary inventory problem for a price/quantity setting firm [J]. Naval Res. Logist. Quart. 1975,22461–476.
[163] Tsiros, M. Heilman.The Effect of Expiration Dates and Perceived Risk on Purchasing Behavior in Grocery Store Perishable Categories[J]. Journal of marketing, 2005, 69, 114-129.
[164]VanRyzin G , McGillJ . Revenue management without forecasting or optimization: an adaptive algorithm for determining airline seat protection levels [J] .Management Science,2000,46 :76 0-775.
[165]Van Ryzin G and Q Liu .On the choice-based linear programming model for network revenue management. Working paper, Decision, Risk & Operation of Columbia Business School 2004, 8(17).
[166]Varma, G .D.,Vetas, N .Optimal dynamic pricing with inventories [J]. Economics Letters, 2001, 72:335-340.
[167]Vulcano, G,G. van Ryzin, C. Maglaras. Optimal dynamic auctions for revenue management [J ].Management Science,2002,48(11):1388-1407.
[168]Weatherford, L., S. Bodily. A taxonomy and research overview of perishable-asset revenue management :yield management, overbooking and pricing[ J]. Operation Research, 1992,40 (5),:831-844
[169] Winer, R.S. (1985). A Price Vector Model of Demand for Consumer Durables: PreliminaryDevelopments. Marketing Science 4(1), 74-90.
[170]Xu, X., W.J. Hopp.Customer heterogeneity and strategic behavior in revenue management: a martingale approach.2004, Working paper.
[171] Zabel, E.Monopoly and uncertainty [J]. Rev. Econom. Stud. 1970,37205–37219.
[172]Zhao, W., Y-S Zheng.Optimal dynamic pricing for perishable assets with nonhomogeneous demand [J].Management Science ,2000 ,46 (3): 375-388
[173] Zhou,et al.On the Threshold Purchasing Behavior of Customers Facing Dynamically Priced Perishable Products. 2005, working paper
[174]艾兴政; 唐小我; 曾勇; 动态需求下高峰负荷定价模型研究。[J] 电子科技大学学报。1999(06):635-642
[175]曹洪,周江。季节性产品动态定价研究[J],数量经济技术经济研究,2004, (3): 122-125。
[176]陈旭。酒店收益管理的研究进展与前景[J],管理科学学报,2003,6( 6):72 -7 8 。
[177]陈旭。考虑批发价格更新的易逝品的零售商订货策 [J],中国管理科学 ,2 00 4, 12 (4):57-63。
[178]陈旭。基于收益管理的汽车出租定价与车辆配置[J],系统工程理论方法与应用 , 2005,14 (3):264-267。
[179]陈平。收入管理在易逝性高科技产品管理中的应用研究[J],科技与经济,2005 ,(1) :5 4-56。
[180]杜荣,胡奇英,魏轶华。两方竟争情况下产品动态定价研究[J],管理工程学报 2003, (1):20-24。
[181]官振中; 黄深泽; 史本山. 基于收益管理的季节性商品动态定价策略.[J]西南交通大学学报。2005(5):696-670。
[182]黄为,刘永俊。航空公司收益管理初探[J],民航经济与技术,1998,(5):21- 28 。
[183]李艳华.帕累托最优与航空客运中的超售[J]中国民航学院学报 ,2 00 0, 18 ( 2):18-22。
[184]林耀山,周晶,杨慧高科技产品的市场竞争特性分析[J],现代管理科学, 2005 ,(2) :8-10。
[185]刘军,邱莞华.航空客运收益管理的结构模型[J].北京航空航天大学学报(社 会 科 学版 ) ,2000,13 (4):17-21。
[186]刘德文,萧柏春,鲁若愚.易逝性高新技术产品在衰退期的收入管理问题[J],管理科学学报,2003,(6 ):6 6-71。
[187]刘军,邱婉华.关于收益管理问题的描述及分类[J].民航经济与技术,2000 ,(7):49-50.
