随机产出环境下生产和替代销售的联合决策研究
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摘要
半导体等高科技产品具有较短的生命周期(一般是一年或两年)，很多产品在上市半年内将损失它整个生命周期的60%以上的价值。由于动态激烈变化的市场需求、较长的制造提前期、昂贵的生产准备成本、技术的不断快速革新，使得高科技产品制造产业面临着更大的市场风险。因此，有效的生产和销售决策，能够减少资金的浪费、提升企业利润并降低投资失败的风险。
本文的研究是以半导体制造产业为背景进行的，半导体制造工艺异常复杂、产品质量对生产环境的敏感性非常高，即使同一批产品(尤其是半导体产品)的质量也存在着巨大差异，这些不同质量的产品常被划分为不同的等级进行销售。因此，投入一定量的原材料其不同质量产品的产出率具有高度的不确定性。在销售阶段，不同等级产品需求具有向下替代性，即低等级的产品缺货时可以用高等级产品满足。由于不同等级产品的产出率和需求具有随机性，如何确定最优的原材料投入数量以及在考虑需求替代的情况下采用何种策略进行销售成为一大难题。
生产和销售都具有随机性的制造系统被称为随机产销系统。在现有的相关研究中，与随机产销系统生产或销售决策的相关研究多将生产问题和决策问题进行单独的研究，而进行集成研究的文献只研究了单周期的决策问题。因此本文的研究聚焦于多销售周期、多产品的随机产销系统的生产和销售联合决策问题，分别针对生产和销售不同步、同步以及考虑客户生命周期价值的情况展开研究。为了分析问题、验证模型和结论的正确性，本文的研究进行了仿真试验与分析。本文的研究内容主要包括：
(1)生产和销售不同步时随机产销系统生产和销售联合决策。研究单周期生产、多周期销售的随机产销系统，在每个销售周期每种产品的需求是独立的。因此，本问题的决策包括两点：原材料投入量和每个销售周期内各等级产品需求的满足量。该问题可构建为一个多参数的随机动态规划模型，其研究目标是使得随机产销系统在整个产销阶段的总利润最大化。通过数理分析找到了在销售阶段的最优替代销售策略，然而由于模型的动态复杂性，求解最优的原材料投入量决策模型面临巨大的工作量。因此，在比较几个相似的销售策略基础上，得到了对决策模型进行简化求解算法。算例研究表明，得到的算法能够有效地降低求解的复杂度。
(2)生产和销售阶段不同步时产销系统生产和销售联合决策。研究多周期生产、多周期销售的产销决策问题，即企业按照一定的节拍生产，当第一批次产品出产后，产品销售阶段开始。按照生产节拍长度将销售阶段划分为相同的销售周期，由于客户需求是连续的，在每个销售周期对产品销售进行实时库存控制面临着困难，因此采用ATP(available to promise)控制策略对产品销售进行实时决策。首先对两产品的销售问题展开研究，得到了基于ATP的产品销售方法。在此基础上，研究多产品、多销售周期情况下基于ATP的最优原材料投入量和产品替代销售策略，并通过算例验证了所提替代销售策略的有效性。
(3)基于客户生命周期价值的产量决策研究。通过深入分析客户生命周期价值理论，将客户生命周期价值植入到系统动力学系统模型中，证明了客户生命周期价值与系统原材料投入量的内在关联。此后，分析了原材料供应商控制的产业链产量控制问题，并以系统利润最大化为优化目标进行建模和求解。研究将客户生命周期价值作为优化目标，能够促进企业更加关注长期利润。
由于生产制造的复杂性、随机性、动态性和销售时产品的替代性，进行多产品、多周期产销联合决策的建模和求解面临困难，本文在研究中得到了一些优化方法和关键技术。与现有的相关文献相比，主要的创新点在于：
(1)针对现有研究多将生产和销售阶段单独研究的现状，将随机产销系统的生产和销售两阶段相结合进行集成研究，并且针对产销同步、不同步的产销系统，找到了不同的替代销售策略。
(2)在产销不同步的产销系统产销决策研究中，针对随机动态规划模型求解的复杂性设计了精简算法，与传统算法相比本文设计的算法能大幅降低求解运算量。
(3)将系统动力学仿真应用于产销系统中，研究了生产产量与产销系统利润的关系，并发现产销系统利润在半导体新产品上市后不久就快速下滑的现象。与传统研究方法相比，基于系统动力学的仿真能够对难以建模优化的复杂系统问题进行直观显现的仿真和分析。
(4)在产销同步情况下的产销决策研究中，针对需求的连续性特征，提出了基于ATP理论的销售替代策略。通过与理想情况下的利润对比，证明了本文所提出的基于ATP理论的销售控制策略具有较好的有效性。
Many high-tech products, especially the semiconductor products,tend to lose more than60%of their values in first six month afterlaunched, in that the life cycle of which are very short (one or two yearsin general). Meanwhile, due to dynamic market demand, long lead time,expensive facility cost and continuous technology innovation, thesemiconductor industry suffers from intensive capital cost and hugemarket risk. Therefore, the effective yield management of manufacturingis critical to reducing the waste of capital, lowering the risk of failureinvestment, while improving the profitability of company.
The manufacturing processes of the semiconductor products areextremely complex, which lead to the highly unstable performances ofthe semiconductor products even if they are produced in the same batch(especially for the high-tech products). The products with differentperformances are usually divided into different grade at the allocationstage. However, at the allocation stage, the demands for different gradeproducts are downwards substitutable; namely, the shortage of inferiorproducts can be satisfied with superior ones. As a consequence ofrandomness in both yield rates and demands for classified products, itbecomes a huge challenge for semiconductor manufacturers to makedecisions on the optimal input of raw materials at the production stageand the optimal allocation policy at the allocation stage.
The manufacture system with randomness in both production andallocation stages are named Random Yield and Allocation (RYA) System. In this paper, we focus on the raw material input and the allocationproblems of a RYA system with multi-selling period, multi-product andupgrade substitution. Mathematical programming, and intelligentoptimization theory are used to find the relationship between thecontinuous demand and the decision on the production and the sellingpolices. Stochastic process and CRM theory based on the value ofcustomer life cycle are studied in the thesis. In order to analyze problemand test the validity of model and conclusion, instance statistics andsimulations are used. The research mainly focuses on three aspects:
(1) The problem of the random system with unsynchronizedproduction and allocation processes. We focus on the raw materialquantity and the optimal allocation policy problem of the RYA systemwith single production period and multi allocation periods. The allocationstage is divided into multiple allocation periods, and demand for eachproduct in each period is independent with each other. This problem canbe formulated as a multi-parameter stochastic dynamic programmingmodel with the objective to maximize the total profits in the productionand allocation stages.
The theoretical analysises are used to find the optimal substitutepolicy in the allocation stage. Based on the comparison of several relatedmodels with different allocation polices; the simplified solution algorithmis setup. The example studies show that the algorithm is effective ineliminating the unimaginable workload.
(2) The problem of the random system with asynchronizedproduction and allocation processes. This study focuses on themulti-periods production and multi-periods allocation simultaneously,that is, the products is made in certain batches, the selling periods begin after the first batch of the product is done. The length of one productionperiod equals to that of one allocation period. The allocation decision ofthe products is difficult because of the continuity of the demand. So ATPtheory (available to promise) is adopted in this research. The allocationpolicy basing on the ATP theory is setup through an experiment on a RYAsystem with two products, and multi-periods. Then, the RYA system withmulti-product, multi-period is examined by simulations. The solution forcorresponding optimal raw material input, and the simulation are givenand the numerical example shows that this solution is feasible.
(3) The yield management problem considering the customerlifetime value. The customer lifetime value model is formulated in thesystem dynamic simulation framework, and the simulation result showsthe relationship between the customer lifetime value and the raw materialinput quantity. After that, the production quantity optimization problemfrom the side of raw material supplier is analyzed in the suppliercontrolled supply chain. The objective is to maximize the customerlifetime value of the whole supply chain, and this research can be ofsomehow helpful to encourage manufactures to pay more attention onlong-term profits.
The complexity, randomness, dynamic and the substitutability of thesemiconductors leads to huge difficulty in modeling and solving theproblem of decision making of a RYA system with multi-product andmulti-period. Thus, the simplified algorithm and crucial technology aregiven to lower the difficulties.
Comparing with the existing literature, the contributions of thisresearch are:
(1) The existing literature focuses separately on yield quantity or the allocation policy, while the joint studies on the decision making are madein single period. The joint research on both raw material quantity and theoptimal allocation policy of a RYA system is done in this thesis.
(2) The problem is formulated as a stochastic dynamic programmingmodel. The proposed allocation policy is much optimal than the newsboyallocation policy and the myopic allocation policy. Then the bounds ofthe decision parameters are given and an effective algorithm by modelcomparison is setup. The experiments show that the proposed algorithmis effective.
(3) All the related factors to the yield management of asemiconductor manufacturer, such as human resource, capital, cost andmarketing are considered in a system dynamics model. After severalrounds of simulation, the relationship between the yield quantity and themanufacturers profit is found. The simulations also show that thesemiconductor manufacturer’s profit decreases quickly in a relative shorttime period after the products are launched. Comparing with thetraditional optimization method, system dynamics simulation can showthe results more visibly.
(4) In a RYA system with synchronized production and distribution,the real time allocation decision is difficult to be made. Thus the ATPtheory is introduced to build an allocation policy. The comparison withthe ideal solution by numerical studies verifies the effectiveness of theproposed allocation policy.
