收益管理及供应链管理中的多阶段问题研究
|
摘要
在零售业,由于产品生命周期的缩短和需求不确定性的提高,越来越多的公司被迫去处理降价和缺货等商业问题。此外,信息技术的发展和零售业成本结构的特点为定量化分析该行业的管理决策提供了关键的数据支持。由此,对零售业中有关收益管理和供应链管理问题的研究日益受到人们的重视。不过,随着公司对收益的优化将逐步从短期转向长期,又因管理实践中的决策时点大都是离散的,所以从时间维度上将现存收益管理和供应链管理的研究推广到多阶段意义重大。因此,基于零售业背景,针对以往研究所存在的不足,沿时间(Period)和空间(Stage)两个维度,本文试图从定量分析的角度对收益管理及供应链管理中的多阶段问题进行研究。
1)多阶段收益管理问题
在这一部分,我们主要从经销商(零售商)的角度,研究相应于不同特性的产品,尤其是不同需求属性的产品的定价和库存控制等收益管理问题。
首先,我们研究价格敏感的随机需求产品分批销售时的数量优化问题。其中将着重讨论销售期容量固定的季节性产品在两阶段的容量分配策略,并将借助数值分析研究市场参数对最优策略和最优收益值的影响。结合理论和数值分析结果,我们发现:在随机且价格敏感的需求下,产品分批销售时的最优预售数量随当前价格和剩余产品的数量递增,而随剩余的销售时间递减。
其次,在零售实践中,经常见到有些商品在超市被大堆展示销售而有些商品却被分批限量销售。根据这种现象,我们首次在收益管理研究领域中引入需求依赖库存水平的情形,并在此基础上研究当需求同时依赖于价格和库存展示水平时,有关产品的动态定价和容量分配问题。我们建立问题的MDP模型;然后针对模型的几种特殊情况,分别获得单阶段模型的解析解及每阶段的预留数量(即不被拿来展示预售的产品数量)事先确定时的多阶段模型的解的存在唯一性;同时讨论最优解的结构性质。其中主要结果有:(a)对于过多的库存展示数量会给需求带来负面影响的高档商品,即使在销售季节后期也很少会出现降价抛售的现象;同时,我们的模型分析表明对于这类商品采用高价低销量的营销策略优于薄利多销的策略。(b)对于产品在各阶段的预留计划在一开始时就确定的情形,存在唯一的最优定价策略。(c)柔性定价策略都可提高收益,且动态定价尤其适合那些价格弹性小、市场需求变动大和库存持有量少的高端产品。
最后,我们首次从概念上对容量固定且无时效限制的这类产品进行界定,并针对这类产品的初始订购及其销售数量和销售时间等问题进行探讨。具体地,假定每阶段的需求为相互独立且分布已知的随机变量,同时价格服从随机游走的自回归过程,在考虑使经销商的期望折扣销售收益达到最大的目标下,我们分别建立关于该问题的有限阶段和无限阶段优化模型;获得模型的最优解并讨论解的有关性质;而后我们给出有限阶段模型的数值求解方法并在此基础上通过数值分析进一步验证了最优解的结构性质,描述几个系统参数对最优策略及相应最优收益值的影响,同时还给出经销商关于销售期数选择的一些管理洞见。其中获得以下三个主要结果:(a)需求变动越大,越有必要对库存在各销售阶段进行合理分配;当需求变动适中时,由于随着销售期的延长,收益的增长并不是很明显,考虑到资金的机会成本,速战速决的销售策略比较可取。(b)持有成本愈低,价格涨势愈大,则初始订购越多且预留的产品愈多;此外,当前价格越高,拿来预售的产品数量却越少。(c)不同于传统的收益管理问题,由于产品的无时效限制使得经销商可自由选择销售期,从而当需求变动越大,价格越高,价格增长幅度越大及成本越低时,销售时间可持续越长。
2)多阶段供应链管理问题
这一部分,我们将在供应链框架下基于信息共享和契约机制考虑渠道成员的决策(订购、定价、容量分配)及其对供应链绩效的影响。
首先,我们研究信息共享和供应链契约这两种协调供应链系统的主要机制。一开始就两种不同需求(独立需求和相关需求)环境下的有关信息共享机制理论进行概述;接下来分别阐述基于两种不同研究方法(优化方法,博弈论方法)的供应链契约理论;然后在此基础上,论述两种协调机制集成研究理论的进展。得到的结论有:既然信息共享和供应链契约都为协调供应链系统的有效机制,那么在实践中对两者的集成或优化整合定会给系统成员带来潜在利益的提升。从某种意义上说,成员之间所缔结的契约类型基本上界定了彼此的关系,而像信息需不需要共享、与谁共享、共享到什么程度等此类问题都依赖于这种关系的密切程度。所以,有关成员之间信息共享的问题也可纳入缔结契约内容的范畴,而不是割裂地对它们分别处理。
其次,依托批发价契约,我们首次考虑下游零售商的收益管理策略对上游企业及整个供应链系统的绩效的影响。具体地,假设零售销售期由两周期组成,我们讨论当零售商对订购后容量相对固定的产品在两个销售周期进行优化分配时,零售商基于优化销售的收益管理策略对整个非中心化供应链的订购、批发价决策及供应链绩效所产生的影响。我们建立问题的数学模型,然后对模型进行理论和数值分析,并通过与传统的批发价契约进行比较,发现:(a)对于中心化系统,优化销售策略在降低系统产量的同时,却能提高系统的利润;而对于非中心化供应链系统,零售商的优化销售策略不仅提升自己所占渠道的利润份额,而且与传统的批发价契约相比,还能提升系统的效率和绩效。(b)无论是传统批发价契约还是基于优化销售的批发价契约,两种契约模式都有各自适用的商业环境。如对于价格涨势较大的高端产品市场,优化销售的批发价契约优于传统的批发价契约;而对于价格比较平稳的日常用品等中低端产品市场,传统的批发价契约却比较适合。
总之,本研究除了涉足时尚、易逝、短生命周期的这类产品外,我们还将目光投向了另一类珍稀高端的非易逝产品。源于后一类产品的特性并结合零售实践,本文提出一些以往文献都未曾触及的问题,并通过模型分析和数值分析得到一些有意义的结果和管理洞见。
In retail, as a result of shorter life cycle of products and increasing uncertainty on demands, more and more companies are forced to deal with business issues such as stock-out and mark-down. In addition, the developments of information technology, and the characteristics of cost structure in retail provide a key support on data for the quantitative analysis of decision-making and management in this industry. Thus, the research on revenue management and supply chain management in retail practice is more and more concerned by people. However, as the company's objective of optimizing earnings will be progressively transformed from short-term to long-term, and decisions-making in management practice are usually based on discrete time, so the promotion on existing reserch on revenue management and supply chain management will be imperative along the dimension of time(from continuous time or single period to multi-period). Therefore, based on the retail background, for the shortcomings in existing literature, along time (Period) and space (Stage) dimensions, in this thesis we attempt to make quantitative analysis of some multi-period problems in revenue management and supply chain management.
1) Multi-Period Problems in Revenue Management.
In this part, we focus on the issues on pricing and inventory control in revenue management from the perspective of the dealers (retailers), according to different characteristics of the products, in particular the attributes of demands.
First, we consider the problem of choosing optimal batch selling quantities for a class of seasonal items with fixed capacity under random price-sensitive demand. Where, the policies of capacity allocation over two-period are discussed for this kind of seasonal products, and impacts of the market parameters on the optimal decision-making and the optimal revenue value are studied through numerical analysis. In view of theoretical and numerical results, we find that the optimal batch quantities set to sell are non-decreasing in current price and inventory on hand, and are non-increasing in the number of sales periods remaining under random price-sensitive demand.
Second, in retail practice, large piles of some items are displayed in supermarkets for sale, and some items with limited-quantities are put for sale. According to this phenomenon, we are the first time to introduce the stock-dependent demand into the research area of revenue management. On this basis, we address the combined periodical pricing and capacity allocation problem when the demands in any period depend randomly, in a general form, on the price and starting inventory level on display. We formulate a MDP model for the problem, find an analytic solution for the single–period model while the demand function is increasing in starting inventory level on display and is dependent of price in a power form. Suppose that the inventory to be set aside for the future in each period is determined at the beginning of the horizon, we identify that the optimal policy for periodical pricing problem is myopic and unique. Finally, we develop structural properties. The main results are as follows:(a)While the inventory level displayed has a negative influence on demand, for some valuable goods, the clearance sales policy is not necessarily considered at the end of sales horizon. Since too many inventories on display may not benefit to pricing and mark-down may do harm to the development of brand, the policy with high-level price and low–quantity sales is generally adopted by the sellers who sell those valuable goods. (b)The unique optimal price of multi-period pricing problem exists for a given capacity allocation schedule. (c)The recourse in pricing usually results in better sales performance, and is more advisable for those goods with low-level price elasticity, high-level risk and limited-amount supply.
Finally, we are the first time to define and describe this type of products with fixed capacity in sales horizon and non-perishable properties, and discuss the problems such as their initial inventory control, the amount preset to sell for current period and the choice of selling time. More specifically, we suppose that the demand in each period is an independent random variable with known distribution function, and the price is determined exogenously and fluctuates according to AR process. We develop finite and infinite horizon models with the objective of maximizing total expected discounted revenue, discuss existence of the optimal sales policy and characterize structural properties of its, establish a practical and efficient algorithm for computing the optimal batch quantities and the optimal revenue. Finally, we examine the analytic results and characterize how system parameters affect the optimal policy and corresponding optimal revenue value through a series of numerical experiments. In addition, we offer some managerial insights as to how long the sales horizon should be chose to last for managers. The main results are as follows: (a)Under the higher demand uncertainty, those sellers who allocate rationally the inventory held on hand at each period are able to snatch more profits. However, the increment of revenue resulted from prolonging the sales horizon is not obvious under moderate demand variation, to take the quick–break sales policy is advisable allowing for the opportunity costs of inventory capital. (b)The lower the holding costs and the greater the trends of mark-up, the more the optimal quantities preserved for future periods and the initial stock; in addition, the quantities preset for the current period are non-increasing in the current price. (c)Different from the traditional problem of capacity allocation in revenue management, from a non-perishable perspective, the seller may arbitrarily choose the number of sales period. The greater the demand variation and the higher the price and the larger range of price rising and the lower the holding costs are, the longer the sales horizon should be considered to last.
2) Multi-Period Problems in Supply Chain Management.