[188]李庭国。基于RFID的零售企业管理信息系统方案设计[J]。物流技术,2006,29:31-33。
[189]李晓花,萧柏春,航空公司收入管理价格与舱位控制的统一分析[J],管理科学学报,2004(12):63-69。
[190]罗利,萧柏春。收入管理理论的研究现状及发展前景[J],管理科学学报, 2004,7(5): 75-83。
[191]彭鸿广,任志宇。基于RFID 技术的供应链协同商务管理[J]。工业技术经济,2006,5:91—93。
[192]王锁红 ,糜仲春。网络零售业中的动态定价模式[J]。2004.3,62-64。
[181]魏轶华,胡奇英。顾客有最大、最小保留价的连续时间收益管理[J],管理科学学报 ,2002,5 (6):4 8一51。
[193]席丽娟,徐虹。饭店收益管理的定价决策研究[J],旅游科学,2005 ,(4) :43-47。
[194]许洪; 胡运权; 李军。航空公司定价动态模型与座位配置研究。[J]系统工程理论与实践。2004 (12):44-49。
[195]俞言兵,罗利,寡占市场易逝性产品收入管理的博弈分析[J],运筹与管理,2003, (12):31-34。
[196]曾波.收益管理在旅游产品价格策略中的应用[J],江西社会科学,2003 ,(3) :242-243。
[2] Amman.H.M, Kendric.D .Nonconvexities in stochastic control models [J]. International Economic Review, 1995, 36(2)455-475.
[3]Anthes GH .The price had better be right [J]. Computerworld December1998, 21: 65-66.
[4] Anderson, Wilson. Wait or Buy? The Strategic Consumer: Pricing and Profit Implications.2003, working paper.
[5] Arndorfer, J.B., M. Creamer. Best Buy taps Rapp to weed out angels, devils [M]. Advertising Age. 2005, 76(20): 8.
[6] Arnold, M.A., S.A. Lippman. The analytics of search with posted prices[J]. Econ. Theory .2001, 17:447-466.
[7] Ausubel, L.M., R.J. Deneckere.Reputation in bargaining and durable goods monopoly [J].Econometrica. 1989, 57(3): 511-531.
[8] Ausubel, L.M., R.J. Deneckere.Durable goods monopoly with incomplete information [J]. Rev.Econ. Stud. 1992, 59(4): 795-812.
[9] Aviv, Y., A. Pazgal.Optimal pricing of seasonal products in the presence of forward-looking consumers. 2003, Working paper.
[10] Aviv, Y. and A. Pazgal.Pricing of short- cycle products through active learning. 2002, Working paper, Washington University.
[11] Besanko, D., W.L. Winston.Optimal price skimming by a monopolist facing rational consumers [J]. Mgmt. Sci. 1990, 36(5): 555-567.
[12]Biller, S., ea al.Dynamic pricing and the direct-to-customer model in the automotive industry. 2005, working paper
[13] Bitran, G.R., S.V. Mondschein.Periodic pricing of seasonal products in retailing [J]. Mgmt. Sci. 1997, 43(1): 64-79.
[14]Badinelli, R alph D.An optimal dynamic policy for hotel yield management [J]. European Journal of Operational Research. 2000, 121:476- 503.
[15]Balakrishnan, N .,S .V .S ridharan,J .W .Patterson . Rationing capacity between two product classes [J ].Decision Sciences. 1996, 27(2):18 5-214.
[16]Bell P C. Revenue management: That's the ticket [J]. OR/MS Today, 1998, April: 430-439.
[17]Belobaba.Airline Yield Management: An overview of seat inventory control [J].Transportation Science, 1987,21:63-73.
[18] Besanko, D., W.L. Winston.Optimal price skimming by a monopolist facing rational consumers [J]. Mgmt. Sci. 199036(5): 555-567.
[19]Biller, S., ea al. Dynamic pricing and the direct-to-customer model in the automotive industry.2005, working paper.
[20]Bernard R,Delfo M.Measuring the impact of revenue management [J].Journal of revenue and pricing management, 2003, 2(3):261-270.
[21]Bertsmias D ,de Boer S V Simulation based optimization for airline network yield management[R].Working Paper, MIT,Cambridge,MA,2000.
[22]Bertsimas D., G. Perakis. Dynamic pricing: a learning approach. Working paper , MIT,2001.
[23]Bertsimas.D and Sanne D B ,Dynamic pricing and inventory control for multiple products[J].Journal of Revenue and Pricing Management ,2005,Vo1.3(4):303-319.
[24]Bitran G. R. ,H. K. Wadhwa. A methodology for demand learning with an Application of the optimal pricing of seasonal products. Working paper, MIT Sloan School of Management,1996:1-23.
[25]Bitran G. R. ,H. K. Wadhwa. Some structural properties of the seasonal product pricing problem .Working paper, MIT Sloan School of Management, 1996:1-33.
[26]Bitran, G., R Caldentey. An overview of pricing models for revenue management [J]. Manufacturing & Service Operations Management, 2003, 5( 3), 203-229.