本文的研究是以半导体制造产业为背景进行的，半导体制造工艺异常复杂、产品质量对生产环境的敏感性非常高，即使同一批产品(尤其是半导体产品)的质量也存在着巨大差异，这些不同质量的产品常被划分为不同的等级进行销售。因此，投入一定量的原材料其不同质量产品的产出率具有高度的不确定性。在销售阶段，不同等级产品需求具有向下替代性，即低等级的产品缺货时可以用高等级产品满足。由于不同等级产品的产出率和需求具有随机性，如何确定最优的原材料投入数量以及在考虑需求替代的情况下采用何种策略进行销售成为一大难题。
生产和销售都具有随机性的制造系统被称为随机产销系统。在现有的相关研究中，与随机产销系统生产或销售决策的相关研究多将生产问题和决策问题进行单独的研究，而进行集成研究的文献只研究了单周期的决策问题。因此本文的研究聚焦于多销售周期、多产品的随机产销系统的生产和销售联合决策问题，分别针对生产和销售不同步、同步以及考虑客户生命周期价值的情况展开研究。为了分析问题、验证模型和结论的正确性，本文的研究进行了仿真试验与分析。本文的研究内容主要包括：
(1)生产和销售不同步时随机产销系统生产和销售联合决策。研究单周期生产、多周期销售的随机产销系统，在每个销售周期每种产品的需求是独立的。因此，本问题的决策包括两点：原材料投入量和每个销售周期内各等级产品需求的满足量。该问题可构建为一个多参数的随机动态规划模型，其研究目标是使得随机产销系统在整个产销阶段的总利润最大化。通过数理分析找到了在销售阶段的最优替代销售策略，然而由于模型的动态复杂性，求解最优的原材料投入量决策模型面临巨大的工作量。因此，在比较几个相似的销售策略基础上，得到了对决策模型进行简化求解算法。算例研究表明，得到的算法能够有效地降低求解的复杂度。
(2)生产和销售阶段不同步时产销系统生产和销售联合决策。研究多周期生产、多周期销售的产销决策问题，即企业按照一定的节拍生产，当第一批次产品出产后，产品销售阶段开始。按照生产节拍长度将销售阶段划分为相同的销售周期，由于客户需求是连续的，在每个销售周期对产品销售进行实时库存控制面临着困难，因此采用ATP(available to promise)控制策略对产品销售进行实时决策。首先对两产品的销售问题展开研究，得到了基于ATP的产品销售方法。在此基础上，研究多产品、多销售周期情况下基于ATP的最优原材料投入量和产品替代销售策略，并通过算例验证了所提替代销售策略的有效性。
(3)基于客户生命周期价值的产量决策研究。通过深入分析客户生命周期价值理论，将客户生命周期价值植入到系统动力学系统模型中，证明了客户生命周期价值与系统原材料投入量的内在关联。此后，分析了原材料供应商控制的产业链产量控制问题，并以系统利润最大化为优化目标进行建模和求解。研究将客户生命周期价值作为优化目标，能够促进企业更加关注长期利润。
由于生产制造的复杂性、随机性、动态性和销售时产品的替代性，进行多产品、多周期产销联合决策的建模和求解面临困难，本文在研究中得到了一些优化方法和关键技术。与现有的相关文献相比，主要的创新点在于：
(1)针对现有研究多将生产和销售阶段单独研究的现状，将随机产销系统的生产和销售两阶段相结合进行集成研究，并且针对产销同步、不同步的产销系统，找到了不同的替代销售策略。
(2)在产销不同步的产销系统产销决策研究中，针对随机动态规划模型求解的复杂性设计了精简算法，与传统算法相比本文设计的算法能大幅降低求解运算量。
(3)将系统动力学仿真应用于产销系统中，研究了生产产量与产销系统利润的关系，并发现产销系统利润在半导体新产品上市后不久就快速下滑的现象。与传统研究方法相比，基于系统动力学的仿真能够对难以建模优化的复杂系统问题进行直观显现的仿真和分析。
(4)在产销同步情况下的产销决策研究中，针对需求的连续性特征，提出了基于ATP理论的销售替代策略。通过与理想情况下的利润对比，证明了本文所提出的基于ATP理论的销售控制策略具有较好的有效性。
Many high-tech products, especially the semiconductor products,tend to lose more than60%of their values in first six month afterlaunched, in that the life cycle of which are very short (one or two yearsin general). Meanwhile, due to dynamic market demand, long lead time,expensive facility cost and continuous technology innovation, thesemiconductor industry suffers from intensive capital cost and hugemarket risk. Therefore, the effective yield management of manufacturingis critical to reducing the waste of capital, lowering the risk of failureinvestment, while improving the profitability of company.
The manufacturing processes of the semiconductor products areextremely complex, which lead to the highly unstable performances ofthe semiconductor products even if they are produced in the same batch(especially for the high-tech products). The products with differentperformances are usually divided into different grade at the allocationstage. However, at the allocation stage, the demands for different gradeproducts are downwards substitutable; namely, the shortage of inferiorproducts can be satisfied with superior ones. As a consequence ofrandomness in both yield rates and demands for classified products, itbecomes a huge challenge for semiconductor manufacturers to makedecisions on the optimal input of raw materials at the production stageand the optimal allocation policy at the allocation stage.
The manufacture system with randomness in both production andallocation stages are named Random Yield and Allocation (RYA) System. In this paper, we focus on the raw material input and the allocationproblems of a RYA system with multi-selling period, multi-product andupgrade substitution. Mathematical programming, and intelligentoptimization theory are used to find the relationship between thecontinuous demand and the decision on the production and the sellingpolices. Stochastic process and CRM theory based on the value ofcustomer life cycle are studied in the thesis. In order to analyze problemand test the validity of model and conclusion, instance statistics andsimulations are used. The research mainly focuses on three aspects:
(1) The problem of the random system with unsynchronizedproduction and allocation processes. We focus on the raw materialquantity and the optimal allocation policy problem of the RYA systemwith single production period and multi allocation periods. The allocationstage is divided into multiple allocation periods, and demand for eachproduct in each period is independent with each other. This problem canbe formulated as a multi-parameter stochastic dynamic programmingmodel with the objective to maximize the total profits in the productionand allocation stages.
The theoretical analysises are used to find the optimal substitutepolicy in the allocation stage. Based on the comparison of several relatedmodels with different allocation polices; the simplified solution algorithmis setup. The example studies show that the algorithm is effective ineliminating the unimaginable workload.
(2) The problem of the random system with asynchronizedproduction and allocation processes. This study focuses on themulti-periods production and multi-periods allocation simultaneously,that is, the products is made in certain batches, the selling periods begin after the first batch of the product is done. The length of one productionperiod equals to that of one allocation period. The allocation decision ofthe products is difficult because of the continuity of the demand. So ATPtheory (available to promise) is adopted in this research. The allocationpolicy basing on the ATP theory is setup through an experiment on a RYAsystem with two products, and multi-periods. Then, the RYA system withmulti-product, multi-period is examined by simulations. The solution forcorresponding optimal raw material input, and the simulation are givenand the numerical example shows that this solution is feasible.
(3) The yield management problem considering the customerlifetime value. The customer lifetime value model is formulated in thesystem dynamic simulation framework, and the simulation result showsthe relationship between the customer lifetime value and the raw materialinput quantity. After that, the production quantity optimization problemfrom the side of raw material supplier is analyzed in the suppliercontrolled supply chain. The objective is to maximize the customerlifetime value of the whole supply chain, and this research can be ofsomehow helpful to encourage manufactures to pay more attention onlong-term profits.
The complexity, randomness, dynamic and the substitutability of thesemiconductors leads to huge difficulty in modeling and solving theproblem of decision making of a RYA system with multi-product andmulti-period. Thus, the simplified algorithm and crucial technology aregiven to lower the difficulties.
Comparing with the existing literature, the contributions of thisresearch are:
(1) The existing literature focuses separately on yield quantity or the allocation policy, while the joint studies on the decision making are madein single period. The joint research on both raw material quantity and theoptimal allocation policy of a RYA system is done in this thesis.
(2) The problem is formulated as a stochastic dynamic programmingmodel. The proposed allocation policy is much optimal than the newsboyallocation policy and the myopic allocation policy. Then the bounds ofthe decision parameters are given and an effective algorithm by modelcomparison is setup. The experiments show that the proposed algorithmis effective.
(3) All the related factors to the yield management of asemiconductor manufacturer, such as human resource, capital, cost andmarketing are considered in a system dynamics model. After severalrounds of simulation, the relationship between the yield quantity and themanufacturers profit is found. The simulations also show that thesemiconductor manufacturer’s profit decreases quickly in a relative shorttime period after the products are launched. Comparing with thetraditional optimization method, system dynamics simulation can showthe results more visibly.
(4) In a RYA system with synchronized production and distribution,the real time allocation decision is difficult to be made. Thus the ATPtheory is introduced to build an allocation policy. The comparison withthe ideal solution by numerical studies verifies the effectiveness of theproposed allocation policy.
引文
[1] Abad P. L., Aggarwal V.2005. Incorporating transport cost in the lot size and pricingdecisions with downward sloping demand. International Journal of Production Economics, Vol.95,No.3, pp.297-305.
[2] Agrawal V., Nahmias S.1997. Rationalization of the supplier base in the presence of yielduncertainty. Production and Opereration Management, Vol.6, No.3, pp.291-308
[3] Anupindi R., Kellar R.1993. Diversification under supply uncertainty. Management Science,Vol.39, No.8, pp.944-963.
[4] Achabal D. D., Mcintyre S.H., Smith, S. A.2000. Kalyanam K.A decision support system forvendor managed inventory, Journal of Retailing, Vol.76, No.4, pp.430-454.
[5] Aichlymayr M.2000. DC mart: who manages inventory in a value chain.Transportation andDistribution, Vol.41, No.5. pp.60-68.
[6] Alstrup J,S., Boas,O.B., Madsen G., Vidal R.V.V.1986. Booking policy for flights with twotypes of passengers. European Journal of Operational Research, Vol.27, pp.274-288.
[7] Archibald, T.W., Sassen S.A.E., Thomas L.C.1997. An optimal policy for a two depotinventory problem with stock transfer. Management Science, Vol.43, No.2, pp.173-183.
[8] Axsater S.2003. A new decision rule for lateral transshipments in inventory systems.Management Science, Vol.49, No.9, pp.1168-1179.
[9] Bassok Y., Anupindi R. and Akella, R.1999. Single period multi-product inventory modelswith substitution. Operations Research, No.4, pp.632-642.
[10] Bassok Y., Ernst R.1995. Dynamic allocations for multi-product distribution. TransportationScience, Vol.29, No.3, pp.256-266.
[11] Bassok Y., Akella A.R.1999. Single-period multi-product inventory models with substitution.Operations Research, Vol.47, No.9, pp.632-642.
[12] Basuroy S. Nguyen D.1998. Multinomial logit market share models: Equilibriumcharacteristics and strategic implications, Management Science, Vol.44, No.10, pp.1396-1408.
[13] Bernstein F., Federgruen A.2004. Dynamic inventory and pricing models for competingretailers. Naval Research Logistics, Vol.51, No.2, pp.258–274.
[14] Becker G.M, DeGroot M.H., Marschak J.1964. Measuring utility by a single-responsesequential method. Behavioral Science, Vol.9, No.3, pp.226-232.