In this part, we investigate decisions-making (ordering, pricing, capacity allocation) for channel members and their impacts on supply chain performance under the framework of supply chain based on information-sharing and contractual mechanism.
First, we study two main mechanisms which coordinate supply chain system, i.e, information-sharing and supply chain contract. In the beginning, the theories of information-sharing mechanism under two different demands (independent demand and correlated demand)are outlined; next, the theories of supply chain contract are illustrated based on two different research methods (optimization, game theory) ; on this basis, the progress of integrated research on these two coordination mechanisms are then disserted. The main results are as follows: Since both information-sharing and contracts are effective mechanisms to coordinate supply chain system, in practice, to optimize or to integrate these two mechanisms will undoubtedly enhance the potential benefits of channel members. In a sense, the type of contract established among the members basically defines the relationships each other. Whether to share information, with whom to share, to what extent to share, such issues are dependent on close degree of these relationships. Therefore, the issue of information-sharing among the members should also be included in the scope of establishment of contract, rather than be treated separately.
Second, relying on the wholesale price contract, we consider influences of some strategies of downstream retailer's revenue management on the performances of upstream suppliers and whole supply chain system. Specifically, suppose that the sales horizon consists of two perods, we discuss the impacts of retailer's optimizing allocation for the fixed capacity over two periods on order, wholesale price and performances of decenteralized supply chain. We begin with establishing a mathematical model of the problem, and then make theoretical and numerical analysis of the model, and through comparing with the traditional wholesale price contract we find that: (a)For centeralized system, optimizing selling strategies contibute to reduce the output of the system, at the same time, increase the profits of the system; and for decenteralized supply chain system, retailer’s optimizing selling strategies enhance not only his own share of the profits of the channels, but also the efficiencies and performances of system comparing with the traditional wholesale price contract. (b)Whether the traditional wholesale price contract or the wholesale price contract based on the sales optimization, there exist their own suitable business environments. For high-end products with larger margin of prices rising, the wholesale price contract based on the sales optimization is superior to the traditional wholesale price contract; and for daily necessities with more stable prices, in such low-end products market, the traditional wholesale price contract is more suitable for being applied.
In conclusion, in addition to fashion, perishable products with short life cycle, this study also set foot the other rare and high-end non-perishable products. Stem from the characteristics of the latter and combine with the retail practice, in specific study of this thesis, we propose a few of problems not involved in previous literature, and obtain some meaningful results and management insights through modeling analysis and numerical experiments.
1)多阶段收益管理问题
在这一部分,我们主要从经销商(零售商)的角度,研究相应于不同特性的产品,尤其是不同需求属性的产品的定价和库存控制等收益管理问题。
首先,我们研究价格敏感的随机需求产品分批销售时的数量优化问题。其中将着重讨论销售期容量固定的季节性产品在两阶段的容量分配策略,并将借助数值分析研究市场参数对最优策略和最优收益值的影响。结合理论和数值分析结果,我们发现:在随机且价格敏感的需求下,产品分批销售时的最优预售数量随当前价格和剩余产品的数量递增,而随剩余的销售时间递减。
其次,在零售实践中,经常见到有些商品在超市被大堆展示销售而有些商品却被分批限量销售。根据这种现象,我们首次在收益管理研究领域中引入需求依赖库存水平的情形,并在此基础上研究当需求同时依赖于价格和库存展示水平时,有关产品的动态定价和容量分配问题。我们建立问题的MDP模型;然后针对模型的几种特殊情况,分别获得单阶段模型的解析解及每阶段的预留数量(即不被拿来展示预售的产品数量)事先确定时的多阶段模型的解的存在唯一性;同时讨论最优解的结构性质。其中主要结果有:(a)对于过多的库存展示数量会给需求带来负面影响的高档商品,即使在销售季节后期也很少会出现降价抛售的现象;同时,我们的模型分析表明对于这类商品采用高价低销量的营销策略优于薄利多销的策略。(b)对于产品在各阶段的预留计划在一开始时就确定的情形,存在唯一的最优定价策略。(c)柔性定价策略都可提高收益,且动态定价尤其适合那些价格弹性小、市场需求变动大和库存持有量少的高端产品。
最后,我们首次从概念上对容量固定且无时效限制的这类产品进行界定,并针对这类产品的初始订购及其销售数量和销售时间等问题进行探讨。具体地,假定每阶段的需求为相互独立且分布已知的随机变量,同时价格服从随机游走的自回归过程,在考虑使经销商的期望折扣销售收益达到最大的目标下,我们分别建立关于该问题的有限阶段和无限阶段优化模型;获得模型的最优解并讨论解的有关性质;而后我们给出有限阶段模型的数值求解方法并在此基础上通过数值分析进一步验证了最优解的结构性质,描述几个系统参数对最优策略及相应最优收益值的影响,同时还给出经销商关于销售期数选择的一些管理洞见。其中获得以下三个主要结果:(a)需求变动越大,越有必要对库存在各销售阶段进行合理分配;当需求变动适中时,由于随着销售期的延长,收益的增长并不是很明显,考虑到资金的机会成本,速战速决的销售策略比较可取。(b)持有成本愈低,价格涨势愈大,则初始订购越多且预留的产品愈多;此外,当前价格越高,拿来预售的产品数量却越少。(c)不同于传统的收益管理问题,由于产品的无时效限制使得经销商可自由选择销售期,从而当需求变动越大,价格越高,价格增长幅度越大及成本越低时,销售时间可持续越长。
2)多阶段供应链管理问题
这一部分,我们将在供应链框架下基于信息共享和契约机制考虑渠道成员的决策(订购、定价、容量分配)及其对供应链绩效的影响。
首先,我们研究信息共享和供应链契约这两种协调供应链系统的主要机制。一开始就两种不同需求(独立需求和相关需求)环境下的有关信息共享机制理论进行概述;接下来分别阐述基于两种不同研究方法(优化方法,博弈论方法)的供应链契约理论;然后在此基础上,论述两种协调机制集成研究理论的进展。得到的结论有:既然信息共享和供应链契约都为协调供应链系统的有效机制,那么在实践中对两者的集成或优化整合定会给系统成员带来潜在利益的提升。从某种意义上说,成员之间所缔结的契约类型基本上界定了彼此的关系,而像信息需不需要共享、与谁共享、共享到什么程度等此类问题都依赖于这种关系的密切程度。所以,有关成员之间信息共享的问题也可纳入缔结契约内容的范畴,而不是割裂地对它们分别处理。
其次,依托批发价契约,我们首次考虑下游零售商的收益管理策略对上游企业及整个供应链系统的绩效的影响。具体地,假设零售销售期由两周期组成,我们讨论当零售商对订购后容量相对固定的产品在两个销售周期进行优化分配时,零售商基于优化销售的收益管理策略对整个非中心化供应链的订购、批发价决策及供应链绩效所产生的影响。我们建立问题的数学模型,然后对模型进行理论和数值分析,并通过与传统的批发价契约进行比较,发现:(a)对于中心化系统,优化销售策略在降低系统产量的同时,却能提高系统的利润;而对于非中心化供应链系统,零售商的优化销售策略不仅提升自己所占渠道的利润份额,而且与传统的批发价契约相比,还能提升系统的效率和绩效。(b)无论是传统批发价契约还是基于优化销售的批发价契约,两种契约模式都有各自适用的商业环境。如对于价格涨势较大的高端产品市场,优化销售的批发价契约优于传统的批发价契约;而对于价格比较平稳的日常用品等中低端产品市场,传统的批发价契约却比较适合。
总之,本研究除了涉足时尚、易逝、短生命周期的这类产品外,我们还将目光投向了另一类珍稀高端的非易逝产品。源于后一类产品的特性并结合零售实践,本文提出一些以往文献都未曾触及的问题,并通过模型分析和数值分析得到一些有意义的结果和管理洞见。
In retail, as a result of shorter life cycle of products and increasing uncertainty on demands, more and more companies are forced to deal with business issues such as stock-out and mark-down. In addition, the developments of information technology, and the characteristics of cost structure in retail provide a key support on data for the quantitative analysis of decision-making and management in this industry. Thus, the research on revenue management and supply chain management in retail practice is more and more concerned by people. However, as the company's objective of optimizing earnings will be progressively transformed from short-term to long-term, and decisions-making in management practice are usually based on discrete time, so the promotion on existing reserch on revenue management and supply chain management will be imperative along the dimension of time(from continuous time or single period to multi-period). Therefore, based on the retail background, for the shortcomings in existing literature, along time (Period) and space (Stage) dimensions, in this thesis we attempt to make quantitative analysis of some multi-period problems in revenue management and supply chain management.
1) Multi-Period Problems in Revenue Management.
In this part, we focus on the issues on pricing and inventory control in revenue management from the perspective of the dealers (retailers), according to different characteristics of the products, in particular the attributes of demands.
First, we consider the problem of choosing optimal batch selling quantities for a class of seasonal items with fixed capacity under random price-sensitive demand. Where, the policies of capacity allocation over two-period are discussed for this kind of seasonal products, and impacts of the market parameters on the optimal decision-making and the optimal revenue value are studied through numerical analysis. In view of theoretical and numerical results, we find that the optimal batch quantities set to sell are non-decreasing in current price and inventory on hand, and are non-increasing in the number of sales periods remaining under random price-sensitive demand.
Second, in retail practice, large piles of some items are displayed in supermarkets for sale, and some items with limited-quantities are put for sale. According to this phenomenon, we are the first time to introduce the stock-dependent demand into the research area of revenue management. On this basis, we address the combined periodical pricing and capacity allocation problem when the demands in any period depend randomly, in a general form, on the price and starting inventory level on display. We formulate a MDP model for the problem, find an analytic solution for the single–period model while the demand function is increasing in starting inventory level on display and is dependent of price in a power form. Suppose that the inventory to be set aside for the future in each period is determined at the beginning of the horizon, we identify that the optimal policy for periodical pricing problem is myopic and unique. Finally, we develop structural properties. The main results are as follows:(a)While the inventory level displayed has a negative influence on demand, for some valuable goods, the clearance sales policy is not necessarily considered at the end of sales horizon. Since too many inventories on display may not benefit to pricing and mark-down may do harm to the development of brand, the policy with high-level price and low–quantity sales is generally adopted by the sellers who sell those valuable goods. (b)The unique optimal price of multi-period pricing problem exists for a given capacity allocation schedule. (c)The recourse in pricing usually results in better sales performance, and is more advisable for those goods with low-level price elasticity, high-level risk and limited-amount supply.