[27]Bitran, G., S Mondschein. An application of yield management to the hotel industry considering multiple stays [J].Operation Research, 1995, 43:427- 443.
[28]Bitran, S. Monschein. Periodic pricing of seasonal product in retailing [J].Management Science, 1997, 43(1):427-443.
[29]Bitran, R. Caldentey, S. Mondschein. Coordinating clearance markdown sales of seasonal products in the retail chains[J ].Operation Research,1 998, 46 (5):60 9- 62 4.
[30]Bitran, G. R. and S.M. Gilbert .Managing hotel reservations with uncertain arrivals. 1992,Working Paper
[31] Blackburn, J. and G. Scudder .Supply Chain Strategies for Perishable Products: The Case of Fresh Produce.2003, OGSM Working Paper #03-14, Vanderbilt University, Nashville.
[32]Boyd E A, Bilegan I C. Revenue Management and E-Commerce[J]. Management Science, 2003, 49(10):1363-1386.
[33]Brent W L .Retail revenue management competitive strategy for grocery retails [J]. Journal of revenue and pricing management,2003,2(3):229-233.
[34]Burnetas, AX, C. E. Smith. Adaptive ordering and pricing for perishable products [J] .Operation Research, 2000, 48(3):436-443.
[35]Bridges, E., C.K. Yim, and R.A. Briesch.A High-Tech Product Market Share Model with Customer Expectations[J].Marketing Science, 1995, 14(1), 61–81.
[36] Bulow, J.I. Durable-goods monopolists[J]. J. Polit. Econ.1982, 90(2): 314-332.
[37]Carvalho and M.L. Puttman. Dynamic pricing and reinforcement learning, url: gg.nwu.edu/academic/deptprog/ meds-dep/or-seminars/puterman.pdf, 2003
[38]Burnetas.A.N., et al.Adaptive ordering and pricing for perishable products.Operations Reasarch.2000,48(3)436-443.
[39] Chatwin, R.E. Optimal dynamic pricing of perishable products with stochastic demand and a finite set of prices [J]. Eur. J. Oper. Res. 2000, 125(1): 149-174.
[40] Coase, R.H.Durability and monopoly[J]. J. Law & Econ. 1972, 15(1): 143-149.
[41] Conlisk J., E. Gerstner, J. Sobel.Cyclic pricing by a durable goods monopolist [J]. Quar. J.Econ. 1984, 99(3): 489-505.
[42] Cooper, W.L., T. Homem-de-Mello, A.J. Kleywegt.Models of the spiral-down effect in revenue management. 2004, Working paper.
[43]Caldentey R and Wein L M.Revenue Management of a Make-to-Stock Queue [R]. Working Paper, Stem School of Business, New York University, New York,2004.
[44]Campbell, K., E Morlok .Rail freight service flexibility and yield management [J]. Transportation Research Forum Proceedings,1994,2 :529- 548.
[45]Chatwin, R.E. Optimal dynamic pricing of perishable products with stochastic demand and a finite set of prices[J].European Journal of Operation Research,2000,12 5:14 9-174.
[46] Chande et al.Perishable inventory management and dynamic pricing using RFID technology[J].2005,Sadhana,30, Parts 2 & 3, April/June 2005, pp. 445–462.
[47]Cooper W. L.. Asymptotic behavior of an allocation policy for revenue Management [J ].Operation Research,2002,50(4):720-727.
[48]Cross, R. G. Revenue Management -Hard-core Tactics f or Market Domination [C]. Broadway books, New York, 1997.
[49]Curry, R. E..Optimal airline seat allocation with fare classes nested by origins and destinations[J].Tr ansportationS cience,1990,24(3):193-204,
[50]Desiraju, Ramarao ,Steven M. Shugan . Strategic Service Pricing and Yield Management, Journal of Marketing, 1999,63 :44 -56.
[51]Donaghy K, McMahon U, et al. Yield management: an overview [J].International Journal of Hospitality M anagement, 1995, 14 (2):139-150.
[52]Dasgupta and R. Das.Dynamic pricing with limited competitor information in a multi-agent economy. In Conf. on Cooperative Information Systems (London: SpringerVerlag) 2000, 299–310.
[53] Dana, J.D., Jr. Advance-purchase discounts and price discrimination in competitive markets [J]. J. Polit. Econ. 1998, 106(2): 395-422.
[54] Dana, J.D., Jr. Using yield management to shift demand when the peak time is unknown [J].RAND J. Econ. 1999(a), 30(3): 456-474.