[15] Ben-Daya M., Raouf A.1994. Inventory models involving lead time as decision variable.Journal of the Operational Research Society, Vol.45, No.4, pp.579-582.
[16] Bernstein F., Federgruen Awi.2005. Decentralized Supply Chains with Competing RetailersUnder Demand Uncertainty. Management Science, Vol.51, No.1, pp.18-29.
[17] Bitran G.R., Dasu S.1992.Ordering Policies in an Environment of Stochastic Yields andSubstitutable Demands. Operations Research Archive. Vol.40, No.5, pp.999-1017.
[18] Bitran G.R., Leong T.Y.1992. Deterministic Approximations to Co-Production Problems withService Constraints and Random Yields. Management Science, Vol.38, No.5, pp.724-742.
[19] Bitran G. and Dasu S.1992. Ordering policies in an environment of stochastic yields andsubstitutable demand. Operations Research, Vol.50, No.7. pp.999-1017.
[20] Bitran G.R., Gilbert S.M.1996. Managing hotel reservations with uncertain arrivals.Operations Research. Vol.44, No.1, pp.35-49.
[21] Bitran G.R., Tirupati D.1988. Planning and scheduling for epitaxial wafer productionfacilities. Operations Research, Vol.36, No.1, pp.34-49.
[22] Bitran G.R., Dasu S.1992. Ordering policies in an environment of stochastic yields andsubstitutable demands. Operations Research, Vol.40, No.5, pp.999-1017.
[23] Bitran G.R., Leong T.Y.1992. Deterministic approximations to co-production problems withservice constraints and stochastic yields. Management Science, Vol.38, No.5, pp.724-742.
[24] Bitran G.R., Tirupati D.1988. Planning and scheduling for epitaxial wafer productionfacilities. Operational Research, Vol.36, No.6, pp.34-49.
[25] Bollapragada S. Morton T.E.1999. Myopic heuristics fo rthe random yeild problem,Operations research, Vol.47, No.5, pp.713-722.
[26] Bookbinder J.H., Cakanyildirim M.1999. Random lead tines and expedited orders ininventory systems. European Journal of Operational Research, Vol.115, No.2, pp.300-313.
[27] Bowersox D.J., Closs D.J.1996. Logistics Management: the Integrated Supply Chain Process.New York: The McGraw-Hill, Inc.
[28] Brumelle S.L., McGill J.I., Oum T.H., Sawaki K., Tretheway M.W.1990. Allocation ofairline seats between stochastically dependent demand. Transportation Science, Vol.24, No.3,pp.183-192.
[29] Burke M.1996. It's time for vendor managed inventory. Industrial Distribution, Vol.85, No.2, pp.74-90.
[30] Buzzell R.D., Ortmeyer G.1995. Channel partnerships streamline distribution. SloanManagement Review, Vol.36, pp.85-96.
[31] Cachon G.P.2001. Stock wars: Inventory competition in a two-echelon supply chain withmultiple retailers. Operations Research, Vol.49, No.5, pp.658-674.
[32] Cakanyildirim M.1995. Analysis of endogenous lead-time (Q, r) inventory systems. AScThesis, Department of Management Sciences, University of Waterloo, Ont.
[33] Cai X.Q., Chen J., Xiao Y.b., Xu X.L.2010. Optimization and Coordination of Fresh ProductSupply Chains with Freshness Keeping Effort, Production and Operations Management, Vol.19,No.3, pp.261-278.
[34] Carolyn F.S. and Mary A.M.2000. Maximizing the use of vendor managed inventories whileminimizing internal headaches. Dynamic APICS Resource Education, pp.27-32.
[35] Cetinkaya S., Lee C.Y.2000. Stock replenishment and shipment scheduling forvendor-managed inventory system. Management Science, Vol.46, No.2, pp.217-232.
[36] Chao X.L., Chen H. Zheng S.H.2008. Joint replenishment and pricing decisions in inventorysystems with stochastically dependant supply capacity, European Journal of Operational Research,Vol.191, No.1, pp.142-155.
[37] Chen H., Chen J. Chen Y.2006. A coordination mechanism for a supply chain with demandinformation updating.International Journal of Production Economics, Vol.103, No.1,347-361.
[38] Chen F.1998. Echelon reorder points, installation reorder points, and the value of centralizeddemand information. Management Science, Vol.44, No.2, pp.221-334.
[39] Chen F., Drezner Z., Ryan J.K.2000. Quantifying the bullwhip effect in a supply chain: theimpact of forecasting, lead times and information. Management Science, Vol.46, No.3, pp.436-443.
[40] Chen W.C., Chien C.F.2010. Evaluating capacity pooling strategy in semiconductormanufacturing: a productivity perspective study. International Journal of Production Research, Vol.28, No.24, pp.566-588.
[41] Chen J.C., Fan Y.C., and Chen C.W.,2008. Capacity requirements planning for twin fabs ofwafer fabrication. International Journal of Production Research. Vol.41, No.16, pp.3921-3941.
[42] Cheung K., Lee L.H.,2002. The inventory Benefit of Shipment Coordination and Stockrebalancing in a Supply Chain.Management Science, Vol.48, No.2, pp.300-307.
[43] Chien C.F. Hsu C.,2006. A novel method for determining machine subgroups and backupswith an empirical study for semiconductor manufacturing. Journal of Intelligent Manufacturing,Vol.17, No.4, pp.429-440.
[44] Chien C.F., Hsu C.2007. Construct the OGE for promoting tool group productivity insemiconductor manufacturing. International Journal of Production Research, Vol.45, No.3, pp.509-524.
[45] Chopra S.2001. Meindl P. Supply chain management: Strategy, planning, and operations.Prentice-Hall, Inc.
[46] Chopra S., Meindl P.2001. Supply chain management: Strategy, planning, and operations.Prentice-Hall, Inc.
[47] Corbett C.J. and Rajaram K.,2006. A generalization of the inventory pooling effect tononnormal dependent demand. Manufacturing and Service Operations Management, Vol.84, No.4, pp.351-358.
[48] Cottrill K.1997. Reforging the supply chain. Journal of Business Strategy, Vol.18, No.6, pp.35-39.
[49] Cunningham, S.P., Spanos C.J., Voros K.1995. Semiconductor yield improvement: resultsand best practices. IEEE Transport. Semiconductor Manufacture, Vol.8, No.2, pp.103-109.
[50] Curry R.1990. Optimal seat allocation with fare classes nested by Original destinations.Transportation Science，Vol.24, No.3, pp.193-204.
[51] Dada M., Nicholas C. Petruzzi L. Schwarz B.2007. A newsvendor's procurement problemwhen suppliers are unreliable. Manufacturing Service Opereation Management, Vol.9, No.1,pp.9-32.
[52] Dai Y.2005. Pricing in revenue management for multiple firms competing for customers.International Journal of Production Economics, Vol.98, No.3, pp.1-16.
[53] Dong Y, Xu K.2002. A supply chain model of vender managed inventory.TransportationResearch Part E, Vol.38, No.4, pp.75-95.
[54] Doniavi A., Mileham A.R., Newnes L.B.1996. A systems approach to modeling in themanufacturing environment, in Proceedings of the12th National Conference on ManufacturingResearch, Bath, September.
[55] Drezner Z., Ryan J.K.2000. Quantifying the bullwhip effect in a simple supply chain: Theimpact of forecasting, lead times, and information. Management Science, Vol.46, No.3, pp.436-443.
[56] Duenyas I., Tsal C.Y.2000. Control of a manufacturing system with random product yieldand downward substitutability. IIE Transactions. Vol.30, No.2, pp.785-795.
[57] Ehrhardt R.1984.(s, S) Policies for a dynamic inventory model with stochastic lead time.Operations Research, Vol.32, No.1, pp.121-132.
[58] Ernst R., Kouvelis P.1999. The effects of selling packaged goods on inventory decisions.Management Science, Vol.45, pp.8, pp.1142-1155.
[59] Fangruo C., Federgruen2001. A and Yu-sheng Near-optimal pricing and replenishmentstrategies for a retail/distribution system. Operations Research. Vol.49, No.6, pp.839-853.
[60] Farley G.A.1997. Discovering Supply Chain Management: a Roundtable DiscussionAPICS-The Performance Advantage, Vol.7, No.1, pp.38-39.
[61] Federgruen A., Yang N,2009. Optimal supply diversification under general supply risks.Operations Research, Vol.57, No.6, pp.1451-1468.
[62] Federgruen A., Yang N.2008. Selecting a Portfolio of suppliers under demand and supplyrisks. Operations Research, Vol.56, No.4, pp.916-936.
[63] Feng Y.Y., Xiao B.C.2000. Optimal Policies of Yield Management with MultiplePredetermined Prices. Operations Research, Vol.48, No.2, pp.332-343.
[64] Forsberg R.1995. Optimization of order-up-to S policies for two-level inventory systemswith compound Poisson demand. European Journal of Operational Research, Vol.81, No.1, pp.143-153.
[65] Frank K.C., Zhang R.Q., Duenyas I.2003. Optimal inventory policies for inventory systemswith priority demand classes. Operations Research. Vol.51, No.6, pp.993-1002.
[66] Gallego G., Katircioglu K., Ramachandran B.,2006.Semiconductor inventory managementwith multiple grade parts and downgrading. Production planning and control. Vol.17, No.7,pp.689-700.
[67] Gan B.P.2007. Analysis of a borderless fab using interoperating AutoSched AP models.International Journal of Production Research, Vol.45, No.3, pp.675-697.
[68] Ganeshan R.1999. Managing supply chain inventories: A multiple retailer, one warehouse,multiple supplier model. International Journal of Production Economics, Vol.59, No.5, pp.341-354.
[69] Gerber N.1999. Objective comparisons of consignment, just-in-time, and stockless.HospitalMaterial Management Quarterly, Vol.13, No.1, pp.10-17.
[70] Gerchak Y, Khmelnitsky E.2003. A consignment system where suppliers cannot verifyretailer's sales reports. International Journal of Production Economics, Vol.83, No.3, pp.37-43.
[71] Gerchak Y., Tripathy A., Wang K.1996. Co-production models with stochastic functionalityyields. IIE Transactions, Vol.28, No.5, pp.391-403.
[72] Goyal S.K.1977. An integrated inventory model for a single supplier-single customerproblem. International Journal of Production Research, Vol.15, No.1, pp.107-111.