Finally, we are the first time to define and describe this type of products with fixed capacity in sales horizon and non-perishable properties, and discuss the problems such as their initial inventory control, the amount preset to sell for current period and the choice of selling time. More specifically, we suppose that the demand in each period is an independent random variable with known distribution function, and the price is determined exogenously and fluctuates according to AR process. We develop finite and infinite horizon models with the objective of maximizing total expected discounted revenue, discuss existence of the optimal sales policy and characterize structural properties of its, establish a practical and efficient algorithm for computing the optimal batch quantities and the optimal revenue. Finally, we examine the analytic results and characterize how system parameters affect the optimal policy and corresponding optimal revenue value through a series of numerical experiments. In addition, we offer some managerial insights as to how long the sales horizon should be chose to last for managers. The main results are as follows: (a)Under the higher demand uncertainty, those sellers who allocate rationally the inventory held on hand at each period are able to snatch more profits. However, the increment of revenue resulted from prolonging the sales horizon is not obvious under moderate demand variation, to take the quick–break sales policy is advisable allowing for the opportunity costs of inventory capital. (b)The lower the holding costs and the greater the trends of mark-up, the more the optimal quantities preserved for future periods and the initial stock; in addition, the quantities preset for the current period are non-increasing in the current price. (c)Different from the traditional problem of capacity allocation in revenue management, from a non-perishable perspective, the seller may arbitrarily choose the number of sales period. The greater the demand variation and the higher the price and the larger range of price rising and the lower the holding costs are, the longer the sales horizon should be considered to last.
2) Multi-Period Problems in Supply Chain Management.
In this part, we investigate decisions-making (ordering, pricing, capacity allocation) for channel members and their impacts on supply chain performance under the framework of supply chain based on information-sharing and contractual mechanism.
First, we study two main mechanisms which coordinate supply chain system, i.e, information-sharing and supply chain contract. In the beginning, the theories of information-sharing mechanism under two different demands (independent demand and correlated demand)are outlined; next, the theories of supply chain contract are illustrated based on two different research methods (optimization, game theory) ; on this basis, the progress of integrated research on these two coordination mechanisms are then disserted. The main results are as follows: Since both information-sharing and contracts are effective mechanisms to coordinate supply chain system, in practice, to optimize or to integrate these two mechanisms will undoubtedly enhance the potential benefits of channel members. In a sense, the type of contract established among the members basically defines the relationships each other. Whether to share information, with whom to share, to what extent to share, such issues are dependent on close degree of these relationships. Therefore, the issue of information-sharing among the members should also be included in the scope of establishment of contract, rather than be treated separately.
Second, relying on the wholesale price contract, we consider influences of some strategies of downstream retailer's revenue management on the performances of upstream suppliers and whole supply chain system. Specifically, suppose that the sales horizon consists of two perods, we discuss the impacts of retailer's optimizing allocation for the fixed capacity over two periods on order, wholesale price and performances of decenteralized supply chain. We begin with establishing a mathematical model of the problem, and then make theoretical and numerical analysis of the model, and through comparing with the traditional wholesale price contract we find that: (a)For centeralized system, optimizing selling strategies contibute to reduce the output of the system, at the same time, increase the profits of the system; and for decenteralized supply chain system, retailer’s optimizing selling strategies enhance not only his own share of the profits of the channels, but also the efficiencies and performances of system comparing with the traditional wholesale price contract. (b)Whether the traditional wholesale price contract or the wholesale price contract based on the sales optimization, there exist their own suitable business environments. For high-end products with larger margin of prices rising, the wholesale price contract based on the sales optimization is superior to the traditional wholesale price contract; and for daily necessities with more stable prices, in such low-end products market, the traditional wholesale price contract is more suitable for being applied.
In conclusion, in addition to fashion, perishable products with short life cycle, this study also set foot the other rare and high-end non-perishable products. Stem from the characteristics of the latter and combine with the retail practice, in specific study of this thesis, we propose a few of problems not involved in previous literature, and obtain some meaningful results and management insights through modeling analysis and numerical experiments.
引文
[1]施罗德(Schroeder, R.G.)著;韩伯棠等译.《运作管理》[M].北京:中国劳动社会保障出版社, 2004.
[2] Paul H.Zipkin. Foundations of inventory management[M].Co-Singapore McGraw-Hill Book, 2000.
[3] Evan L.Porteus. Foundations of stochastic inventory theory[M]. Stanford, California: Stanford University Press, 2002.
[4]菲利普.科特勒等著;梅清豪译.《市场营销管理》[M].北京:中国人民大学出版社, 2001.
[5] Udell, Jon G. Successful marketing strategies[M]. Madison, Wi :Mimir Publishers, 1972.
[6] Morris, Michael H., Leyland F.Pitt. Do strategy frameworks apply in the United States and abroad? [J], Indust. Marketing Management, 1993; 22:215-221
[7] Tellis, Gerard J. Beyond the many faces of pricing [J], Journal of Marketing, 1986; 50:146-160
[8] Nagle, Thomas, Reed K. Holden. The strategy and tactics of pricing[M]. Englewood Cliffs, NJ: Ind Ed. Prentice Hall, 1995.
[9] Diamanatopoulos, Brianp.Mathews. The specification of pricing objectives: empirical evidence from an ohgopoly firm [J], Managerial and Decision Econom, 1994; 15:73-85
[10] Coe. Strategy in retreat pricing drops out[J], Journal of Bus and Indust. Marketing, 1990; 5(1):5-25
[11] Peter M. Noble, Thomas S. Gruca. Industrial pricing: theory and managerial practice [J], Marketing Science, 1999; 18(3):435-454
[12] Whitin, T.M. Inventory control and price theory [J], Management Science, 1955; 2:61-80
[13] Mills, E.S. Uncertainty and price theory [J],Quart. J. Econom., 1959;73:116-130
[14] Awi Federgruen, Aliza Heching. Combined pricing and inventory control under uncertainty [J], Operations Research, 1999; 47(3):454-475
[15] Chao Xiuli, Sean X. Zhou. Joint inventory-and-pricing strategy for a stochastic continuous-review system [J], IIE Transactions, 2006; 38:401–408
[16] Deng Shiming, Candace A. Yano .Joint production and pricing decisions with set up costs and capacity constraints [J], Management Science, 2006; 52(5): 741–756
[17] Li Qing, Zheng Shaohui. Joint inventory replenishment and pricing control for systems with uncertain yield and demand [J], Operations Research, 2006; 54(4):696-705
[18] Kevin Pak, Nanda Piersma. Overview of or techniques for airline revenue management [J], Statistica Neerlandica, 2002; 56(4):479–495
[19] Jeffrey I.Mcgill, Garrett J.Van Ryzin. Revenue management: research overview and prospects [J], Transportation Science, 1999; 33(2):233-256
[20] K.Littlewood.Forecasting and control of passenger bookings[C]. In AGIFORS Symposium Proc. 12, Nathanya, Israel, 1972.
[21] Gabriel Bitran, Rene Caldentey. An overview of pricing models for revenue management [J], M &SOM, 2003; 5(3):203-229
[22] Michael Z. F. Li, Tae H. Oum. A note on the single leg, multi-fare seat allocation problem [J], Transportation Science, 2002; 36(3):349–353
[23] Conrad J. Lautenbacher, Shaler Stidham, Jr. The underlying markov decision process in the single-leg airline yield-management problem [J], Transportation Science, 1999; 33(2): 136-146
[24] Tak C. Lee, Marvin Hersh. A model for dynamic airline seat inventory control with multiple seat booking [J], Transportation Science, 1993; 27(3): 252-265
[25] Janakiram Subramanian,Shaler Stidham Jr,Conrad J. Lautenbacher. Airline yield management with overbooking, cancellations, and no-shows [J], Transportation Science, 1999; 33(2):147-167
[26] Wen Zhao, Yu-Sheng Zheng. A dynamic model for airline seat allocation with passenger diversion and no-shows [J], Transportation Science, 2001; 35(1):80–98
[27] Dimitris Bertsimas, Sanne de Boer. Simulation-based booking limits for airline revenue management [J], Operations Research, 2005; 53(1):90–106
[28] Guillermo Gallego , Garrett VanRyzin. A multiproduct dynamic pricing problem and its applications to network yield management [J], Operations Research, 1997; 45(1):24-41
[29] Dimitris Bertsimas, Ioana Popescu. Revenue management in a dynamic network environment [J], Transportation Science, 2003; 37(3): 257-277
[30] Itir Karaesmen, Garrett van Ryzin. Overbooking with substitutable inventory classes [J], Operations Research, 2004; 52(1): 83–104
[31] Serguei Netessine , Robert A.Shumsky. Revenue management games: horizontal and vertical competition [J], Management Science, 2005; 51(5):813–831
[32] Feng Youyi, Xiao Baichun. A dynamic airline seat inventory control model and its optimal policy [J], Operations Research, 2001; 49(6):938-949
[33] Arvind Rajan, Rakesh, Richard Steinberg. Dynamic pricing and ordering decisions by a monopolist [J], Management Science, 1992; 38(2):240-262
[34] You Pengheng. Dynamic pricing in airline seat management for flights with multiple flight legs [J], Transportation Science, 1999; 33(2): 192-206
[35] Serguei Netessine, Sergei Savin, Wenqiang Xiao. Revenue management through dynamic cross selling in e-commerce retailing [J], Operations Research, 2006; 54(5): 893–913
[36] Qing Ding, Panos Kouvelis, Joseph M. Milner. Dynamic pricing through discounts for optimizing multiple-class demand fulfillment [J], Operations Research, 2006; 54(1):169–183