[55] Dana, J.D., Jr.Equilibrium price dispersion under demand uncertainty: the roles of costly capacity and market structure [J]. RAND J. Econ. 1999(b), 30(4): 632-660.
[56] Dana, J.D., Jr.Monopoly price dispersion under demand uncertainty [J]. International Econ.Rev.2001, 42(3): 649-670.
[57]Das Varma, G., N. Vettas.Optimal dynamic pricing with inventories [J]. Econ. Letters. 2001, 72:335-340.
[58] Dolan R J, Jeuland A P. Experience curves and dynamic demand models: Implications for optimalpricing strategies [J].Journal of Marketing, 1981, 45: 52～73.
[59] Dutta, P., A. Matros, J.W. Weibull.Betrand competition with intertemporal demand. 2004, Working paper.
[60]Easley.D,et al .Controlling a stochastic progress with unknown parameters[J]. Econometrica 1988,56(5)1045-1064.
[61] Elmaghraby, W., P. Keskinocak.Dynamic pricing in the presence of inventory considerations: research overview, current practices and future directions [J]. Mgmt. Sci 2003, 49(10): 1287-1309.
[62] Elmaghraby, W., A. Gulcu, P. Keskinocak.Optimal markdown mechanisms in the presence of rational customers with multi-unit demands.2004,Working paper.
[63]Erdem, T., S. Imai, and M. Keane. A Model of Consumer Brand and Quantity Choice Dynamics under Price Uncertainty [J]. Quantitative Marketing and Economics 2003, 1(1), 5–64.
[64]Erdem, T. and M.P. Keane.Decision-making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets [J]. Marketing Science 1996,15(1), 1–20.
[65] Federgruen, A., A. Hetching.Combined pricing and inventory control under uncertainty [J].Oper. Res.1999, 47(3): 454-475.
[66] Feng, Y., G. Gallego.Optimal starting times for end-of-season sales and optimal stopping times for promotional fares [J]. Mgmt. Sci. 1995, 41(8): 1371-1391.
[67] Feng, Y., G. Gallego.Perishable asset revenue management with Markovian time dependent demand intensities [J]. Mgmt. Sci. 2000, 46(7): 941-956
[68] Feng, Y., B. Xiao.Optimal policies of yield management with multiple predetermined prices[J]. Oper. Res. 2000(a) 48(2): 332-343.
[69] Feng, Y., B. Xiao. A continuous-time yield management model with multiple prices and reversible price changes [J]. Mgmt. Sci. 2000(b), 46(5): 644-657.
[70]Feng, Y., G. Gallego. Perishable asset revenue management with markovian time dependent demand intensities[J]. Management Science,2000 ,46 (7 ):9 41-956.
[71]Feng, Y., B. Xiao. Maximizing revenues of perishable assets with a risk factor [J]. Operation Research, 1999, 47(2):33 7-341.
[72]Feng, Y., B. Xiao. Optimal policies of yield management with multiple predetermined prices[J].Management Science,2000.48 (2):332-343.
[73]Feng,Y., B .Xiao. A Combined Risk and Revenue Analysis for Managing Perishable Products [R].2004, Working paper, The Chinese University of HongKong.
[74]Feng,Y .,B .X iao.A continuous-time yield management model with multiple prices and reversible price changes[J].Management Science,2000,4 6(5): 644- 657 .
[75]Fisher M., A. Raman. Reducing the cost of demand uncertainty through accurate response to early sales [J].Operation Research,19 96,44 :87 -99.
[76]FrederickH .deB.Harris,JonathanP .Pinder.A revenue management approach to demand management and order booking in assemble-to-order manufacturing [ J]. Joural of 0perations Management, 1995,13:299-309.
[77]FujiwaraO,SoewandiH ,S edarageD .An optimal ordering policy for a two-stage inventory system for perishable products [J].European Journal of Operational Research,19 97(99):412-424
[78] Gallego, G., G. van Ryzin.Optimal dynamic pricing of inventories with stochastic demand over finite horizons [J]. Mgmt. Sci.1994, 40(8): 999-1020.
[79] Gallego, G., G. van Ryzin. A multi product dynamic pricing problem and its applications to network yield management [J].Oper. Res. 1997, 45(1): 24-41.
[80] Gallien, J. Dynamic mechanism design for online commerce.2004,Working paper.
[81]Gary.L.L.Optical Methods of Engineering Analysis[B].Cambridge/New York: Cambridge University Press, 1995.
[82]Gihman, Skorohod, A.V. Introduction to the Theory of Random Processes [B]. Springer, 1979.