[73] Gruen T.W. D.S. Corsten Bharadwaj S.2002. Retial out-of-stocks: A worldwide examinationof extent, casues and consumer responsees. Working paper, Goizueta Bussiness School, EmoryUniversity, Atlanta.
[74] Gules H.K., Burgess T.F.1996. Manufacturing technology and the supply chain: Linkingbuyer-supplier relationships and advanced manufacturing technology. European Journal ofPurchasing and Supply Management, Vol.2, No.1, pp.31-38.
[75] Ha A.Y.1997a. Inventory rationing in a make-to-stock production system with severaldemand classes and lost sales. Management Science, Vol.43, No.8, pp.1093-1103.
[76] Ha A.Y.1997b. Stock rationing policy for a make-to-stock production system with twopriority classes and backordering. Naval Research Logistics, Vol.44, No.5, pp.457-472.
[77] Ha A.Y.2000. Stock rationing in an M/E/1make-to-stock queue. Management Science, Vol.46, No.1, pp.77-87.
[78] Hall B.H.1993. The Stock Market's Valuation of RandD Investment During the1980's. TheAmerican Economic Review, Vol.83, No.2, pp.259-264.
[79] Henig M., Gerchak Y.1990. The structure of periodic review policies in the presence ofstochastic yield. Operations Research, Vol.38, No.4, pp.634–643.
[80] Hopp W.J.,2008. A Static Approximation for Dynamic Demand Substitution withApplications in a Competitive Market, Operations Research, Vol,56, No.3, pp.630-645.
[81] Horton D.1998. Modeling the yield of mixed-technology die. Solid State Technology, Vol.41, No.9,109-119.
[82] Hsu V.N., Liu C.L., Xiao W.Q.2005. Dynamic lot size problem with one way prodctuionsubstitution.IIE Tranctions. Vol.34, No.2, pp.74-89.
[83] Hsu, A., Bassok Y.1999. Stochastic Yield and Stochastic Demand in a Production Systemwith Downward Substitution. Operations Research, Vol.47, No.2, pp.277-290.
[84] Hu X.X. Duenyas I. Kapuscineski R.2008. Oprimal joint inventory and transshipmentcontrol under uncertain capacity, Operations Research, Vol.56, No.3, pp.881-897.
[85] Huang C.2004. An optimal policy for a single-vendor single-buyer integratedproduction-inventory problem with process unreliability consideration.International Journal ofProduction Economics, Vol.91, No.3, pp.91-98.
[86] Huang H.C., Song H.Q.2010. Modified Base-stock Policies for Semiconductor ProductionSystem with Dependent Yield Rates. European Journal of Operational Research. Vol.45, pp.39-54.
[87] Ge Z.Q.2010. Semiconductor Manufacturing Process Monitoring Based on AdaptiveSubstatistical PCA. IEEE Transactions on Semiconductor Manufacturing. Vol.23, No.1, pp.99-108.
[88] Iyer A. V, Bergen M.E.1997. Quick response in manufacturer-retailer channels. ManagementScience, Vol.43, No.4, pp.559-570.
[89] Jackson B.B.1985. Build customer relation-ships that last. Harvard Business Review, Vol.63, No.6, pp.120-148.
[90] Jain D., Singh S.2002. Customer lifetime value researches in marketing: a review anddirections. Journal of interactive marketing, Vol.16, No.2, pp.34-41
[91] Jeuland A.P., Shugan S.M.1983. Managing channel profits. Marketing Science, Vol.2, No.3,pp.239-272.
[92] Jia J., Mason S.J.2009. Semiconductor manufacturing scheduling of jobs containing multipleorders on identical parallel machines. International Journal of Production Research, Vol.47, No.10, pp.2565-2585.
[93] Jordan W.C., Graves S.C.1995. Principles on the benefit of manufacturing process flexibility.Management Science, Vol.41, No.4, pp.577-594.
[94] Karaesmen I., van Ryzin G.J.2004. Overbooking with substitutable inventory classes.Operations Research, Vol.52, No.1, pp.83-104.
[95] Karmarkar U.1981. The multi-period multi-location inventory problem. Operations Research,Vol.29, No.2, pp.215-228.
[96] Karmarkar U., Lin S.C.1987. Production planning with uncertain yields and demands.Working Paper. Simon Graduate School of Business Administration, University of Rochester,Rochester, NY.
[97] Karmarkar U.S.1981. The multi-period multi-location inventory problem. OperationsResearch. Vol.29, No.2, pp.215-228.
[98] Kazaz B.2004. Production Planning Under Yield and Demand Uncertainty withYield-Dependent Cost and Price, Manufacturing and Service Operations Management Summer,Vol.6, No.3, pp.209-224.
[99] Keller G, Noori H.1988. Justifying new technology acquisition through its impact on the costof running an inventory policy. IIE Transitions, Vol.20, No.3, pp.284-291.
[100] Kim K.L., Hayya J.C.1992. Hong J.D.Setup reduction in economic production quantitymodel. Decision Sciences, Vol.23, No.1, pp.500-508.
[101] Kimes, S.E.1989. The Cornell Hotel and Restaurant Administration Quarterly. The basics ofyield management. Vol.30, No.3, pp.14-19.
[102] Kohli R., Park H.1994.Coordinating buyer-seller transactions across multiple products.Management Science, Vol.40, No.9, pp.45-50.
[103] Kothari V.1984. Silicon wafer manufacture. Unpublished thesis, Sloan school ofmanagement, MIT, Cambridge, Massachusetts.
[104] Kraiselburd S., Narayanan V.G..2004. A Contracting in a supply chain with stochasticdemand and substitute products. Production and Operations Management, Vol.13, No.1, pp.46-62.
[105] Kraiselburd S.2006. When is Vendor Managed Inventory Good for the Retailer? Impact ofRelative Margins and Substitutions Rates. Work Paper, Harvard Business School.
[106] Krajewski L.J. and Ritzman L.P.1996. Operations Management: Strategy and Analysis.Addision-Wesley, MA.
[107] Krupp J.A.G.1997. Safety Stock Management. Production and Inventory ManagementJournal, Vol.38, No.3, pp.11-18.
[108] Kuehl R.1999. Design of experiments: statistical principles of research design and analysis(second version). Duxbury Press, California, USA.
[109] Kumar N., Kennedy K., Gildersleeve K., Abelson R., Mastrangelo C.M., Montgomery D.C.2006. A review of yield modeling techniques for semiconductor manufacturing, InternationalJournal of Production Research, Vol.44, No.23, pp.5019-5036.
[110] Lal R., Staelin R.1984. An approach for developing an optimal discount pricing policy.Management Science, Vol.30, No.12, pp.1524-1539
[111] Lambert D.M, Cooper M.C.2000. Issues in the supply chain management. IndustrialMarketing Management, Vol.29, No.3, pp.65-83.
[112] Lee C.C., Chu W.H.J.2005. Who should control inventory in a supply chain. EuropeanJournal of Operational Research, Vol.164, No.1, pp.158-172.
[113] Lee H.L., Billington C.1992. Supply chain management: Pitfalls and opportunities.SloanManagement Review, Vol.33, No.3, pp.65-73.
[114] Lee H.L., Billington C.1996. The Evolution of Supply-chain management models andpractices at Hewlett Packard. Interfaces, Vol.25, No.5,42-63.
[115] Lee H.L., So K.C., Tang C.S.2000. The value of information sharing in a two-level supplychain. Management Science, Vol.46, No.5,626-643.
[116] Lee H.L., Yano C.A.1988. Production control in multistage systems with variable yieldlosses. Operations Research, Vol.36, No.2, pp.269-278.
[117] Lee H., Padamanabhan P.1997. Whang, S. Information distortion in a supply chain: thebullwhip effect. Management Science, Vol.43, No.4, pp.546-558.
[118] Lemon K.N., Verhoef P.C.2003. The Theoretical Underpinnings of Customer AssetManagement: A Framework and Propositions for Future Research. Journal of the Academy ofMarketing Science, Vol.32, pp.271-292
[119] Li Q., Xu H., Zheng S.H.,2008. Periodic review inventory systems with random yield:bounds and heuristics, IIE Transacitons, Vol.40, pp.434-394.
[120] Li Q., Zheng S.,2006. Joint inventory replenishment and pricing control for systems withuncertain yield and demand, Operations Research, Vol.54, No.4, pp.696-705.
[121] Lippman S.A., McCardle K.F.,1997. The competitive newsboy. Operations Research,Vol.45, No.1, pp.54-65.
[122] Liu L.J.2006. Supply chain coordination with quantity discount policy. International Journalof Production Economics, Vol.101, No.1, pp.89-98.
[123] Liao C.J., Shuyu C.H.1991. An analytical determination of lead time with normal demand.International Journal of Operations and Production Management, Vol., No.2, pp.72-78.
[124] Mahajan S., Ryzin G.1999. Inventory Competitive Under Dynamic Consumer Choice.Working Paper. Columbia Business School.
[125] Mahajan S., Ryzion G.2001a. Stocking retail assortments under dynamic consumersubstitution. Operations Research, Vol.49, No.3, pp.334-351.
[126] Mahajan S. Van Ryzion G.2001b. Inventory competition under dynamix consumer choice.Operations Research, Vol.49, No.5, pp.646-657.
[127] Mallik S.2007. Contracting over multiple parameters: Inventory allocation insemiconductor manufacturing. European Journal of Operational Research, Vol.182, No.1, pp.174-193.
[128] Manchanda P., Dubé J.P., Goh K.Y., Chintagunta P. K.2006. The Effect of BannerAdvertising on Internet Purchasing. Journal of Marketing Research. Vol.43, No.1, pp.98-108.
[129] McGillivray A.R., Silver E.A.1978. Some concepts for inventory control undersubstitutable demand. INFOR, Vol.16, No.1, pp.47-63.
[130] Mishra B. K., Raghunathan S.2004. Retailer-vs. vendor-managed inventory and randcompetition. Management Science, Vol.50, No.4, pp.445-457.
[131] Mitchell J.S., Slamet P.1995. The Meaning and Management of yield in Hotels.International journal of contemporary Hospitality Management, Vol.7, No.4, pp.23-26.
[132] Moinzadeh K., Lee H.L.1987. A continuous-review inventory model with constantre-supply time and defective items. Naval Research Logistics. Vol.29, No.2, pp.457–467.