[37] Constantinos Maglaras, Joern Meissner. Dynamic pricing strategies for multiproduct revenue management problems [J], M & SOM, 2006;8(2):136–148
[38] Cathy H. Xia, Parijat Dube. Dynamic pricing in e-services under demand Uncertainty [J], Production and Operations Management, 2007; 16(6):701–712
[39] Constantinos Maglaras, Assaf Zeevi. Pricing and design of differentiated services:approximate analysis and structural insights[J],Operations Research, 2005; 53(2):242–262
[40] Constantinos Maglaras. Revenue management for a multiclass single-server queue via a fluid model analysis[J],Operations Research, 2006; 54(5):914–932
[41] Yossi Aviv, Amit Pazgal. A partially observed markov decision process for dynamic pricing [J], Management Science, 2005; 51(9):1400–1416
[42] Somnath Mukhopadhyay,Subhashish Samaddar,Glenn Colville. Improving revenue management decision making for airlines by evaluating analyst-adjusted passenger demand forecasts [J], Decision Sciences, 2007; 38 (2):309-327
[43] Xiaowei Xu, Wallace J. Hopp. A monopolistic and oligopolistic stochastic flow revenue management model [J], Operations Research, 2006; 54(6):1098–1109
[44] Georgia Perakis, Anshul Sood. Competitive multi-period pricing for perishable products: a robust optimization approach[J], Math. Program, Ser. B, 2006; 107:295-335
[45] Beat Burger, Matthias Fuchs. Dynamic pricing-- A future airline business model [J], Journal of Revenue and Pricing Management, 2005; 4(1): 39–53
[46] Yuri Levin, Jeff McGill, Mikhail Nediak. Dynamic pricing in the presence of strategic consumers and oligopolistic competition [J], Management Science, 2009; 55(1):32–46
[47] Gérard P. Cachon, Robert Swinney. Purchasing, pricing, and quick response in the presence of strategic consumers [J], Management Science, 2009; 55(3):497-511
[48] Max Shen Zuo-Jun, Su Xuanming. Customer behavior modeling in revenue management and auctions: a review and new research opportunities[J],Production and Operations Management , 2007; 16(6):713–728
[49] Dan Zhang, William L. Cooper. Revenue management for parallel flights with customer-choice behavior [J], Operations Research, 2005; 53(3):415–431
[50] John G. Wilson, Chris K. Anderson, Sang-Won Kim. Optimal booking limits in the presence of strategic consumer behavior [J], Intl. Trans. in Op. Res, 2006; 13:99–110
[51] Yossi Aviv, Amit Pazgal. Optimal pricing of seasonal products in the presence of forward-looking consumers [J], M &SOM, 2008; 10(3):339–359
[52] Frank Y. Chen, Jian Chen, Yongbo Xiao. Optimal control of selling channels for an online retailer with cost-per-click payments and seasonal products [J], Production and Operations Management, 2007; 16(3):292–305
[53] RenéCaldentey, Lawrence M.Wein. Revenue management of a make-to-stock queue [J], Operations Research, 2006; 54(5):859–875
[54] Yuri Levin, Jeff McGill, Mikhail Nediak. Price guarantees in dynamic pricing and revenue management [J], Operations Research, 2007; 55(1):75–97
[55] Jérémie Gallien. Dynamic mechanism design for online commerce [J], Operations Research, 2006; 54(2):291–310
[56] William L. Cooper, Diwakar Gupta. Stochastic comparisons in airline revenue management [J], M &SOM, 2006; 8( 3):221–234
[57] Guillermo Gallego,Robert Phillips. Revenue management of flexible products [J], M &SOM, 2004; 6(4):321–337
[58] Apostolos Burnetas, Stephen Gilbert. Future capacity procurements under unknown demand and increasing costs [J], Management Science, 2001; 47(7):979–992
[59]罗利,萧柏春.收益管理理论的研究现状及发展前景[J],管理科学学报,2004; (5):75-83
[60]官振中,陈旭,卜祥智,史本山.易逝性高科技产品收益管理资源分配及定价策略[J],系统工程, 2006; (1):23-29
[61]卜祥智,赵泉午,陈荣秋.基于收益管理的海运集装箱舱位分配与路径选择优化模型[J] ,管理工程学报, 2008; (3):94-99
[62]罗利,彭际华.竞争环境下的民航客运收益管理动态定价模型[J],系统工程理论与实践, 2007; (11):15-25
[63]肖勇波,陈剑,刘晓玲.基于乘客选择行为的双航班机票联合动态定价模型[J],系统工程理论与实践, 2008; (1):46-55
[64]魏轶华,胡奇英,夏玉森.有多个销售渠道的连续时间收益管理问题研究[J],中国管理科学, 2008; (2):37-41
[65]张荣,黄学祥.成本与需求约束下的不对称电力竞价博弈[J] .系统工程理论与实践, 2009, (1):37-43
[66] Sridhar Tayur, Ram Ganeshan, Michael Magazine. Quantitative models for supply chain management [M]. Boston: Kluwer Academic Publishers, 1999.
[67] Clark, A.J. Scarf, H. Optimal policies for a multiechelon inventory problem [J], Management Science, 1960; 6:475-650
[68] Saibal Ray, Shanling Li, Yuyue Song. Tailored supply chain decision making under price-sensitive stochastic demand and delivery uncertainty [J], Management Science, 2005; 51 (12):1873–1891
[69] Serguei Netessine, Zhang Fuqiang. Positive vs. negative externalities in inventory management: implications for supply chain design[J], M & SOM, 2005; 7(1):58–73
[70] Serguei Netessine, Nils Rudi. Supply chain choice on the internet [J], Management Science, 2006; 52(6):844–864
[71] Fernando Bernstein, Awi Federgruen. Decentralized supply chains with competing retailers under demand uncertainty[J], Management Science, 2005; 51( 1):18–29
[72] Fernando Bernstein,Gregory A. DeCroix,Yulan Wang. Incentives and commonality in a decentralized multiproduct assembly system [J], Operations Research, 2007; 55(4):630–646
[73] Andy A.Tsay. Managing retail channel overstock: markdown money and return policies [J], Journal of Retailing, 2001; 77:457-492
[74] Yigal Gerchak, Yunzeng Wang. Revenue-sharing vs. wholesale-price contracts in assembly systems with random demand [J], Production and Operations Management 2004; 13(1):23–33
[75] Christos Koulamas. A newsvendor problem with revenue sharing and channel coordination [J], Decision Sciences, 2006; 37 (1):91-100
[76] Apostolos Burnetas, Stephen M. Gilbert, Craig E. Smith. Quantity discounts in single-period supply contracts with asymmetric demand information [J], IIE Transactions, 2007; 39:465–479
[77] Dong Lingxiu, Liu Hong. Equilibrium forward contracts on nonstorable commodities in the presence of market power[J],Operations Research, 2007; 55(1):128–145
[78] Fernando Bernstein,Francis de Véricourt. Competition for procurement contracts with service guarantees [J], Operations Research, 2008; 56(3):562–575
[79] Yunzeng Wang. Joint pricing-production decisions in supply chains of complementary products with uncertain demand [J], Operations Research, 2006; 54(6):1110–1127
[80] Daniel Granot, Shuya Yin. Price and order postponement in a decentralized newsvendor model with multiplicative and price-dependent demand[J], Operations Research, 2008; 56( 1):121–139
[81] Song Yuyue, Saibal Ray, Li Shanling. Structural properties of buyback contracts for price-setting newsvendors [J], M &SOM, 2008; 10(1):1–18
[82] Sirong Luo, Metin Cakanyildirim. Pricing and production games under revenue sharing and information updates[J], Journal of Revenue and Pricing Management, 2005; 4(3):370-301
[83] Li Lode, Zhang Hongtao. Confidentiality and information sharing in supply chain coordination [J], Management Science, 2008; 54(8):1467–1481
[84] Chung-Chi Hsieh, Wu Cheng-Han. Capacity allocation, ordering, and pricing decisions in a supply chain with demand and supply uncertainties[J], EuropeanJournal of Operational Research ,2008; 184:667–684
[85] Tamer Boyaci, Guillermo Gallego. Supply chain coordination in a market with customer service competition [J], Production and Operations Management , 2004;13(1): 3–22
[86] Birendra K. Mishra, Srinivasan Raghunathan, Yue Xiaohang. Information sharing in supply chains: Incentives for information distortion[J] , IIE Transactions,2007; 39:863–877
[87] Christopher S. Tang. Perspectives in supply chain risk management [J], Int. J. Production Economics, 2006; 103:451–488
[88] Shantanu Bhattacharya, V. Daniel R. Guide, Jr., Luk N. Van Wassenhove. Optimal order quantities with remanufacturing across new product generations [J], Production and Operations Management, 2006; 15(3):421–431
[89] Su Xuanming, Zhang Fuqiang. Strategic customer behavior, commitment, and supply chain performance[J], Management Science, 2008; 54(10):1759-1773
[90] Panos Kouvelis, Chester Chambers, Haiyan Wang. Supply chain management research and production and operations management: review, trends, and opportunities [J], Production and Operations Management, 2006; 15(3): 449–469
[91]艾兴政,唐小我,倪得兵.价格上涨环境下供应链的渠道协调机制研究[J],管理科学学报, 2004;(5):24-30
[92]陈志祥.敏捷供应链供需协调绩效关联分析与实证研究[J],管理科学学报,2005;(1):78-87
[93]陈旭.考虑期权合同供应链的零售商订货研究[J],管理科学学报, 2006;(3) :7-23
[94]宋华明,马士华.二阶段供应链中提前期压缩的影响与协调[J],管理科学学报,2007;(1) :46-53
[95]邵晓峰,季建华.基于补偿合约的供应链定价与能力设计的协调问题研究[J],中国管理科学, 2008;(4) :62-68
[96]黄祖庆,易荣华,达庆利.第三方负责回收的再制造闭环供应链决策结构的效率分析[J] ,中国管理科学, 2008;(3):73-77
[97]李丽君,黄小原,庄新田.双边道德风险条件下供应链的质量控制策略[J],管理科学学报, 2005;(1) :42-47
[98]吴军,李健,汪寿阳.供应链风险管理中的几个重要问题[J],管理科学学报,2006;(6) :1-12
[99]周永务,杨善林.基于不对称需求信息的供应链协调定价[J],系统工程学报,2006;(6):591-597
[100]熊中楷,李根道,唐彦昌,李薇.网络环境下考虑动态定价的渠道协调问题研究[J],管理工程学报,2007;(3):49-55
[101]谢金星,王淼.两级供应链中基于提前订货折扣的博弈[J],系统工程学报,2008;(2):181-187
[102]张菊亮,陈剑.供应商管理库存应对突发事件[J],中国管理科学, 2008;(5):71-76
[103] Gabriel Bitran, Rene Caldenity, Susanaa Mondschen. Coordinating clearance markdown sales of seasonal products in retail chains [J], Operations Research, 1998; 46(5):609-624
[104] Phi Hoang. The future of revenue management and pricing science [J], Journal of Revenue and Pricing Management, 2007; 6(2):151-153
[105] Zhao Wen, Wang Yunzeng. Coordination of joint pricing-production decision in a supply chain [J], IIE Transactions, 2002; 34:701-715
[106] Zou Xuxia, Shaligram Pokharel, Rajesh Piplani. A two-period supply contract modelfor a decentralized assembly system [J], European Journal of Operational Research 2008; 187: 257–274
[107] Wayne S. Desarbo, Vithala R.Rao, Joel H.Steckel, Jerry Wind, Richard Colombo. A friction model for describing and forecasting price changes[J], Marketing Science,1987; 6(4):299-319
[108] Conrad J.Lautenbacher, Shaler Stidham, JR. The underlying markov decision process in the single-leg airline yield management problem [J]. Transportation Science, 1999; 33(2):136-146
[109] Kalyan Talluri, Garrett Van Ryzin. Revenue management under a general discrete choice model of consumer behavior [J], Management Science, 2004; 50(1):15-33
[110] Youyi Feng, Baichun Xiao. Optimal policies of yield management with multiple predetermined prices [J], Operations Research, 2000; 48(2):332-343
[111] Gabriel R.Bitran, Susana V.Mondschein. Periodic pricing of seasonal products in retailing [J], Management Science, 1997; 43(1):64-78
[112] Stephen A.Smith, Dale D.Achabal. Clearance pricing and inventory policies for retail chains [J], Management Science, 1998; 44(3):285-300
[113] George E.Monahan, Nicholas C.Petruzzi.,Wen Zhao.The Dynamic pricing problem from a newsvendor’s perspective[J], Manufacturing & Service Operations Management , 2004; 6(1):73-91
[114]胡奇英,刘建庸.《马尔可夫决策引论》[M].西安:西安电子科技大学出版社,2000.