[83]Gupta, K. Ravikumar, and M. Kumar. Adaptive strategies for price markdown in a multiunit descending price auction: A comparative study. In Proc. IEEE Conf. Systems, Man, and Cybernetics, 2002, 373–378
[84]Gupta, Sunil.Impact of Sales Promotions on When, What, and How Much to Buy [J]. Joumal of Marketing Research. 1988, 25 (November), 342-355.
[85]Gupta D. and Cooper W .L. Stochastic Comparisons in Production Yield Management and Airline Revenue Management[R], Working Paper, Department of MechanicalEngineering University of Minnesota, Minneapolis, 2002.
[86]Gupta D and Wang L. Manufacturing Capacity Revenue Management[R] .Departmen t of Mechanical Engineering University of Minnesota,Minneapolis ,2004.
[87]Gurnani,H.,Tang .Optional ordering decisions with uncertain cost and demand forecast updating[J]. Management Science .1999, 45(10) 1456-1462.
[88]Golabi, Kamal, Optimal Inventory Policies When Ordering Prices Are Random[J]. Operations Research, 1985, 33(3), 575-588.
[89]Gonul, F. and K. Srinivasan. Estimating the Impact of Consumer Expectations of Coupons on Purchase Behavior: A Dynamic Structural Model [J]. Marketing Science 1996,15(3), 262–279.
[90]Geraghty, M. K. and E. Johnson .Revenue management saves national car rental [J] .Interfaces,1997, 27(1):107-127.
[91]Gob C H, Greenberg B S and Matsuo H. Two-stage perishable inventory models [J] Management Science, 1993,39(5):633-649.
[92]Gould, J. P..Inventories and stochastic demand: equilibrium models of the firm and industry [J]. Journal of Business, 1978, 51(1):1-42.
[93]Harris, R. and P. Peacock. Ten steps to Yield Management success [J] Marketing M anagement, 1995, 4( 2):45 .
[94]HakseverC and MoussourakisJ .A model for optimizing multi-product inventory systems with multiple constraints [J].International Journal of Production Economics, 2005, 97:18-30.
[95] Hennessy, Terry .Where Category Management Really Counts[J]. Progressive Grocer, 1998, 77 (2), pp. 63–66.
[96]Heching, A., Gallego, G., G. van Ryzin.2002.mark-down pricing: an empirical analysis of policies and revenue potential at one apparel retail . Journal of revenue and retailing management .1(2):139-160.
[97]Heisen, Kristiaan and David Schmittlein, Some Characterizations of Stockpiling Behavior Under Uncertainty.Marketing Letters,1992, 3, 15-18.
[98]Ho, TH., Tang, C., and Bell, D.Rational Shopping Behavior and the Option Value of Variable Pricing. Management Science,1998, 44, 145-160.
[99]Holak, SL, Lehmann, DR, & Sultan, F. The Role of Expectations in the Adoption of. Innovative Consumer Durables: Some Preliminary Evidence. Journal of Retailing, 1987,63(Fall),67-73.
[100]Hu and Yilian Zhang. Online reinforcemcnt learning in multi agent systems, url: cimon.rochester.edu/public-html/papers/priceagent1.pdf, 2002
[101] Kadet, A.The price is right. Smart Money. Dec 2004: 90-94.
[102] Kahn, C.The durable goods monopolist and consistency with increasing costs. Econometrica. 1986, 54(2): 275-294.
[103]Kannan P.K.and Kopalle P.K. Dynamic pricing on the internet: Importance and implications for consumer behavior. Int. J. Electron. Commerce, 2001, 5(3): 63–83
[104]Keane, Michael P. Simulation Estimation for Panel Data Models with Limited Dependent Variables. In G.S. Maddala, C.R. Rao, and H.D. Vinod (eds.), Handbook of Statistics II:Econometrics. 1993,Amsterdam: Elsevier Science Publishers.
[105] Keane, Michael P. Modeling Heterogeneity and State Dependence in Consumer Choice Behavior[J]. Journal of Business and Economic Statistics 1997(a), 15(3), 310–327.
[106]Keane, M. Current Issues in Discrete Choice Modeling[J].Marketing Letters 1997(b), 8(3), 307–322.
[107]Keane, Michael P. and Kenneth I. Wolpin.Solution and Estimation of Dynamic Programming Models by Simulation [J]. Review of Economics and Statistics 1994, 76(4), 648–672.
[108] Keane, Michael P. and Kenneth I. Wolpin. Estimating Welfare Effects Consistent with Forward Looking Behavior, Part I: Lessons from a Simulation Exercise[J]. Journal of Human Resources 2002, 37(3), 570–599.