[133] Moinzadeh K., Lee H.L.1987. A continuous-review inventory model with constantre-supply time and defective items. Naval Research Logistics, Vol.34, No.4, pp.457-467.
[134] Moon L, Choi S.1998. A note on lead time and distributional assumptions in continuousreview inventory models. Computers and Operations Research, Vol.29, No.2, pp.1007-1012.
[135] Nadiri M.I., Prucha I.1996. Estimation of the depreciation rate of physical and RandDcapital in the U.S total manufacturing sector. Economic Inquiry, Vol.34, No.1, pp.43-56.
[136] Nagarajan M., Rajagopalan S.2008. Inventory models for substitutable products:Optimalpolicies and heuristics, Management Scinence, Vol.54No.2, pp.1453-1466.
[137] Narayanan V.G, Raman A.2005. Agency Costs in a Supply Chain with Demand Uncertaintyand Price Competition. Management Science, Vol.51, No.1, pp.120-132.
[138] Nasri F., Affisco J.F., Paknejad M.J.1990. Setup cost reduction in an inventory model withfinite-range stochastic lead times. International Journal of Production Research, Vol.29, No.2, pp.199-212.
[139] Netessine S. Rudi N. Centralized and competitive inventory models with demandsubstitution, Operations Research, Vol.51, No.2, pp.329-335.
[140] Parlar M.1988. Game theoretic analysis of the substitutable product inventory problem withrandom demands.Naval Research Logistics, Vol.29, No.2, pp.397-409.
[141] Pasternack B.A., Drezner Z.1991. Optimal inventory policies for substitutable commoditieswith stochastic demand. Naval Research Logistics, Vol.38, No.2, pp.221-240.
[142] Pasternack B.A., Drezner Z.1991.Optimal inventory policies for substitutable commoditieswith stochastic demand. Naval Research Logistics, Vol.38, No.2, pp.221-240.
[143] Peikert A., Thoma J., Brown S.1998. Rapid modeling technique for measurableimprovements in factory performance. IEEE Winter Simulation. Conference, pp.1011–1015.
[144] Pentico D.1974.The assortment problem with stochastic demand. Management Science, Vol.21, No.2, pp.268-290.
[145] Phan D.D., Vogel D.R.2010. A model of customer relationship management and businessintelligence systems for catalogue and online retailers. Information and Management, Vol.47, No.2, pp.69-77.
[146] Quirk M., Serda J.2001. Semiconductor manufacturing technology. Upper Saddle River,NJ: Prentice Hall.
[147] Rajaram K., Tang C.S.2001. The impact of product substitution on retail merchandising [J].European Journal of Operational Research, Vol.29, No.2, pp.582-601.
[148] Rao U.S., Swaminathan J.M., Zhang J.2004. Multi-product inventory planning withdownward substitution, stochastic demand and setup costs. IIE Transactions. Vol.36, No.1, pp.59-71.
[149] Robinson L.W.1994. Optimal and approximate control policies for airline booking withsequential fare classes. Operations Research, Vol.45, No.2, pp.252-263.
[150] Robinson L.W.1990. Optimal and approximate policies in multi-period, multi-productinventory models with transshipment. Operations Research, Vol.38, No.2, pp.278-295.Rockafellar R.T.1970. Convex Analysis. Princeton University Press, Princeton, New Jersey.
[151] Sabater J., Sierra C.2005. Review on Computational Trust and Reputation Models.Artificial Intelligence Review, Vol.24, No.1, pp.33-60.
[152] Sack E.A.1998. Global yield engineering for IC production. Solid State Technology, Vol.41,No.12, pp.81-85.
[153] Shane H., Klock M.2010. The Relation Between Patent Citations and Tobin's Q in theSemiconductor Industry. Review of Quantitative Finance and Accounting, Vol.9, No.2, pp.131-146.
[154] Shumsky R.A., F.Q. Zhang.2009. Dynamic Inventory Management with Substitution.Operations research, Vol.57, No.3, pp.671-684.
[155] Smith S.A., Aggrawal.2000. with demand substitution Management of multi-item retailinventory systems Operations Research, Vol.29, No.4, pp.50-64.
[156] Talluri K.,Van Ryzin G.2004. The Theory and Practice of Revenue Management. Kluwer,Academic Publishers.
[157] Taskin Z. Caner, A. Tamer ünal.Tactical level planning in float glass manufacturing withco-production, random yields and substitutable products.252-261. Volume199, Number1,November2009
[158] Thomas W.G, Daniel S.C., Sundar B. Retail Out of Stocks: A Worldwide Examination ofExtent, Causes, and Consumer Responses. Working Paper, Emory University, Goizueta BusinessSchool, Atlanta, GA USA; University of St. Gallen, Institute of Technology Mana.
[159] Tobin K.W., Karnowski T.P.2005. Technology considerations for future semiconductor datamanagement systems. Semiconductor Fabric Technology, Vol.12, No.1, pp.57–63.
[160] Tomlin B., Wang Y.2008. Pricing and operational recourse in co-production systems.Management Science, Vol.54, No.3, pp.522-537.
[161] Topkis D.M.1968. Optimal Ordering and Rationing Policies in a Non-stationary DynamicInventory Model with n Demand Classes, Management Science, Vol.15, No.3, pp.160-176.
[162] Uzsoy R., Lee C., Martin-Vega L.A.1992. A review of production planning and schedulingmodels in the semiconductor industry. Part I: system characteristics, performance evaluation andproduction planning. IIE Transport, Vol.24, No.4, pp.47-60.
[163] Van Mieghem J.A., Rudi N.2002. Newsvendor networks: inventory management andinventory investment with discretionary activities. Manufacturing and Service OperationsManagement, Vol.4, No.4, pp.313-335.
[164] Van Slyke R., Wets R.1966. Programming under uncertainty and stochastic optimal control.SIAM Journal on Control, Vol.4, No.1, pp.179-193.
[165] Verhoef P. C.2003. Understanding the effect of customer relationship management effortson customer retention and customer share development. Journal of Marketing, Vol.67, No.4, pp.30-45.
[166] Wang C.X.2009. Random yield and uncertain demand in decentralized supply chains underthe traditional and VMI arrangements, International Journal of Production Research, Vol.47, No.7, pp.1955-1968.
[167] Wollmer R.D.1992. An airline seat management model for a single leg route when lowerfare classes book first. Operations Research.Vol.40, No.1, pp.26-37.
[168] Woodruff R.B,1997. Customer value: The next source for competitive advantage Journal ofthe Academy of Marketing Science, Vol.25, No.2, pp.139-153.
[169] Wu M.C., Chen C.F., Shmih C.F.,2009. Route planning for two wafer fabs with capacitysharing mechanisms. International Journal of Production Research, Vol.47, No.16, pp.5843-5856.
[170] Xu H.,2010. Managing produciton and procutrement through option contruacts insupplychains with random yield, International Journal of Prodcution Economics, Vol.126, No.2,pp.306-313.
[171] Xu H., Yao D.D., Zhen S.H.,2011. Optimal control of replenishmen and substitution in aninventory system with nonstationary batch demand, Produciton and Operations Management,Vol.20, No.5, pp.727-736.
[172] Zhu K., Thonemann U.W.2009. Coordination of pricing and inventory control acrossproducts, Naval Reseach Logistics. Vol.56, No.2, pp.175-190.
[173]赵霞,吴方卫,2009.随机产出与需求下农产品供应链协调的收益共享合同研究,中国管理科学, Vol.17, pp.88-95.
[174]张艾荣,2007.(Q,R)补货及产品替代下的供应链库存模型研究[博士学位论文],华中科技大学.
[175]冉翠玲,杨桂元,2008.两个零售商之间具有产品替代性的订货模型,科技和产业,Vol.8, No.4, pp.66-68,85.
[176]徐德英,柏庆国,2009.两种可替代物品的经济订货批量模型,数学的实践与认识,No.20, pp.16-24.
[177]王磊,梁粱,戴更新.2005.产品替代度与分销渠道的价格竞争,科研管理, Vol.26,No.6, pp.115-123.
[178]王阳,季晓蕾,2010.两产品的多周期替代库存模型,沈阳化工大学学报, Vol.24, No.4,pp.372-375.
[179]李响,李勇建,蔡小强,2009.随机产率和随机需求下的再制造系统的回收定价决策,系统工程理论与实践, No.29,19-27.
[180]言小明,刘克,2008.马氏需求和随机供应下的订货和定价问题,系统科学与数学,Vol.28, No.11, pp.1346-1353.
[181]吴军,2003.供应链环境下可替代产品库存管理研究[博士学位论文],西安交通大学.
[182]蔡连侨,陈剑,严厚民,2003a.可替代产品的库存模型研究(I)：最有订货量,系统工程理论与实践, No.6, pp.63-68.
[183]蔡连侨,陈剑,严厚民,2003b.可替代产品的库存模型研究(I)：基本性质,系统工程理论与实践, No.8, pp.59-68.
[184]关志民,吕芹,马钦海,2005.多种产品替代需求条件下分销系统的库存策略,东北大学学报(自然科学版), Vol.26, pp.504-507.
[185]高峻峻,吕芹,王迎军,胡乐江,2005.替代性需求下的分销系统库存决策,系统工程理论与实践, Vol.14, No.5, pp.437-442.
[186]徐和,顿彩霞,邹旭霞,2011.即予以执行顾客群的零售商补货及反应性定价策略,中国管理科学, Vol.19, pp.115-121.
[2] Agrawal V., Nahmias S.1997. Rationalization of the supplier base in the presence of yielduncertainty. Production and Opereration Management, Vol.6, No.3, pp.291-308
[3] Anupindi R., Kellar R.1993. Diversification under supply uncertainty. Management Science,Vol.39, No.8, pp.944-963.
[4] Achabal D. D., Mcintyre S.H., Smith, S. A.2000. Kalyanam K.A decision support system forvendor managed inventory, Journal of Retailing, Vol.76, No.4, pp.430-454.
[5] Aichlymayr M.2000. DC mart: who manages inventory in a value chain.Transportation andDistribution, Vol.41, No.5. pp.60-68.
[6] Alstrup J,S., Boas,O.B., Madsen G., Vidal R.V.V.1986. Booking policy for flights with twotypes of passengers. European Journal of Operational Research, Vol.27, pp.274-288.
[7] Archibald, T.W., Sassen S.A.E., Thomas L.C.1997. An optimal policy for a two depotinventory problem with stock transfer. Management Science, Vol.43, No.2, pp.173-183.