[115] M. Corstjens, P. Doyle. A model for optimizing retail space allocation [J], Management Science, 1981; 27:822-833
[116] Silver, E. A. Operations research and inventory management [J], Operations Research 1981; 29:628-645
[117] Timothy L.Urban. Inventory models with the demand rate dependent on stock and shortage levels [J], International Journal of Production Economics, 1995; 40:21-28
[118] TimothyL.Urban. A periodic-review model with serially-correlated, inventory-level-dependent demand [J], International Journal of Production Economics, 2005; 95:287-295
[119] Yigal Gerchak, Wang Yunzeng. Periodic-review inventory models with inventory-level-dependent demand [J], Naval Research Logistics, 1994; 41:99-116
[120] Khmelnitsky,E., Gerchak,Y. Optimal control approach to production systems with inventory-level- dependent demand[J], IEEE Transactions on AutomaticControl, 2002; 47(2):289-292
[121] Zhou, Y.W., Yang, S.L. An optimal replenishment policy for items with inventory-level-dependent demand and fixed lifetime under the LIFO policy [J], Journal of the Operational Research Society, 2003; 54(6):585-593
[122] Timothy L.Urban. Inventory models with inventory-level-dependent demand: a comprehensive review and unifying theory [J], European Journal of Operational Research, 2005; 162:792-804
[123] Teng Jinn-Tsair, Chang Chun-Tao. Economic production quantity model for deteriorating items with price- and stock-dependent demand[J], Computers & Operations Research, 2005; 32:297-308
[124] Datta Dk, Paul K. An inventory system with stock-dependent, price-sensitive demand rate [J], Production Planning and Control, 2001; 12:13-20
[125] Wedad Elmaghraby, Pinar Keskinocak. Dynamic pricing in the presence of inventory considerations: research overview, current practices, and future directions [J], Management Science, 2003; 49(10):1287-1309
[126] Karlin, Samuel. Dynamic inventory with varying stochastic demands [J], Management Science, 1960; 6(3):231-258
[127] Guillermo Gallego, Garrett van Ryzin. Optimal dynamic pricing of inventories with stochastic demand over finite horizons [J], Management Science, 1994; 40(8):999-1020
[128] Zhao.W., Zheng.Y. Optimal dynamic pricing for perishable assets [J], Management Science, 2000; 46:375-388
[129] Su Xuanming. Intertemporal pricing with strategic customer behavior [J], Management Science, 2007; 53(5):726-741
[130] lap Mui Ann Chan, David Simchi-Levi, Julie Swann. Pricing, production, and inventory policies for manufacturing with stochastic demand and discretionary sales [J], Manufacturing & Service Operations Management, 2006; 8(2):149-168
[131] Dimitris Bertsimas, Sanne de Boer. Dynamic pricing and inventory control formuitiple products [J], Journal of Revenue and Pricing Management, 2005; 3(4):303-319
[132] Intriligator, M. D. Econometric models, techniques, and applications[M]. Prentice Hall, Englewood Cliffs, NJ. 1978.
[133] Russell, Gary J., Ruth N. Bolton. Implications of market structure for elasticity structure [J], Journal of Marketing Research, 1988; 25(3):229-241
[134] Kalyanam, Kirthi. Pricing decisions under demand uncertainty: a bayesian mixture model approach [J], Marketing Science, 1996; 15(3):207-221
[135] Martin A. Lariviere, Evan L.Porteus. Selling to the newsvendor: an analysis of price- only contracts [J], Manufacturing & Service Operations Management, 2001; 3 (4):293-305
[136] Karlin,S., C.R..Carr. Prices and optimal inventory policies[M]. Stanford, CA: Stanford University Press, 1962.
[137] Chen Shaoxiang. The infinite horizon periodic review problem with setup costs and capacity constraints: a partial characterization of the optimal policy [J], Operations Research, 2004; 52(3):409-421
[138] Hakan Polatoglu, Izzet Sahin. Optimal procurement policies under price-dependent demand [J], International Journal of Production Economics 2000; 65:141-171
[139] Haresh Gurnani, Christopher S.Tang. Note: optimal ordering decisions with uncertain cost and demand forecast updating [J], Management Science, 1999; 45(10):1456-1462
[140] Hongyan Huang, Suresh P.Sethi, Houmin Yan. Purchase contract management with demand forecast updates [J], IIE Transactions, 2005; 37:775-785
[141] Wu Jianghua. Quantity flexibility contracts under bayesian updating [J], Computers & Operations Research, 2005; 32:1267-1288
[142] Karen L. Donohue. Efficient supply contracts for fashion goods with forecast updating and two production modes [J], Management Science, 2000; 46(11):1397-1411
[143] Elizabeth Junqueira Durango-Cohen, Candace Arai Yano. Supplier commitment and production decisions under a forecast-commitment contract [J], Management Science, 2006; 52(1):54-67
[144] Lawrence W. Robinson. Optimal and approximate control policies for airline booking with sequential nonmonotonic fare classes [J], Operations Research, 1995; 43(2):252-263
[145] Peter P.Belobaba. Airline yield management: an overview of seat inventory control [J], Transportation Science, 1987; 21(2):63-73
[146] William L.Cooper. Asymptotic behavior of an allocation policy for revenue management [J], Operations Research, 2002; 50(4):720-727
[147] Peter P. Belobaba. Application of a probabilistic decision model to airline seat inventory control [J], Operations Research, 1989; 37(2):183-197
[148] Li Du, Qiying Hu, Wuyi Yue. Analysis and evaluation for optimal allocation in sequential internet auction systems with reserve price[J], Dynamics of Continuous .Discrete and Impulsive Systems .Series B: Applications & Algorithms,2005; 12:617-631
[149] Milgrom. P., Roberts. J. Communication and inventories as substitutes in organizing production [J], Scandanavian J. Econom., 1988; 90:275-289
[150] Lee, H. L., K. C. So, S.Tang. The value of information sharing in a two-level supply chain [J], Management Science, 2000; 46:626-643
[151] Yossi Aviv. The effect of collaborative forecasting on supply chain performance [J], Management Science, 2001; 47(10):1326-1343
[152] Gerard P. Cachon, Marshall Fisher. Supply chain inventory management and the value of shared information [J], Management Science, 2000; 46 (8):1032-1048
[153] Zhu Kaijie, Ulrich W.Thonemann. Modeling the benefits of sharing future demand information [J], Operations Research, 2004; 52(1):136-147
[154] Huang Boray, Seyed M.R. Iravani. Production control policies in supply chains with selective-information sharing [J], Operations Research, 2005;53(4):662-674
[155] Henriott, L. Transforming supply chains into e-chains [J]. Supply Chain Management Rev., Special Global Supplement (spring),1999; 5-18
[156] Susan Cohen kulp, Hau L.Lee, Elie ofek. Manufacturer benefits from information integration with retail customers [J], Management Science, 2004; 50(4):431-444
[157] Gavirneni.S., R .Kapuscinski, S.Tayur. Value of information in capacitated supply chains [J], Management Science, 1999; 45(1):16-24
[158]王瑛.供应链伙伴信息共享的博弈与激励[J],中国管理科学, 2005; 13(5): 61-66
[159] Pasternack B.A. Optimal pricing and returns policies for perishable commodities [J], Marketing Science, 1985; 4(4):166-176
[160] Gerarad. P. Cachon. Handbooks in Operations research and Management Science: Supply Chain Management [M]. Edited by Steve Graves and Tonde Kok and Published by North-Holland, 2003.