[109]Kendric.D.Stochastic control for economic models[B].McGraw-Hill Book company,New York.1981.
[110]Krishna, Aradhna.The Normative Impact of Consumer Price Expectations for Multiple Brands on Consumer Purchase Behavior [J]. Marketing Science, 1992, 11 (3), 266–287.
[111]——— The Effect of Deal Knowledge on Consumer Purchase Behavior[J]. Journal of Marketing Research, 1994(a),31 (February),76–91.
[112]——— The Impact of Dealing Patterns on Purchase Behavior[J]. Marketing Science, 1994(b)13(4), 351–73.
[113]———, Imran C. Currim, and Robert W. Shoemaker. Consumer Perceptions of Promotional Activity[J]. Journal of Marketing, 1989,55 (April), 4–16.
[114]Kachani, S., G. Perakis. A fluid model of dynamic pricing and inventory Management for make-to-stock manufacturing systems. Working paper, Operation Research Center,2002
[115]Kimes, S E. Yield Management: A tool for capacity-constrained service firm s [J] , Journal of Operations M anagement.1989.
[116]Kincaid,W.M,D.A., Imram S, Currim and Robert W Shoemaker .Consumer Perceptions of Promotional Activity[J].Journal of Marketing,1991, 55(2) 4-16.
[117]Kunreuther H, Schrage L.Joint pricing and inventory decisions for constant priced items[J].Management Science,1973,19(7):732-738.
[118]Lal, Rajiv and Ram Rao.Supermarket Competition: The Case of Every Day Low Pricing [J].Marketing Science, 1997, 16(1), 60–80.
[119]Lariviere, M.A., E.L.Porteus. Stalking information: Bayesian inventory management with unobserved lost sales [J]. Management Science, 1999, 45(3):346-343.
[120] Lariviere, M.A., J.A. van Mieghem.Strategically seeking service: how competition can generate Poisson arrivals [J]. Manufacturing & Service Oper.Mgmt.2004, 6(1): 23-40.
[121] Lau, H.,. The Newsboy Problem with Price-Dependent Demand Distribution, IIE Transactions, 1998,20: 168-175.
[122] Lazear, E.P. Retail pricing and clearance sales[J]. Amer. Econ. Rev.1986, 76(1): 14-32.
[123] Lilien, et al .Pricing non-storable perishable goods by using a purchase restriction with an application to airline fare pricing. Eur. J. Oper. Res. 1999, 134(3): 631-647.
[124] Liu, Q., G. van Ryzin. Strategic Capacity Rationing to Induce Early Purchases.2005,Working Paper.Columbia Business School.
[125] Litwak, David. So Many Programs, So Little Data.Supermarket Business [J].1997, 52 (9), pp.165–69.
[126]Li, D., Tang, O. Dynamic pricing through AUTOID-enabled product tracing in food supply chains. 2005,Proceedings of The 18th International Conference on Production Research.
[127]McFadden, D., “Conditional Logit Analysis of Qualitative Choice Behavior”, in P. Zarembka (ed.), Frontiers in Econometrics, Academic Press, New York, 1974。
[128] Maglaras, C., J. Meissner.Dynamic pricing strategies for multi-product revenue management problems. 2005, Working paper.
[129]Mantrala, M. K., S. Rao.A decision-support system that helps retailers decide order quantities and markdowns for fashion goods [J]. Interfaces 2001, 31(3) S146–S165.
[130]Meyer, Robert J. and Jo?o Assun??o.The Optimality of Consumer Stockpiling Strategies [J]. Marketing Science, 1990, 9 (1), 18–41.
[131]Meyer, Robert J,et al.Dynamic influence on individual choice behavior[J]. Marketing letters, 1997, 8:3,349-360.
[132]Meyer, Robert J. and Jo?o Assun??o.The Rational effect of price promotions on sales and consumption[J]. Management Science, 1993, 39(5), 517-535.
[133] McAfee, R.P., T. Wiseman. Capacity choice counters the Coase conjecture. 2004, Working paper.
[134] McAfee, R.P., V. Velde.Dynamic pricing in the airline industry. 2005, Working paper.
[135] McWilliams, G. Minding the store: analyzing customers, Best Buy decides not all are welcome. Wall Street Journal. 2004, Nov 8: A1.
[136]Monahan, et al.The dynamic pricing problem from a newvendor’sperpective[J] . M&SOM, 2004,6(1)73-91.