[8] Axsater S.2003. A new decision rule for lateral transshipments in inventory systems.Management Science, Vol.49, No.9, pp.1168-1179.
[9] Bassok Y., Anupindi R. and Akella, R.1999. Single period multi-product inventory modelswith substitution. Operations Research, No.4, pp.632-642.
[10] Bassok Y., Ernst R.1995. Dynamic allocations for multi-product distribution. TransportationScience, Vol.29, No.3, pp.256-266.
[11] Bassok Y., Akella A.R.1999. Single-period multi-product inventory models with substitution.Operations Research, Vol.47, No.9, pp.632-642.
[12] Basuroy S. Nguyen D.1998. Multinomial logit market share models: Equilibriumcharacteristics and strategic implications, Management Science, Vol.44, No.10, pp.1396-1408.
[13] Bernstein F., Federgruen A.2004. Dynamic inventory and pricing models for competingretailers. Naval Research Logistics, Vol.51, No.2, pp.258–274.
[14] Becker G.M, DeGroot M.H., Marschak J.1964. Measuring utility by a single-responsesequential method. Behavioral Science, Vol.9, No.3, pp.226-232.
[15] Ben-Daya M., Raouf A.1994. Inventory models involving lead time as decision variable.Journal of the Operational Research Society, Vol.45, No.4, pp.579-582.
[16] Bernstein F., Federgruen Awi.2005. Decentralized Supply Chains with Competing RetailersUnder Demand Uncertainty. Management Science, Vol.51, No.1, pp.18-29.
[17] Bitran G.R., Dasu S.1992.Ordering Policies in an Environment of Stochastic Yields andSubstitutable Demands. Operations Research Archive. Vol.40, No.5, pp.999-1017.
[18] Bitran G.R., Leong T.Y.1992. Deterministic Approximations to Co-Production Problems withService Constraints and Random Yields. Management Science, Vol.38, No.5, pp.724-742.
[19] Bitran G. and Dasu S.1992. Ordering policies in an environment of stochastic yields andsubstitutable demand. Operations Research, Vol.50, No.7. pp.999-1017.
[20] Bitran G.R., Gilbert S.M.1996. Managing hotel reservations with uncertain arrivals.Operations Research. Vol.44, No.1, pp.35-49.
[21] Bitran G.R., Tirupati D.1988. Planning and scheduling for epitaxial wafer productionfacilities. Operations Research, Vol.36, No.1, pp.34-49.
[22] Bitran G.R., Dasu S.1992. Ordering policies in an environment of stochastic yields andsubstitutable demands. Operations Research, Vol.40, No.5, pp.999-1017.
[23] Bitran G.R., Leong T.Y.1992. Deterministic approximations to co-production problems withservice constraints and stochastic yields. Management Science, Vol.38, No.5, pp.724-742.
[24] Bitran G.R., Tirupati D.1988. Planning and scheduling for epitaxial wafer productionfacilities. Operational Research, Vol.36, No.6, pp.34-49.
[25] Bollapragada S. Morton T.E.1999. Myopic heuristics fo rthe random yeild problem,Operations research, Vol.47, No.5, pp.713-722.
[26] Bookbinder J.H., Cakanyildirim M.1999. Random lead tines and expedited orders ininventory systems. European Journal of Operational Research, Vol.115, No.2, pp.300-313.
[27] Bowersox D.J., Closs D.J.1996. Logistics Management: the Integrated Supply Chain Process.New York: The McGraw-Hill, Inc.
[28] Brumelle S.L., McGill J.I., Oum T.H., Sawaki K., Tretheway M.W.1990. Allocation ofairline seats between stochastically dependent demand. Transportation Science, Vol.24, No.3,pp.183-192.
[29] Burke M.1996. It's time for vendor managed inventory. Industrial Distribution, Vol.85, No.2, pp.74-90.
[30] Buzzell R.D., Ortmeyer G.1995. Channel partnerships streamline distribution. SloanManagement Review, Vol.36, pp.85-96.
[31] Cachon G.P.2001. Stock wars: Inventory competition in a two-echelon supply chain withmultiple retailers. Operations Research, Vol.49, No.5, pp.658-674.
[32] Cakanyildirim M.1995. Analysis of endogenous lead-time (Q, r) inventory systems. AScThesis, Department of Management Sciences, University of Waterloo, Ont.
[33] Cai X.Q., Chen J., Xiao Y.b., Xu X.L.2010. Optimization and Coordination of Fresh ProductSupply Chains with Freshness Keeping Effort, Production and Operations Management, Vol.19,No.3, pp.261-278.
[34] Carolyn F.S. and Mary A.M.2000. Maximizing the use of vendor managed inventories whileminimizing internal headaches. Dynamic APICS Resource Education, pp.27-32.
[35] Cetinkaya S., Lee C.Y.2000. Stock replenishment and shipment scheduling forvendor-managed inventory system. Management Science, Vol.46, No.2, pp.217-232.
[36] Chao X.L., Chen H. Zheng S.H.2008. Joint replenishment and pricing decisions in inventorysystems with stochastically dependant supply capacity, European Journal of Operational Research,Vol.191, No.1, pp.142-155.
[37] Chen H., Chen J. Chen Y.2006. A coordination mechanism for a supply chain with demandinformation updating.International Journal of Production Economics, Vol.103, No.1,347-361.
[38] Chen F.1998. Echelon reorder points, installation reorder points, and the value of centralizeddemand information. Management Science, Vol.44, No.2, pp.221-334.
[39] Chen F., Drezner Z., Ryan J.K.2000. Quantifying the bullwhip effect in a supply chain: theimpact of forecasting, lead times and information. Management Science, Vol.46, No.3, pp.436-443.
[40] Chen W.C., Chien C.F.2010. Evaluating capacity pooling strategy in semiconductormanufacturing: a productivity perspective study. International Journal of Production Research, Vol.28, No.24, pp.566-588.
[41] Chen J.C., Fan Y.C., and Chen C.W.,2008. Capacity requirements planning for twin fabs ofwafer fabrication. International Journal of Production Research. Vol.41, No.16, pp.3921-3941.
[42] Cheung K., Lee L.H.,2002. The inventory Benefit of Shipment Coordination and Stockrebalancing in a Supply Chain.Management Science, Vol.48, No.2, pp.300-307.
[43] Chien C.F. Hsu C.,2006. A novel method for determining machine subgroups and backupswith an empirical study for semiconductor manufacturing. Journal of Intelligent Manufacturing,Vol.17, No.4, pp.429-440.
[44] Chien C.F., Hsu C.2007. Construct the OGE for promoting tool group productivity insemiconductor manufacturing. International Journal of Production Research, Vol.45, No.3, pp.509-524.
[45] Chopra S.2001. Meindl P. Supply chain management: Strategy, planning, and operations.Prentice-Hall, Inc.
[46] Chopra S., Meindl P.2001. Supply chain management: Strategy, planning, and operations.Prentice-Hall, Inc.
[47] Corbett C.J. and Rajaram K.,2006. A generalization of the inventory pooling effect tononnormal dependent demand. Manufacturing and Service Operations Management, Vol.84, No.4, pp.351-358.
[48] Cottrill K.1997. Reforging the supply chain. Journal of Business Strategy, Vol.18, No.6, pp.35-39.
[49] Cunningham, S.P., Spanos C.J., Voros K.1995. Semiconductor yield improvement: resultsand best practices. IEEE Transport. Semiconductor Manufacture, Vol.8, No.2, pp.103-109.
[50] Curry R.1990. Optimal seat allocation with fare classes nested by Original destinations.Transportation Science，Vol.24, No.3, pp.193-204.
[51] Dada M., Nicholas C. Petruzzi L. Schwarz B.2007. A newsvendor's procurement problemwhen suppliers are unreliable. Manufacturing Service Opereation Management, Vol.9, No.1,pp.9-32.
[52] Dai Y.2005. Pricing in revenue management for multiple firms competing for customers.International Journal of Production Economics, Vol.98, No.3, pp.1-16.
[53] Dong Y, Xu K.2002. A supply chain model of vender managed inventory.TransportationResearch Part E, Vol.38, No.4, pp.75-95.
[54] Doniavi A., Mileham A.R., Newnes L.B.1996. A systems approach to modeling in themanufacturing environment, in Proceedings of the12th National Conference on ManufacturingResearch, Bath, September.
[55] Drezner Z., Ryan J.K.2000. Quantifying the bullwhip effect in a simple supply chain: Theimpact of forecasting, lead times, and information. Management Science, Vol.46, No.3, pp.436-443.
[56] Duenyas I., Tsal C.Y.2000. Control of a manufacturing system with random product yieldand downward substitutability. IIE Transactions. Vol.30, No.2, pp.785-795.
[57] Ehrhardt R.1984.(s, S) Policies for a dynamic inventory model with stochastic lead time.Operations Research, Vol.32, No.1, pp.121-132.
[58] Ernst R., Kouvelis P.1999. The effects of selling packaged goods on inventory decisions.Management Science, Vol.45, pp.8, pp.1142-1155.
[59] Fangruo C., Federgruen2001. A and Yu-sheng Near-optimal pricing and replenishmentstrategies for a retail/distribution system. Operations Research. Vol.49, No.6, pp.839-853.
[60] Farley G.A.1997. Discovering Supply Chain Management: a Roundtable DiscussionAPICS-The Performance Advantage, Vol.7, No.1, pp.38-39.
[61] Federgruen A., Yang N,2009. Optimal supply diversification under general supply risks.Operations Research, Vol.57, No.6, pp.1451-1468.
[62] Federgruen A., Yang N.2008. Selecting a Portfolio of suppliers under demand and supplyrisks. Operations Research, Vol.56, No.4, pp.916-936.
[63] Feng Y.Y., Xiao B.C.2000. Optimal Policies of Yield Management with MultiplePredetermined Prices. Operations Research, Vol.48, No.2, pp.332-343.
[64] Forsberg R.1995. Optimization of order-up-to S policies for two-level inventory systemswith compound Poisson demand. European Journal of Operational Research, Vol.81, No.1, pp.143-153.
[65] Frank K.C., Zhang R.Q., Duenyas I.2003. Optimal inventory policies for inventory systemswith priority demand classes. Operations Research. Vol.51, No.6, pp.993-1002.
[66] Gallego G., Katircioglu K., Ramachandran B.,2006.Semiconductor inventory managementwith multiple grade parts and downgrading. Production planning and control. Vol.17, No.7,pp.689-700.