[161] Andy A.Tsay The quantity flexibility contract and supplier-customer incentives [J], Management Science, 1999; 45(10):1339-1358
[162] Gerard P. Cachon, Martin A.Lariviere. Supply chain coordination with revenue-sharing contracts: strengths and limitations [J], Management Science, 2005; 51(1):30-44
[163] Charles J.corbett, Gregory A.Decroix. Shared-savings contracts for indirect materials in supply chains: channel profits and environmental impacts[J], Management Science, 2001; 47(7):881-893
[164] Harish Krishnan, Roman Kapuscinski, David A. Butz. Coordinating contracts for decentralized supply chains with retailer promotional effort [J], Management Science, 2004; 50(1):48-63
[165] Wang Yunzeng, Li jiang, Shen Zuo-Jun. Channel performance under consign- ment contract with revenue sharing, Management Science, 2004; 50(1):34-47
[166] Terry A .Taylor, Channel coordination under price protection, midlife returns,and end-of-life returns in dynamic markets [J], Management Science, 2001; 47(9):1220-1234
[167] Gerard P. Cachon. The allocation of inventory risk in a supply-chain: push, pull and advance-purchase discount contracts [J], Management Science, 2004; 50(2): 222-238
[168] Refik Güllü, Geert-Jan Van Houtum, F. Zeynep Surgut, Nesim Erkip. Analysis of a decentralized supply chain under partial cooperation [J], M&SOM, 2005; 7(3):229-247
[169] V. G. Narayanan, Ananth Raman, Jasjit Singh. Agency costs in supply chain with demand uncertainty and price competition [J], Management Science, 2005; 51(1):120-132
[170] Bardia Kamrad, Akhtar Siddique. Supply contracts, profit sharing, switching, and reaction options [J], Management Science, 2004; 50(1):64-82
[171] Apostolos Burnetas, Peter Ritchken. Option pricing with downward-sloping demand curves: the case of supply chain option [J], Management Science, 2005; 51(4): 566-580
[172] Charles J. Corbett, Zhou Deming, Christopher S.Tang. Designing supply contracts: contract type and information asymmetry [J], Management Science, 2004; 50(4):550-559
[173] Stanley Baiman, Paul Z., Fisher Marshel, Madhav V. Rajan. Information contracting and quality costs [J], Management Science, 2000; 46(6):776-789
[174] E. Andrew Boyd, Ioana C.Bilegan. Revenue management and e-commerce[J],Management Science, 2003; 49(10):1363-1386
[175] LevinYuri,Mcgill Jeff,Nediak Mikhail. Risk in revenue management and dynamic pricing [J], Operations Research, 2008; 56 (2):326-343
[176] A.P. Jeuland, M. Shugan. Managing channel profits [J], Marketing Science, 1983; 2(3):239-272
[177] Spengler J. Vertical integration and antitrust policy [J], Journal of Political Economy , 1950; 58(4):347-352
[178] Z. Qin, J.Yang. Analysis of a revenue-sharing contract in supply chain mnagement [J], International Journal of Logistics: Research & Applications, 2008; 11 (1):17-29
[179] Matthew W. McCarter, Gregory B. Northcraft. Happy together? insights and implications of viewing managed supply chains as a social dilemma[J], Journal of Operations Management, 2007; 25:498-511
[180] Tony Haitao Cui, Jagmohan S.Raju, Z.John Zhang. Fairness and channel coordination [J], Management Science, 2007; 53(8):1303-1314
[181] Lingxiu Dong, Kaijie Zhu, Two-wholesale-price contracts: push, pull, and advance-purchase discount contracts [J], M & SOM, 2007;9( 3):291–311
[182] Georgia Perakis, Guillaume Roels. The price of anarchy in supply chains: quantifying the efficiency of price-only contracts [J], Management Science, 2007; 53(8):1249–1268
[2] Paul H.Zipkin. Foundations of inventory management[M].Co-Singapore McGraw-Hill Book, 2000.
[3] Evan L.Porteus. Foundations of stochastic inventory theory[M]. Stanford, California: Stanford University Press, 2002.
[4]菲利普.科特勒等著;梅清豪译.《市场营销管理》[M].北京:中国人民大学出版社, 2001.
[5] Udell, Jon G. Successful marketing strategies[M]. Madison, Wi :Mimir Publishers, 1972.
[6] Morris, Michael H., Leyland F.Pitt. Do strategy frameworks apply in the United States and abroad? [J], Indust. Marketing Management, 1993; 22:215-221
[7] Tellis, Gerard J. Beyond the many faces of pricing [J], Journal of Marketing, 1986; 50:146-160
[8] Nagle, Thomas, Reed K. Holden. The strategy and tactics of pricing[M]. Englewood Cliffs, NJ: Ind Ed. Prentice Hall, 1995.
[9] Diamanatopoulos, Brianp.Mathews. The specification of pricing objectives: empirical evidence from an ohgopoly firm [J], Managerial and Decision Econom, 1994; 15:73-85
[10] Coe. Strategy in retreat pricing drops out[J], Journal of Bus and Indust. Marketing, 1990; 5(1):5-25
[11] Peter M. Noble, Thomas S. Gruca. Industrial pricing: theory and managerial practice [J], Marketing Science, 1999; 18(3):435-454
[12] Whitin, T.M. Inventory control and price theory [J], Management Science, 1955; 2:61-80
[13] Mills, E.S. Uncertainty and price theory [J],Quart. J. Econom., 1959;73:116-130
[14] Awi Federgruen, Aliza Heching. Combined pricing and inventory control under uncertainty [J], Operations Research, 1999; 47(3):454-475
[15] Chao Xiuli, Sean X. Zhou. Joint inventory-and-pricing strategy for a stochastic continuous-review system [J], IIE Transactions, 2006; 38:401–408
[16] Deng Shiming, Candace A. Yano .Joint production and pricing decisions with set up costs and capacity constraints [J], Management Science, 2006; 52(5): 741–756
[17] Li Qing, Zheng Shaohui. Joint inventory replenishment and pricing control for systems with uncertain yield and demand [J], Operations Research, 2006; 54(4):696-705
[18] Kevin Pak, Nanda Piersma. Overview of or techniques for airline revenue management [J], Statistica Neerlandica, 2002; 56(4):479–495
[19] Jeffrey I.Mcgill, Garrett J.Van Ryzin. Revenue management: research overview and prospects [J], Transportation Science, 1999; 33(2):233-256
[20] K.Littlewood.Forecasting and control of passenger bookings[C]. In AGIFORS Symposium Proc. 12, Nathanya, Israel, 1972.
[21] Gabriel Bitran, Rene Caldentey. An overview of pricing models for revenue management [J], M &SOM, 2003; 5(3):203-229
[22] Michael Z. F. Li, Tae H. Oum. A note on the single leg, multi-fare seat allocation problem [J], Transportation Science, 2002; 36(3):349–353
[23] Conrad J. Lautenbacher, Shaler Stidham, Jr. The underlying markov decision process in the single-leg airline yield-management problem [J], Transportation Science, 1999; 33(2): 136-146
[24] Tak C. Lee, Marvin Hersh. A model for dynamic airline seat inventory control with multiple seat booking [J], Transportation Science, 1993; 27(3): 252-265
[25] Janakiram Subramanian,Shaler Stidham Jr,Conrad J. Lautenbacher. Airline yield management with overbooking, cancellations, and no-shows [J], Transportation Science, 1999; 33(2):147-167
[26] Wen Zhao, Yu-Sheng Zheng. A dynamic model for airline seat allocation with passenger diversion and no-shows [J], Transportation Science, 2001; 35(1):80–98
[27] Dimitris Bertsimas, Sanne de Boer. Simulation-based booking limits for airline revenue management [J], Operations Research, 2005; 53(1):90–106
[28] Guillermo Gallego , Garrett VanRyzin. A multiproduct dynamic pricing problem and its applications to network yield management [J], Operations Research, 1997; 45(1):24-41
[29] Dimitris Bertsimas, Ioana Popescu. Revenue management in a dynamic network environment [J], Transportation Science, 2003; 37(3): 257-277
[30] Itir Karaesmen, Garrett van Ryzin. Overbooking with substitutable inventory classes [J], Operations Research, 2004; 52(1): 83–104
[31] Serguei Netessine , Robert A.Shumsky. Revenue management games: horizontal and vertical competition [J], Management Science, 2005; 51(5):813–831
[32] Feng Youyi, Xiao Baichun. A dynamic airline seat inventory control model and its optimal policy [J], Operations Research, 2001; 49(6):938-949
[33] Arvind Rajan, Rakesh, Richard Steinberg. Dynamic pricing and ordering decisions by a monopolist [J], Management Science, 1992; 38(2):240-262
[34] You Pengheng. Dynamic pricing in airline seat management for flights with multiple flight legs [J], Transportation Science, 1999; 33(2): 192-206
[35] Serguei Netessine, Sergei Savin, Wenqiang Xiao. Revenue management through dynamic cross selling in e-commerce retailing [J], Operations Research, 2006; 54(5): 893–913
[36] Qing Ding, Panos Kouvelis, Joseph M. Milner. Dynamic pricing through discounts for optimizing multiple-class demand fulfillment [J], Operations Research, 2006; 54(1):169–183
[37] Constantinos Maglaras, Joern Meissner. Dynamic pricing strategies for multiproduct revenue management problems [J], M & SOM, 2006;8(2):136–148
[38] Cathy H. Xia, Parijat Dube. Dynamic pricing in e-services under demand Uncertainty [J], Production and Operations Management, 2007; 16(6):701–712
[39] Constantinos Maglaras, Assaf Zeevi. Pricing and design of differentiated services:approximate analysis and structural insights[J],Operations Research, 2005; 53(2):242–262
[40] Constantinos Maglaras. Revenue management for a multiclass single-server queue via a fluid model analysis[J],Operations Research, 2006; 54(5):914–932
[41] Yossi Aviv, Amit Pazgal. A partially observed markov decision process for dynamic pricing [J], Management Science, 2005; 51(9):1400–1416
[42] Somnath Mukhopadhyay,Subhashish Samaddar,Glenn Colville. Improving revenue management decision making for airlines by evaluating analyst-adjusted passenger demand forecasts [J], Decision Sciences, 2007; 38 (2):309-327
[43] Xiaowei Xu, Wallace J. Hopp. A monopolistic and oligopolistic stochastic flow revenue management model [J], Operations Research, 2006; 54(6):1098–1109
[44] Georgia Perakis, Anshul Sood. Competitive multi-period pricing for perishable products: a robust optimization approach[J], Math. Program, Ser. B, 2006; 107:295-335
[45] Beat Burger, Matthias Fuchs. Dynamic pricing-- A future airline business model [J], Journal of Revenue and Pricing Management, 2005; 4(1): 39–53
[46] Yuri Levin, Jeff McGill, Mikhail Nediak. Dynamic pricing in the presence of strategic consumers and oligopolistic competition [J], Management Science, 2009; 55(1):32–46
[47] Gérard P. Cachon, Robert Swinney. Purchasing, pricing, and quick response in the presence of strategic consumers [J], Management Science, 2009; 55(3):497-511
[48] Max Shen Zuo-Jun, Su Xuanming. Customer behavior modeling in revenue management and auctions: a review and new research opportunities[J],Production and Operations Management , 2007; 16(6):713–728
[49] Dan Zhang, William L. Cooper. Revenue management for parallel flights with customer-choice behavior [J], Operations Research, 2005; 53(3):415–431
[50] John G. Wilson, Chris K. Anderson, Sang-Won Kim. Optimal booking limits in the presence of strategic consumer behavior [J], Intl. Trans. in Op. Res, 2006; 13:99–110
[51] Yossi Aviv, Amit Pazgal. Optimal pricing of seasonal products in the presence of forward-looking consumers [J], M &SOM, 2008; 10(3):339–359
[52] Frank Y. Chen, Jian Chen, Yongbo Xiao. Optimal control of selling channels for an online retailer with cost-per-click payments and seasonal products [J], Production and Operations Management, 2007; 16(3):292–305
[53] RenéCaldentey, Lawrence M.Wein. Revenue management of a make-to-stock queue [J], Operations Research, 2006; 54(5):859–875
[54] Yuri Levin, Jeff McGill, Mikhail Nediak. Price guarantees in dynamic pricing and revenue management [J], Operations Research, 2007; 55(1):75–97
[55] Jérémie Gallien. Dynamic mechanism design for online commerce [J], Operations Research, 2006; 54(2):291–310
[56] William L. Cooper, Diwakar Gupta. Stochastic comparisons in airline revenue management [J], M &SOM, 2006; 8( 3):221–234
[57] Guillermo Gallego,Robert Phillips. Revenue management of flexible products [J], M &SOM, 2004; 6(4):321–337
[58] Apostolos Burnetas, Stephen Gilbert. Future capacity procurements under unknown demand and increasing costs [J], Management Science, 2001; 47(7):979–992
[59]罗利,萧柏春.收益管理理论的研究现状及发展前景[J],管理科学学报,2004; (5):75-83
[60]官振中,陈旭,卜祥智,史本山.易逝性高科技产品收益管理资源分配及定价策略[J],系统工程, 2006; (1):23-29
[61]卜祥智,赵泉午,陈荣秋.基于收益管理的海运集装箱舱位分配与路径选择优化模型[J] ,管理工程学报, 2008; (3):94-99
[62]罗利,彭际华.竞争环境下的民航客运收益管理动态定价模型[J],系统工程理论与实践, 2007; (11):15-25
[63]肖勇波,陈剑,刘晓玲.基于乘客选择行为的双航班机票联合动态定价模型[J],系统工程理论与实践, 2008; (1):46-55
[64]魏轶华,胡奇英,夏玉森.有多个销售渠道的连续时间收益管理问题研究[J],中国管理科学, 2008; (2):37-41
[65]张荣,黄学祥.成本与需求约束下的不对称电力竞价博弈[J] .系统工程理论与实践, 2009, (1):37-43
[66] Sridhar Tayur, Ram Ganeshan, Michael Magazine. Quantitative models for supply chain management [M]. Boston: Kluwer Academic Publishers, 1999.