[137] Mccarteny.Integrating RFID with Plastic Products and Packaging in the Retail Supply Chain.2006, working paper
[138] Mitchell, V.-W. A Role for Consumer Risk Perceptions in Grocery Retailing[J], British Food Journal,1998,100 (4), 171–83.
[139] Monahan, et al.The dynamic pricing problem from a newvendor’s perpective [J] . M&SOM, 2004, 6(1)73-91.
[140] Monroe K., Delia Bitta.Models for pricing Decision [J].ournal of Marketing Research, 1978,15,pp.413～428.
[141]Nair.H.SDynamics of Pricing in Durable Good Markets: Application to 32-bit Console Video-Games. 2004, Working paper
[142]Narasimhan, C. Incorporating Consumer Price Expectations in a Diffusion Model [J], Marketing Science1989, 8(4)
[143] Narahari Y. et al .Dynamic pricing models for electronic business [J].Sadhan,2005, pp. 231–256.
[144] Nijs, Vincent R., et al.The Category-Demand Effects of Price Promotions[J], Marketing Science, 2001,20 (1), 1–22.
[145] Ovchinnikov.Strategic Response to Wait-or-Buy: Revenue Management through Last ute Deals in the Presence of Customer Learning.2005, working paper.
[146]MinPhip,H.F,Richard.Quantitative Models in marketing Research [B]. 2001, Cambaridge University press.
[147] Rajan, A., Rakesh, R. Steinberg. Dynamic pricing and ordering decisions by a monopolist [J]. Management Sci. 1992, 38 240–262.
[148] Raju, Y. Narahari, and K. Ravikumar. Learning dynamic prices in multi-seller electronicretail markets with price sensitive customers, stochastic demands, and inventory replenishments.IEEE Trans. Systems, Man, Cybern.: Part C (to appear), 2005
[149] Rao, R. C. Pricing research in marketing: The state of the art [J].J. Bus. 1984,57 S39–S64.
[150]Rust, J. Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher [J], Econometrica 1987,55(5), 999–1033
[151]Shugan ,Jinhong Xie .Electronic tickets,smart cards,and online prepayments:when and how to advace sell.J.Marketing science ,2001,20(3): 219-243.
[152]Smith,S.A.,D .A chabal.Clearance pricing and inventory policies for retail chains [J]. Management Science, 1998, 44:28 5-300.
[153]SmithB C ,L eimkuhlerJ F ,et al .Yield management at American Airlines[J]. Interfaces, 1992,22 (1):8- 31.
[154]Sobel, J. 1991. Durable goods monopoly with entry of new consumers. Econometrica. 59(5): 1455-1485.
[155]Song ,et al(2003)A Micromodel of New Product Adoption with Heterogeneous and Forward-Looking Consumers:Application to the Digital Camera Category. Quantitative Marketing and Economics, 1, 371–407,
[156] Stokey, N.L. 1979. Intertemporal price discrimination. Quar. J.Econ. 93(3): 355-371.
[157] Stokey, N.L. 1981. Rational expectations and durable goods pricing. Bell J. Econ. 12(1): 112-128.
[158]Su (2005) Inter-temporal Pricing with Strategic Customer Behavior. Working Paper
[159]Sun, Baohong, Scott Neslin, and Kannan Srinivasan. (2003). Measuring the Impact of Promotions on Brand Switching under Rational Consumer Behavior. Journal of marketing research,XL,389-405.
[160]Sun, Baohong (2003)Promotion Effect on Endogenous Consumption. working paper , Kenan-Flagler Business School, University of North Carolina.
[161]Talluri,K .,G .van Ryzin. Revenue management under a general discrete choice mod el of consumer behavior [J]. Management Science, 2004, 50 (1):19-33.
[162]Thowsen, G. T. A dynamic non stationary inventory problem for a price/quantity setting firm [J]. Naval Res. Logist. Quart. 1975,22461–476.
[163] Tsiros, M. Heilman.The Effect of Expiration Dates and Perceived Risk on Purchasing Behavior in Grocery Store Perishable Categories[J]. Journal of marketing, 2005, 69, 114-129.
[164]VanRyzin G , McGillJ . Revenue management without forecasting or optimization: an adaptive algorithm for determining airline seat protection levels [J] .Management Science,2000,46 :76 0-775.
[165]Van Ryzin G and Q Liu .On the choice-based linear programming model for network revenue management. Working paper, Decision, Risk & Operation of Columbia Business School 2004, 8(17).