[67] Gan B.P.2007. Analysis of a borderless fab using interoperating AutoSched AP models.International Journal of Production Research, Vol.45, No.3, pp.675-697.
[68] Ganeshan R.1999. Managing supply chain inventories: A multiple retailer, one warehouse,multiple supplier model. International Journal of Production Economics, Vol.59, No.5, pp.341-354.
[69] Gerber N.1999. Objective comparisons of consignment, just-in-time, and stockless.HospitalMaterial Management Quarterly, Vol.13, No.1, pp.10-17.
[70] Gerchak Y, Khmelnitsky E.2003. A consignment system where suppliers cannot verifyretailer's sales reports. International Journal of Production Economics, Vol.83, No.3, pp.37-43.
[71] Gerchak Y., Tripathy A., Wang K.1996. Co-production models with stochastic functionalityyields. IIE Transactions, Vol.28, No.5, pp.391-403.
[72] Goyal S.K.1977. An integrated inventory model for a single supplier-single customerproblem. International Journal of Production Research, Vol.15, No.1, pp.107-111.
[73] Gruen T.W. D.S. Corsten Bharadwaj S.2002. Retial out-of-stocks: A worldwide examinationof extent, casues and consumer responsees. Working paper, Goizueta Bussiness School, EmoryUniversity, Atlanta.
[74] Gules H.K., Burgess T.F.1996. Manufacturing technology and the supply chain: Linkingbuyer-supplier relationships and advanced manufacturing technology. European Journal ofPurchasing and Supply Management, Vol.2, No.1, pp.31-38.
[75] Ha A.Y.1997a. Inventory rationing in a make-to-stock production system with severaldemand classes and lost sales. Management Science, Vol.43, No.8, pp.1093-1103.
[76] Ha A.Y.1997b. Stock rationing policy for a make-to-stock production system with twopriority classes and backordering. Naval Research Logistics, Vol.44, No.5, pp.457-472.
[77] Ha A.Y.2000. Stock rationing in an M/E/1make-to-stock queue. Management Science, Vol.46, No.1, pp.77-87.
[78] Hall B.H.1993. The Stock Market's Valuation of RandD Investment During the1980's. TheAmerican Economic Review, Vol.83, No.2, pp.259-264.
[79] Henig M., Gerchak Y.1990. The structure of periodic review policies in the presence ofstochastic yield. Operations Research, Vol.38, No.4, pp.634–643.
[80] Hopp W.J.,2008. A Static Approximation for Dynamic Demand Substitution withApplications in a Competitive Market, Operations Research, Vol,56, No.3, pp.630-645.
[81] Horton D.1998. Modeling the yield of mixed-technology die. Solid State Technology, Vol.41, No.9,109-119.
[82] Hsu V.N., Liu C.L., Xiao W.Q.2005. Dynamic lot size problem with one way prodctuionsubstitution.IIE Tranctions. Vol.34, No.2, pp.74-89.
[83] Hsu, A., Bassok Y.1999. Stochastic Yield and Stochastic Demand in a Production Systemwith Downward Substitution. Operations Research, Vol.47, No.2, pp.277-290.
[84] Hu X.X. Duenyas I. Kapuscineski R.2008. Oprimal joint inventory and transshipmentcontrol under uncertain capacity, Operations Research, Vol.56, No.3, pp.881-897.
[85] Huang C.2004. An optimal policy for a single-vendor single-buyer integratedproduction-inventory problem with process unreliability consideration.International Journal ofProduction Economics, Vol.91, No.3, pp.91-98.
[86] Huang H.C., Song H.Q.2010. Modified Base-stock Policies for Semiconductor ProductionSystem with Dependent Yield Rates. European Journal of Operational Research. Vol.45, pp.39-54.
[87] Ge Z.Q.2010. Semiconductor Manufacturing Process Monitoring Based on AdaptiveSubstatistical PCA. IEEE Transactions on Semiconductor Manufacturing. Vol.23, No.1, pp.99-108.
[88] Iyer A. V, Bergen M.E.1997. Quick response in manufacturer-retailer channels. ManagementScience, Vol.43, No.4, pp.559-570.
[89] Jackson B.B.1985. Build customer relation-ships that last. Harvard Business Review, Vol.63, No.6, pp.120-148.
[90] Jain D., Singh S.2002. Customer lifetime value researches in marketing: a review anddirections. Journal of interactive marketing, Vol.16, No.2, pp.34-41
[91] Jeuland A.P., Shugan S.M.1983. Managing channel profits. Marketing Science, Vol.2, No.3,pp.239-272.
[92] Jia J., Mason S.J.2009. Semiconductor manufacturing scheduling of jobs containing multipleorders on identical parallel machines. International Journal of Production Research, Vol.47, No.10, pp.2565-2585.
[93] Jordan W.C., Graves S.C.1995. Principles on the benefit of manufacturing process flexibility.Management Science, Vol.41, No.4, pp.577-594.
[94] Karaesmen I., van Ryzin G.J.2004. Overbooking with substitutable inventory classes.Operations Research, Vol.52, No.1, pp.83-104.
[95] Karmarkar U.1981. The multi-period multi-location inventory problem. Operations Research,Vol.29, No.2, pp.215-228.
[96] Karmarkar U., Lin S.C.1987. Production planning with uncertain yields and demands.Working Paper. Simon Graduate School of Business Administration, University of Rochester,Rochester, NY.
[97] Karmarkar U.S.1981. The multi-period multi-location inventory problem. OperationsResearch. Vol.29, No.2, pp.215-228.
[98] Kazaz B.2004. Production Planning Under Yield and Demand Uncertainty withYield-Dependent Cost and Price, Manufacturing and Service Operations Management Summer,Vol.6, No.3, pp.209-224.
[99] Keller G, Noori H.1988. Justifying new technology acquisition through its impact on the costof running an inventory policy. IIE Transitions, Vol.20, No.3, pp.284-291.
[100] Kim K.L., Hayya J.C.1992. Hong J.D.Setup reduction in economic production quantitymodel. Decision Sciences, Vol.23, No.1, pp.500-508.
[101] Kimes, S.E.1989. The Cornell Hotel and Restaurant Administration Quarterly. The basics ofyield management. Vol.30, No.3, pp.14-19.
[102] Kohli R., Park H.1994.Coordinating buyer-seller transactions across multiple products.Management Science, Vol.40, No.9, pp.45-50.
[103] Kothari V.1984. Silicon wafer manufacture. Unpublished thesis, Sloan school ofmanagement, MIT, Cambridge, Massachusetts.
[104] Kraiselburd S., Narayanan V.G..2004. A Contracting in a supply chain with stochasticdemand and substitute products. Production and Operations Management, Vol.13, No.1, pp.46-62.
[105] Kraiselburd S.2006. When is Vendor Managed Inventory Good for the Retailer? Impact ofRelative Margins and Substitutions Rates. Work Paper, Harvard Business School.
[106] Krajewski L.J. and Ritzman L.P.1996. Operations Management: Strategy and Analysis.Addision-Wesley, MA.
[107] Krupp J.A.G.1997. Safety Stock Management. Production and Inventory ManagementJournal, Vol.38, No.3, pp.11-18.
[108] Kuehl R.1999. Design of experiments: statistical principles of research design and analysis(second version). Duxbury Press, California, USA.
[109] Kumar N., Kennedy K., Gildersleeve K., Abelson R., Mastrangelo C.M., Montgomery D.C.2006. A review of yield modeling techniques for semiconductor manufacturing, InternationalJournal of Production Research, Vol.44, No.23, pp.5019-5036.
[110] Lal R., Staelin R.1984. An approach for developing an optimal discount pricing policy.Management Science, Vol.30, No.12, pp.1524-1539
[111] Lambert D.M, Cooper M.C.2000. Issues in the supply chain management. IndustrialMarketing Management, Vol.29, No.3, pp.65-83.
[112] Lee C.C., Chu W.H.J.2005. Who should control inventory in a supply chain. EuropeanJournal of Operational Research, Vol.164, No.1, pp.158-172.
[113] Lee H.L., Billington C.1992. Supply chain management: Pitfalls and opportunities.SloanManagement Review, Vol.33, No.3, pp.65-73.
[114] Lee H.L., Billington C.1996. The Evolution of Supply-chain management models andpractices at Hewlett Packard. Interfaces, Vol.25, No.5,42-63.
[115] Lee H.L., So K.C., Tang C.S.2000. The value of information sharing in a two-level supplychain. Management Science, Vol.46, No.5,626-643.
[116] Lee H.L., Yano C.A.1988. Production control in multistage systems with variable yieldlosses. Operations Research, Vol.36, No.2, pp.269-278.
[117] Lee H., Padamanabhan P.1997. Whang, S. Information distortion in a supply chain: thebullwhip effect. Management Science, Vol.43, No.4, pp.546-558.
[118] Lemon K.N., Verhoef P.C.2003. The Theoretical Underpinnings of Customer AssetManagement: A Framework and Propositions for Future Research. Journal of the Academy ofMarketing Science, Vol.32, pp.271-292
[119] Li Q., Xu H., Zheng S.H.,2008. Periodic review inventory systems with random yield:bounds and heuristics, IIE Transacitons, Vol.40, pp.434-394.
[120] Li Q., Zheng S.,2006. Joint inventory replenishment and pricing control for systems withuncertain yield and demand, Operations Research, Vol.54, No.4, pp.696-705.
[121] Lippman S.A., McCardle K.F.,1997. The competitive newsboy. Operations Research,Vol.45, No.1, pp.54-65.
[122] Liu L.J.2006. Supply chain coordination with quantity discount policy. International Journalof Production Economics, Vol.101, No.1, pp.89-98.
[123] Liao C.J., Shuyu C.H.1991. An analytical determination of lead time with normal demand.International Journal of Operations and Production Management, Vol., No.2, pp.72-78.
[124] Mahajan S., Ryzin G.1999. Inventory Competitive Under Dynamic Consumer Choice.Working Paper. Columbia Business School.
[125] Mahajan S., Ryzion G.2001a. Stocking retail assortments under dynamic consumersubstitution. Operations Research, Vol.49, No.3, pp.334-351.
[126] Mahajan S. Van Ryzion G.2001b. Inventory competition under dynamix consumer choice.Operations Research, Vol.49, No.5, pp.646-657.
[127] Mallik S.2007. Contracting over multiple parameters: Inventory allocation insemiconductor manufacturing. European Journal of Operational Research, Vol.182, No.1, pp.174-193.