[67] Clark, A.J. Scarf, H. Optimal policies for a multiechelon inventory problem [J], Management Science, 1960; 6:475-650
[68] Saibal Ray, Shanling Li, Yuyue Song. Tailored supply chain decision making under price-sensitive stochastic demand and delivery uncertainty [J], Management Science, 2005; 51 (12):1873–1891
[69] Serguei Netessine, Zhang Fuqiang. Positive vs. negative externalities in inventory management: implications for supply chain design[J], M & SOM, 2005; 7(1):58–73
[70] Serguei Netessine, Nils Rudi. Supply chain choice on the internet [J], Management Science, 2006; 52(6):844–864
[71] Fernando Bernstein, Awi Federgruen. Decentralized supply chains with competing retailers under demand uncertainty[J], Management Science, 2005; 51( 1):18–29
[72] Fernando Bernstein,Gregory A. DeCroix,Yulan Wang. Incentives and commonality in a decentralized multiproduct assembly system [J], Operations Research, 2007; 55(4):630–646
[73] Andy A.Tsay. Managing retail channel overstock: markdown money and return policies [J], Journal of Retailing, 2001; 77:457-492
[74] Yigal Gerchak, Yunzeng Wang. Revenue-sharing vs. wholesale-price contracts in assembly systems with random demand [J], Production and Operations Management 2004; 13(1):23–33
[75] Christos Koulamas. A newsvendor problem with revenue sharing and channel coordination [J], Decision Sciences, 2006; 37 (1):91-100
[76] Apostolos Burnetas, Stephen M. Gilbert, Craig E. Smith. Quantity discounts in single-period supply contracts with asymmetric demand information [J], IIE Transactions, 2007; 39:465–479
[77] Dong Lingxiu, Liu Hong. Equilibrium forward contracts on nonstorable commodities in the presence of market power[J],Operations Research, 2007; 55(1):128–145
[78] Fernando Bernstein,Francis de Véricourt. Competition for procurement contracts with service guarantees [J], Operations Research, 2008; 56(3):562–575
[79] Yunzeng Wang. Joint pricing-production decisions in supply chains of complementary products with uncertain demand [J], Operations Research, 2006; 54(6):1110–1127
[80] Daniel Granot, Shuya Yin. Price and order postponement in a decentralized newsvendor model with multiplicative and price-dependent demand[J], Operations Research, 2008; 56( 1):121–139
[81] Song Yuyue, Saibal Ray, Li Shanling. Structural properties of buyback contracts for price-setting newsvendors [J], M &SOM, 2008; 10(1):1–18
[82] Sirong Luo, Metin Cakanyildirim. Pricing and production games under revenue sharing and information updates[J], Journal of Revenue and Pricing Management, 2005; 4(3):370-301
[83] Li Lode, Zhang Hongtao. Confidentiality and information sharing in supply chain coordination [J], Management Science, 2008; 54(8):1467–1481
[84] Chung-Chi Hsieh, Wu Cheng-Han. Capacity allocation, ordering, and pricing decisions in a supply chain with demand and supply uncertainties[J], EuropeanJournal of Operational Research ,2008; 184:667–684
[85] Tamer Boyaci, Guillermo Gallego. Supply chain coordination in a market with customer service competition [J], Production and Operations Management , 2004;13(1): 3–22
[86] Birendra K. Mishra, Srinivasan Raghunathan, Yue Xiaohang. Information sharing in supply chains: Incentives for information distortion[J] , IIE Transactions,2007; 39:863–877
[87] Christopher S. Tang. Perspectives in supply chain risk management [J], Int. J. Production Economics, 2006; 103:451–488
[88] Shantanu Bhattacharya, V. Daniel R. Guide, Jr., Luk N. Van Wassenhove. Optimal order quantities with remanufacturing across new product generations [J], Production and Operations Management, 2006; 15(3):421–431
[89] Su Xuanming, Zhang Fuqiang. Strategic customer behavior, commitment, and supply chain performance[J], Management Science, 2008; 54(10):1759-1773
[90] Panos Kouvelis, Chester Chambers, Haiyan Wang. Supply chain management research and production and operations management: review, trends, and opportunities [J], Production and Operations Management, 2006; 15(3): 449–469
[91]艾兴政,唐小我,倪得兵.价格上涨环境下供应链的渠道协调机制研究[J],管理科学学报, 2004;(5):24-30
[92]陈志祥.敏捷供应链供需协调绩效关联分析与实证研究[J],管理科学学报,2005;(1):78-87
[93]陈旭.考虑期权合同供应链的零售商订货研究[J],管理科学学报, 2006;(3) :7-23
[94]宋华明,马士华.二阶段供应链中提前期压缩的影响与协调[J],管理科学学报,2007;(1) :46-53
[95]邵晓峰,季建华.基于补偿合约的供应链定价与能力设计的协调问题研究[J],中国管理科学, 2008;(4) :62-68
[96]黄祖庆,易荣华,达庆利.第三方负责回收的再制造闭环供应链决策结构的效率分析[J] ,中国管理科学, 2008;(3):73-77
[97]李丽君,黄小原,庄新田.双边道德风险条件下供应链的质量控制策略[J],管理科学学报, 2005;(1) :42-47
[98]吴军,李健,汪寿阳.供应链风险管理中的几个重要问题[J],管理科学学报,2006;(6) :1-12
[99]周永务,杨善林.基于不对称需求信息的供应链协调定价[J],系统工程学报,2006;(6):591-597
[100]熊中楷,李根道,唐彦昌,李薇.网络环境下考虑动态定价的渠道协调问题研究[J],管理工程学报,2007;(3):49-55
[101]谢金星,王淼.两级供应链中基于提前订货折扣的博弈[J],系统工程学报,2008;(2):181-187
[102]张菊亮,陈剑.供应商管理库存应对突发事件[J],中国管理科学, 2008;(5):71-76
[103] Gabriel Bitran, Rene Caldenity, Susanaa Mondschen. Coordinating clearance markdown sales of seasonal products in retail chains [J], Operations Research, 1998; 46(5):609-624
[104] Phi Hoang. The future of revenue management and pricing science [J], Journal of Revenue and Pricing Management, 2007; 6(2):151-153
[105] Zhao Wen, Wang Yunzeng. Coordination of joint pricing-production decision in a supply chain [J], IIE Transactions, 2002; 34:701-715
[106] Zou Xuxia, Shaligram Pokharel, Rajesh Piplani. A two-period supply contract modelfor a decentralized assembly system [J], European Journal of Operational Research 2008; 187: 257–274
[107] Wayne S. Desarbo, Vithala R.Rao, Joel H.Steckel, Jerry Wind, Richard Colombo. A friction model for describing and forecasting price changes[J], Marketing Science,1987; 6(4):299-319
[108] Conrad J.Lautenbacher, Shaler Stidham, JR. The underlying markov decision process in the single-leg airline yield management problem [J]. Transportation Science, 1999; 33(2):136-146
[109] Kalyan Talluri, Garrett Van Ryzin. Revenue management under a general discrete choice model of consumer behavior [J], Management Science, 2004; 50(1):15-33
[110] Youyi Feng, Baichun Xiao. Optimal policies of yield management with multiple predetermined prices [J], Operations Research, 2000; 48(2):332-343
[111] Gabriel R.Bitran, Susana V.Mondschein. Periodic pricing of seasonal products in retailing [J], Management Science, 1997; 43(1):64-78
[112] Stephen A.Smith, Dale D.Achabal. Clearance pricing and inventory policies for retail chains [J], Management Science, 1998; 44(3):285-300
[113] George E.Monahan, Nicholas C.Petruzzi.,Wen Zhao.The Dynamic pricing problem from a newsvendor’s perspective[J], Manufacturing & Service Operations Management , 2004; 6(1):73-91
[114]胡奇英,刘建庸.《马尔可夫决策引论》[M].西安:西安电子科技大学出版社,2000.