[166]Varma, G .D.,Vetas, N .Optimal dynamic pricing with inventories [J]. Economics Letters, 2001, 72:335-340.
[167]Vulcano, G,G. van Ryzin, C. Maglaras. Optimal dynamic auctions for revenue management [J ].Management Science,2002,48(11):1388-1407.
[168]Weatherford, L., S. Bodily. A taxonomy and research overview of perishable-asset revenue management :yield management, overbooking and pricing[ J]. Operation Research, 1992,40 (5),:831-844
[169] Winer, R.S. (1985). A Price Vector Model of Demand for Consumer Durables: PreliminaryDevelopments. Marketing Science 4(1), 74-90.
[170]Xu, X., W.J. Hopp.Customer heterogeneity and strategic behavior in revenue management: a martingale approach.2004, Working paper.
[171] Zabel, E.Monopoly and uncertainty [J]. Rev. Econom. Stud. 1970,37205–37219.
[172]Zhao, W., Y-S Zheng.Optimal dynamic pricing for perishable assets with nonhomogeneous demand [J].Management Science ,2000 ,46 (3): 375-388
[173] Zhou,et al.On the Threshold Purchasing Behavior of Customers Facing Dynamically Priced Perishable Products. 2005, working paper
[174]艾兴政; 唐小我; 曾勇; 动态需求下高峰负荷定价模型研究。[J] 电子科技大学学报。1999(06):635-642
[175]曹洪,周江。季节性产品动态定价研究[J],数量经济技术经济研究,2004, (3): 122-125。
[176]陈旭。酒店收益管理的研究进展与前景[J],管理科学学报,2003,6( 6):72 -7 8 。
[177]陈旭。考虑批发价格更新的易逝品的零售商订货策 [J],中国管理科学 ,2 00 4, 12 (4):57-63。
[178]陈旭。基于收益管理的汽车出租定价与车辆配置[J],系统工程理论方法与应用 , 2005,14 (3):264-267。
[179]陈平。收入管理在易逝性高科技产品管理中的应用研究[J],科技与经济,2005 ,(1) :5 4-56。
[180]杜荣,胡奇英,魏轶华。两方竟争情况下产品动态定价研究[J],管理工程学报 2003, (1):20-24。
[181]官振中; 黄深泽; 史本山. 基于收益管理的季节性商品动态定价策略.[J]西南交通大学学报。2005(5):696-670。
[182]黄为,刘永俊。航空公司收益管理初探[J],民航经济与技术,1998,(5):21- 28 。
[183]李艳华.帕累托最优与航空客运中的超售[J]中国民航学院学报 ,2 00 0, 18 ( 2):18-22。
[184]林耀山,周晶,杨慧高科技产品的市场竞争特性分析[J],现代管理科学, 2005 ,(2) :8-10。
[185]刘军,邱莞华.航空客运收益管理的结构模型[J].北京航空航天大学学报(社 会 科 学版 ) ,2000,13 (4):17-21。
[186]刘德文,萧柏春,鲁若愚.易逝性高新技术产品在衰退期的收入管理问题[J],管理科学学报,2003,(6 ):6 6-71。
[187]刘军,邱婉华.关于收益管理问题的描述及分类[J].民航经济与技术,2000 ,(7):49-50.
[188]李庭国。基于RFID的零售企业管理信息系统方案设计[J]。物流技术,2006,29:31-33。
[189]李晓花,萧柏春,航空公司收入管理价格与舱位控制的统一分析[J],管理科学学报,2004(12):63-69。
[190]罗利,萧柏春。收入管理理论的研究现状及发展前景[J],管理科学学报, 2004,7(5): 75-83。
[191]彭鸿广,任志宇。基于RFID 技术的供应链协同商务管理[J]。工业技术经济,2006,5:91—93。
[192]王锁红 ,糜仲春。网络零售业中的动态定价模式[J]。2004.3,62-64。
[181]魏轶华,胡奇英。顾客有最大、最小保留价的连续时间收益管理[J],管理科学学报 ,2002,5 (6):4 8一51。
[193]席丽娟,徐虹。饭店收益管理的定价决策研究[J],旅游科学,2005 ,(4) :43-47。
[194]许洪; 胡运权; 李军。航空公司定价动态模型与座位配置研究。[J]系统工程理论与实践。2004 (12):44-49。
[195]俞言兵,罗利,寡占市场易逝性产品收入管理的博弈分析[J],运筹与管理,2003, (12):31-34。
[196]曾波.收益管理在旅游产品价格策略中的应用[J],江西社会科学,2003 ,(3) :242-243。