[128] Manchanda P., Dubé J.P., Goh K.Y., Chintagunta P. K.2006. The Effect of BannerAdvertising on Internet Purchasing. Journal of Marketing Research. Vol.43, No.1, pp.98-108.
[129] McGillivray A.R., Silver E.A.1978. Some concepts for inventory control undersubstitutable demand. INFOR, Vol.16, No.1, pp.47-63.
[130] Mishra B. K., Raghunathan S.2004. Retailer-vs. vendor-managed inventory and randcompetition. Management Science, Vol.50, No.4, pp.445-457.
[131] Mitchell J.S., Slamet P.1995. The Meaning and Management of yield in Hotels.International journal of contemporary Hospitality Management, Vol.7, No.4, pp.23-26.
[132] Moinzadeh K., Lee H.L.1987. A continuous-review inventory model with constantre-supply time and defective items. Naval Research Logistics. Vol.29, No.2, pp.457–467.
[133] Moinzadeh K., Lee H.L.1987. A continuous-review inventory model with constantre-supply time and defective items. Naval Research Logistics, Vol.34, No.4, pp.457-467.
[134] Moon L, Choi S.1998. A note on lead time and distributional assumptions in continuousreview inventory models. Computers and Operations Research, Vol.29, No.2, pp.1007-1012.
[135] Nadiri M.I., Prucha I.1996. Estimation of the depreciation rate of physical and RandDcapital in the U.S total manufacturing sector. Economic Inquiry, Vol.34, No.1, pp.43-56.
[136] Nagarajan M., Rajagopalan S.2008. Inventory models for substitutable products:Optimalpolicies and heuristics, Management Scinence, Vol.54No.2, pp.1453-1466.
[137] Narayanan V.G, Raman A.2005. Agency Costs in a Supply Chain with Demand Uncertaintyand Price Competition. Management Science, Vol.51, No.1, pp.120-132.
[138] Nasri F., Affisco J.F., Paknejad M.J.1990. Setup cost reduction in an inventory model withfinite-range stochastic lead times. International Journal of Production Research, Vol.29, No.2, pp.199-212.
[139] Netessine S. Rudi N. Centralized and competitive inventory models with demandsubstitution, Operations Research, Vol.51, No.2, pp.329-335.
[140] Parlar M.1988. Game theoretic analysis of the substitutable product inventory problem withrandom demands.Naval Research Logistics, Vol.29, No.2, pp.397-409.
[141] Pasternack B.A., Drezner Z.1991. Optimal inventory policies for substitutable commoditieswith stochastic demand. Naval Research Logistics, Vol.38, No.2, pp.221-240.
[142] Pasternack B.A., Drezner Z.1991.Optimal inventory policies for substitutable commoditieswith stochastic demand. Naval Research Logistics, Vol.38, No.2, pp.221-240.
[143] Peikert A., Thoma J., Brown S.1998. Rapid modeling technique for measurableimprovements in factory performance. IEEE Winter Simulation. Conference, pp.1011–1015.
[144] Pentico D.1974.The assortment problem with stochastic demand. Management Science, Vol.21, No.2, pp.268-290.
[145] Phan D.D., Vogel D.R.2010. A model of customer relationship management and businessintelligence systems for catalogue and online retailers. Information and Management, Vol.47, No.2, pp.69-77.
[146] Quirk M., Serda J.2001. Semiconductor manufacturing technology. Upper Saddle River,NJ: Prentice Hall.
[147] Rajaram K., Tang C.S.2001. The impact of product substitution on retail merchandising [J].European Journal of Operational Research, Vol.29, No.2, pp.582-601.
[148] Rao U.S., Swaminathan J.M., Zhang J.2004. Multi-product inventory planning withdownward substitution, stochastic demand and setup costs. IIE Transactions. Vol.36, No.1, pp.59-71.
[149] Robinson L.W.1994. Optimal and approximate control policies for airline booking withsequential fare classes. Operations Research, Vol.45, No.2, pp.252-263.
[150] Robinson L.W.1990. Optimal and approximate policies in multi-period, multi-productinventory models with transshipment. Operations Research, Vol.38, No.2, pp.278-295.Rockafellar R.T.1970. Convex Analysis. Princeton University Press, Princeton, New Jersey.
[151] Sabater J., Sierra C.2005. Review on Computational Trust and Reputation Models.Artificial Intelligence Review, Vol.24, No.1, pp.33-60.
[152] Sack E.A.1998. Global yield engineering for IC production. Solid State Technology, Vol.41,No.12, pp.81-85.
[153] Shane H., Klock M.2010. The Relation Between Patent Citations and Tobin's Q in theSemiconductor Industry. Review of Quantitative Finance and Accounting, Vol.9, No.2, pp.131-146.
[154] Shumsky R.A., F.Q. Zhang.2009. Dynamic Inventory Management with Substitution.Operations research, Vol.57, No.3, pp.671-684.
[155] Smith S.A., Aggrawal.2000. with demand substitution Management of multi-item retailinventory systems Operations Research, Vol.29, No.4, pp.50-64.
[156] Talluri K.,Van Ryzin G.2004. The Theory and Practice of Revenue Management. Kluwer,Academic Publishers.
[157] Taskin Z. Caner, A. Tamer ünal.Tactical level planning in float glass manufacturing withco-production, random yields and substitutable products.252-261. Volume199, Number1,November2009
[158] Thomas W.G, Daniel S.C., Sundar B. Retail Out of Stocks: A Worldwide Examination ofExtent, Causes, and Consumer Responses. Working Paper, Emory University, Goizueta BusinessSchool, Atlanta, GA USA; University of St. Gallen, Institute of Technology Mana.
[159] Tobin K.W., Karnowski T.P.2005. Technology considerations for future semiconductor datamanagement systems. Semiconductor Fabric Technology, Vol.12, No.1, pp.57–63.
[160] Tomlin B., Wang Y.2008. Pricing and operational recourse in co-production systems.Management Science, Vol.54, No.3, pp.522-537.
[161] Topkis D.M.1968. Optimal Ordering and Rationing Policies in a Non-stationary DynamicInventory Model with n Demand Classes, Management Science, Vol.15, No.3, pp.160-176.
[162] Uzsoy R., Lee C., Martin-Vega L.A.1992. A review of production planning and schedulingmodels in the semiconductor industry. Part I: system characteristics, performance evaluation andproduction planning. IIE Transport, Vol.24, No.4, pp.47-60.
[163] Van Mieghem J.A., Rudi N.2002. Newsvendor networks: inventory management andinventory investment with discretionary activities. Manufacturing and Service OperationsManagement, Vol.4, No.4, pp.313-335.
[164] Van Slyke R., Wets R.1966. Programming under uncertainty and stochastic optimal control.SIAM Journal on Control, Vol.4, No.1, pp.179-193.
[165] Verhoef P. C.2003. Understanding the effect of customer relationship management effortson customer retention and customer share development. Journal of Marketing, Vol.67, No.4, pp.30-45.
[166] Wang C.X.2009. Random yield and uncertain demand in decentralized supply chains underthe traditional and VMI arrangements, International Journal of Production Research, Vol.47, No.7, pp.1955-1968.
[167] Wollmer R.D.1992. An airline seat management model for a single leg route when lowerfare classes book first. Operations Research.Vol.40, No.1, pp.26-37.
[168] Woodruff R.B,1997. Customer value: The next source for competitive advantage Journal ofthe Academy of Marketing Science, Vol.25, No.2, pp.139-153.
[169] Wu M.C., Chen C.F., Shmih C.F.,2009. Route planning for two wafer fabs with capacitysharing mechanisms. International Journal of Production Research, Vol.47, No.16, pp.5843-5856.
[170] Xu H.,2010. Managing produciton and procutrement through option contruacts insupplychains with random yield, International Journal of Prodcution Economics, Vol.126, No.2,pp.306-313.
[171] Xu H., Yao D.D., Zhen S.H.,2011. Optimal control of replenishmen and substitution in aninventory system with nonstationary batch demand, Produciton and Operations Management,Vol.20, No.5, pp.727-736.
[172] Zhu K., Thonemann U.W.2009. Coordination of pricing and inventory control acrossproducts, Naval Reseach Logistics. Vol.56, No.2, pp.175-190.
[173]赵霞,吴方卫,2009.随机产出与需求下农产品供应链协调的收益共享合同研究,中国管理科学, Vol.17, pp.88-95.
[174]张艾荣,2007.(Q,R)补货及产品替代下的供应链库存模型研究[博士学位论文],华中科技大学.
[175]冉翠玲,杨桂元,2008.两个零售商之间具有产品替代性的订货模型,科技和产业,Vol.8, No.4, pp.66-68,85.
[176]徐德英,柏庆国,2009.两种可替代物品的经济订货批量模型,数学的实践与认识,No.20, pp.16-24.
[177]王磊,梁粱,戴更新.2005.产品替代度与分销渠道的价格竞争,科研管理, Vol.26,No.6, pp.115-123.
[178]王阳,季晓蕾,2010.两产品的多周期替代库存模型,沈阳化工大学学报, Vol.24, No.4,pp.372-375.
[179]李响,李勇建,蔡小强,2009.随机产率和随机需求下的再制造系统的回收定价决策,系统工程理论与实践, No.29,19-27.
[180]言小明,刘克,2008.马氏需求和随机供应下的订货和定价问题,系统科学与数学,Vol.28, No.11, pp.1346-1353.
[181]吴军,2003.供应链环境下可替代产品库存管理研究[博士学位论文],西安交通大学.
[182]蔡连侨,陈剑,严厚民,2003a.可替代产品的库存模型研究(I)：最有订货量,系统工程理论与实践, No.6, pp.63-68.
[183]蔡连侨,陈剑,严厚民,2003b.可替代产品的库存模型研究(I)：基本性质,系统工程理论与实践, No.8, pp.59-68.
[184]关志民,吕芹,马钦海,2005.多种产品替代需求条件下分销系统的库存策略,东北大学学报(自然科学版), Vol.26, pp.504-507.
[185]高峻峻,吕芹,王迎军,胡乐江,2005.替代性需求下的分销系统库存决策,系统工程理论与实践, Vol.14, No.5, pp.437-442.
[186]徐和,顿彩霞,邹旭霞,2011.即予以执行顾客群的零售商补货及反应性定价策略,中国管理科学, Vol.19, pp.115-121.
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