[115] M. Corstjens, P. Doyle. A model for optimizing retail space allocation [J], Management Science, 1981; 27:822-833
[116] Silver, E. A. Operations research and inventory management [J], Operations Research 1981; 29:628-645
[117] Timothy L.Urban. Inventory models with the demand rate dependent on stock and shortage levels [J], International Journal of Production Economics, 1995; 40:21-28
[118] TimothyL.Urban. A periodic-review model with serially-correlated, inventory-level-dependent demand [J], International Journal of Production Economics, 2005; 95:287-295
[119] Yigal Gerchak, Wang Yunzeng. Periodic-review inventory models with inventory-level-dependent demand [J], Naval Research Logistics, 1994; 41:99-116
[120] Khmelnitsky,E., Gerchak,Y. Optimal control approach to production systems with inventory-level- dependent demand[J], IEEE Transactions on AutomaticControl, 2002; 47(2):289-292
[121] Zhou, Y.W., Yang, S.L. An optimal replenishment policy for items with inventory-level-dependent demand and fixed lifetime under the LIFO policy [J], Journal of the Operational Research Society, 2003; 54(6):585-593
[122] Timothy L.Urban. Inventory models with inventory-level-dependent demand: a comprehensive review and unifying theory [J], European Journal of Operational Research, 2005; 162:792-804
[123] Teng Jinn-Tsair, Chang Chun-Tao. Economic production quantity model for deteriorating items with price- and stock-dependent demand[J], Computers & Operations Research, 2005; 32:297-308
[124] Datta Dk, Paul K. An inventory system with stock-dependent, price-sensitive demand rate [J], Production Planning and Control, 2001; 12:13-20
[125] Wedad Elmaghraby, Pinar Keskinocak. Dynamic pricing in the presence of inventory considerations: research overview, current practices, and future directions [J], Management Science, 2003; 49(10):1287-1309
[126] Karlin, Samuel. Dynamic inventory with varying stochastic demands [J], Management Science, 1960; 6(3):231-258
[127] Guillermo Gallego, Garrett van Ryzin. Optimal dynamic pricing of inventories with stochastic demand over finite horizons [J], Management Science, 1994; 40(8):999-1020
[128] Zhao.W., Zheng.Y. Optimal dynamic pricing for perishable assets [J], Management Science, 2000; 46:375-388
[129] Su Xuanming. Intertemporal pricing with strategic customer behavior [J], Management Science, 2007; 53(5):726-741
[130] lap Mui Ann Chan, David Simchi-Levi, Julie Swann. Pricing, production, and inventory policies for manufacturing with stochastic demand and discretionary sales [J], Manufacturing & Service Operations Management, 2006; 8(2):149-168
[131] Dimitris Bertsimas, Sanne de Boer. Dynamic pricing and inventory control formuitiple products [J], Journal of Revenue and Pricing Management, 2005; 3(4):303-319
[132] Intriligator, M. D. Econometric models, techniques, and applications[M]. Prentice Hall, Englewood Cliffs, NJ. 1978.
[133] Russell, Gary J., Ruth N. Bolton. Implications of market structure for elasticity structure [J], Journal of Marketing Research, 1988; 25(3):229-241
[134] Kalyanam, Kirthi. Pricing decisions under demand uncertainty: a bayesian mixture model approach [J], Marketing Science, 1996; 15(3):207-221
[135] Martin A. Lariviere, Evan L.Porteus. Selling to the newsvendor: an analysis of price- only contracts [J], Manufacturing & Service Operations Management, 2001; 3 (4):293-305
[136] Karlin,S., C.R..Carr. Prices and optimal inventory policies[M]. Stanford, CA: Stanford University Press, 1962.
[137] Chen Shaoxiang. The infinite horizon periodic review problem with setup costs and capacity constraints: a partial characterization of the optimal policy [J], Operations Research, 2004; 52(3):409-421
[138] Hakan Polatoglu, Izzet Sahin. Optimal procurement policies under price-dependent demand [J], International Journal of Production Economics 2000; 65:141-171
[139] Haresh Gurnani, Christopher S.Tang. Note: optimal ordering decisions with uncertain cost and demand forecast updating [J], Management Science, 1999; 45(10):1456-1462
[140] Hongyan Huang, Suresh P.Sethi, Houmin Yan. Purchase contract management with demand forecast updates [J], IIE Transactions, 2005; 37:775-785
[141] Wu Jianghua. Quantity flexibility contracts under bayesian updating [J], Computers & Operations Research, 2005; 32:1267-1288
[142] Karen L. Donohue. Efficient supply contracts for fashion goods with forecast updating and two production modes [J], Management Science, 2000; 46(11):1397-1411
[143] Elizabeth Junqueira Durango-Cohen, Candace Arai Yano. Supplier commitment and production decisions under a forecast-commitment contract [J], Management Science, 2006; 52(1):54-67
[144] Lawrence W. Robinson. Optimal and approximate control policies for airline booking with sequential nonmonotonic fare classes [J], Operations Research, 1995; 43(2):252-263
[145] Peter P.Belobaba. Airline yield management: an overview of seat inventory control [J], Transportation Science, 1987; 21(2):63-73
[146] William L.Cooper. Asymptotic behavior of an allocation policy for revenue management [J], Operations Research, 2002; 50(4):720-727
[147] Peter P. Belobaba. Application of a probabilistic decision model to airline seat inventory control [J], Operations Research, 1989; 37(2):183-197
[148] Li Du, Qiying Hu, Wuyi Yue. Analysis and evaluation for optimal allocation in sequential internet auction systems with reserve price[J], Dynamics of Continuous .Discrete and Impulsive Systems .Series B: Applications & Algorithms,2005; 12:617-631
[149] Milgrom. P., Roberts. J. Communication and inventories as substitutes in organizing production [J], Scandanavian J. Econom., 1988; 90:275-289
[150] Lee, H. L., K. C. So, S.Tang. The value of information sharing in a two-level supply chain [J], Management Science, 2000; 46:626-643
[151] Yossi Aviv. The effect of collaborative forecasting on supply chain performance [J], Management Science, 2001; 47(10):1326-1343
[152] Gerard P. Cachon, Marshall Fisher. Supply chain inventory management and the value of shared information [J], Management Science, 2000; 46 (8):1032-1048
[153] Zhu Kaijie, Ulrich W.Thonemann. Modeling the benefits of sharing future demand information [J], Operations Research, 2004; 52(1):136-147
[154] Huang Boray, Seyed M.R. Iravani. Production control policies in supply chains with selective-information sharing [J], Operations Research, 2005;53(4):662-674
[155] Henriott, L. Transforming supply chains into e-chains [J]. Supply Chain Management Rev., Special Global Supplement (spring),1999; 5-18
[156] Susan Cohen kulp, Hau L.Lee, Elie ofek. Manufacturer benefits from information integration with retail customers [J], Management Science, 2004; 50(4):431-444
[157] Gavirneni.S., R .Kapuscinski, S.Tayur. Value of information in capacitated supply chains [J], Management Science, 1999; 45(1):16-24
[158]王瑛.供应链伙伴信息共享的博弈与激励[J],中国管理科学, 2005; 13(5): 61-66
[159] Pasternack B.A. Optimal pricing and returns policies for perishable commodities [J], Marketing Science, 1985; 4(4):166-176
[160] Gerarad. P. Cachon. Handbooks in Operations research and Management Science: Supply Chain Management [M]. Edited by Steve Graves and Tonde Kok and Published by North-Holland, 2003.
[161] Andy A.Tsay The quantity flexibility contract and supplier-customer incentives [J], Management Science, 1999; 45(10):1339-1358
[162] Gerard P. Cachon, Martin A.Lariviere. Supply chain coordination with revenue-sharing contracts: strengths and limitations [J], Management Science, 2005; 51(1):30-44
[163] Charles J.corbett, Gregory A.Decroix. Shared-savings contracts for indirect materials in supply chains: channel profits and environmental impacts[J], Management Science, 2001; 47(7):881-893
[164] Harish Krishnan, Roman Kapuscinski, David A. Butz. Coordinating contracts for decentralized supply chains with retailer promotional effort [J], Management Science, 2004; 50(1):48-63
[165] Wang Yunzeng, Li jiang, Shen Zuo-Jun. Channel performance under consign- ment contract with revenue sharing, Management Science, 2004; 50(1):34-47
[166] Terry A .Taylor, Channel coordination under price protection, midlife returns,and end-of-life returns in dynamic markets [J], Management Science, 2001; 47(9):1220-1234
[167] Gerard P. Cachon. The allocation of inventory risk in a supply-chain: push, pull and advance-purchase discount contracts [J], Management Science, 2004; 50(2): 222-238
[168] Refik Güllü, Geert-Jan Van Houtum, F. Zeynep Surgut, Nesim Erkip. Analysis of a decentralized supply chain under partial cooperation [J], M&SOM, 2005; 7(3):229-247
[169] V. G. Narayanan, Ananth Raman, Jasjit Singh. Agency costs in supply chain with demand uncertainty and price competition [J], Management Science, 2005; 51(1):120-132
[170] Bardia Kamrad, Akhtar Siddique. Supply contracts, profit sharing, switching, and reaction options [J], Management Science, 2004; 50(1):64-82
[171] Apostolos Burnetas, Peter Ritchken. Option pricing with downward-sloping demand curves: the case of supply chain option [J], Management Science, 2005; 51(4): 566-580
[172] Charles J. Corbett, Zhou Deming, Christopher S.Tang. Designing supply contracts: contract type and information asymmetry [J], Management Science, 2004; 50(4):550-559
[173] Stanley Baiman, Paul Z., Fisher Marshel, Madhav V. Rajan. Information contracting and quality costs [J], Management Science, 2000; 46(6):776-789
[174] E. Andrew Boyd, Ioana C.Bilegan. Revenue management and e-commerce[J],Management Science, 2003; 49(10):1363-1386
[175] LevinYuri,Mcgill Jeff,Nediak Mikhail. Risk in revenue management and dynamic pricing [J], Operations Research, 2008; 56 (2):326-343
[176] A.P. Jeuland, M. Shugan. Managing channel profits [J], Marketing Science, 1983; 2(3):239-272
[177] Spengler J. Vertical integration and antitrust policy [J], Journal of Political Economy , 1950; 58(4):347-352
[178] Z. Qin, J.Yang. Analysis of a revenue-sharing contract in supply chain mnagement [J], International Journal of Logistics: Research & Applications, 2008; 11 (1):17-29
[179] Matthew W. McCarter, Gregory B. Northcraft. Happy together? insights and implications of viewing managed supply chains as a social dilemma[J], Journal of Operations Management, 2007; 25:498-511
[180] Tony Haitao Cui, Jagmohan S.Raju, Z.John Zhang. Fairness and channel coordination [J], Management Science, 2007; 53(8):1303-1314
[181] Lingxiu Dong, Kaijie Zhu, Two-wholesale-price contracts: push, pull, and advance-purchase discount contracts [J], M & SOM, 2007;9( 3):291–311
[182] Georgia Perakis, Guillaume Roels. The price of anarchy in supply chains: quantifying the efficiency of price-only contracts [J], Management Science, 2007; 53(8):1249–1268