仓储系统中存储策略与设备维护策略研究
|
摘要
自上世纪末以来,随着信息技术与电子商务的高速发展,关于物流与供应链的研究已成为管理科学领域的一个研究热点。其中,作为物流与供应链重要节点的仓储系统尤其受到学者们的钟爱。实践中,B2C与C2C电子商务的高速发展对仓储物流企业的订单响应效率提出了新的挑战。
基于上述市场环境,本文研究了影响仓储系统订单响应的两个重要因素：物品的存储策略和存取设备的可靠性。根据研究点的不同,本文的主要研究内容可以分为两个部分：
第一部分是关于存储策略的研究,从物品存储方式上优化系统对订单的响应时间,提高运作效率。本部分的研究分两章进行,其中第2章通过考虑仓储系统中所存储物品种类的有限性的影响,放松了现有文献中“物品所需存储空间等于其平均库存”的假设,对分类存储策略在自动存取系统(AS/RS)中的应用进行研究。文章首先以SIT的连续货架为基于分类存储策略的AS/RS建立存取物品的平均行程时间的基本模型,继而陆续通过放松相关假设的方式对基本模型进行扩展研究,证明了基本模型的鲁棒性。其中包括货架的形状、需求曲线的类型、存储物品种类数量的变化、离散的存储空间以及随机的物品需求等。此外,在第3章中,我们在考虑仓储系统中物品所需的实际存储空间的情况下对各类存储策略(包括随机存储策略、分类存储策略和全周转率存储策略)的运作绩效进行了评价。
仓储系统的订单响应效率不仅仅取决于系统所采取的物品存储策略,还会受到存取设备可靠性的影响。因此,本文第二部分的研究内容是如何确定企业对设备的维护策略,以实现企业利润最大化的目标。本文此部分的研究也由两章内容构成。第4章在考虑企业自已通过设立备件库存与维修车间的方式来维护设备的情形下,通过放弃设备可靠性目标,进而考虑设备盈利能力的方式,以企业期望利润最大化为决策目标,对企业的最优决策进行了研究。进而通过对扩展模型的分析,考察了以企业联盟的方式建立多个企业共用的维修车间和备件库存的决策方式的效果。随后,第5章在第4章的基础上,在维护服务外包的情形下对企业维护合同进行了优化研究。基于PBC的合同机制,用一个序贯博弈模型来刻画顾客(即持有设备的企业)和售后服务供应商之间的博弈关系,并通过纳什博弈模型描述了多个顾客之间的竞争关系。
基于上述研究内容,本文的主要创新点与贡献总结如下：
1)本文第2章在考虑仓储系统中所存储物品种类有限的情况下,发现传统文献中认为理论上最优的全周转率存储策略并不能为仓储系统提供最短的平均行程时间。解释了理论中“全周转率存储策略最优”的结论在实践中得不到企业应用的原因,即传统文献在考虑行程时间模型时仅考虑了分类为存取效率带来的提升,却忽视了分类为仓储系统带来的存储空间的膨胀。
2)通过考虑仓储系统所需要的实际存储空间,本文第3章考察了仓储系统中物品的需求结构(ABC需求曲线的曲度)对存储策略的绩效影响。在传统文献的研究中,我们发现对于任意的ABC需求曲线,随机存储策略为系统带来的平均行程时间是相同的。而在我们考虑不同物品需求结构所需要的实际存储空间时,我们发现随机存储策略为仓储系统带来的平均行程时间对于不同的ABC需求曲线存在着很大的变化。
3)基于上述1)和2)的理论结果,本文给出了AS/RS系统和基于通道的传统仓库的最优分类策略及仓库的最优形状。
4)本文第4章提出了考虑设备盈利能力的售后维护策略,并给出了相应的最优决策。研究发现,设备的最优可靠性需求是随其盈利能力灵活变化的。基于给定设备可靠性目标的决策方式可能会削减企业的利润,尤其是当企业一味的追求设备高可靠性时,企业可能会由于设备可靠性目标与其盈利能力不匹配而造成利润上的重大损失。
5)在企业设备维护服务外包的环境下,第5章用博弈论的方法对售后服务供应链进行了研究。研究发现,(i)对维修服务供应商来说,当备件的单位成本较低时,共享库存确实能够使服务供应商节省维护成本；而当备件的单位成本较高时,共享库存将会伤害供应商的利润,此时为各顾客设立专用备件库存是供应商的最优选择。(ii)当同一个市场中的多名顾客存从同一个售后服务供应商处获取设备维护服务,且供应商为顾客设立共享的备件库存时,由于顾客可以通过成本分摊和缺货惩罚两种途径激励供应商提供所需要的设备可靠性,所以规模较大的顾客可以通过设置与规模小的顾客相同的成本分摊来避免其搭便车行为,而从增加缺货惩罚的角度来迫使供应商提供必要的设备可靠性。因此,所有的顾客,无论其所持有的设备数量多少、盈利能力高低,他们都将为供应商提供相同数额的成本补贴。
Along with the development of Internet Technology and Electronic Commerce, logistics and supply chain becomes one of the most popular research topics since the end of last century. Wherein, warehousing systems, as important nodes in supply chains, receive great attentions from the scholars. In practice, the rapid development of B2C and C2C e-commerce brings the warehouses new challenges in order response.
Under the business environment described above, this thesis studies the most important issues related with order response efficiency of warehousing systems:the storage policies of the system and the availability of the storage/retrieval (S/R) machine. According to these two research issues, this thesis can be partitioned into two parts.
The first part of this thesis focuses on storage policies of the warehousing systems, analyzes how to improve the operational efficiency based on different storage policies. The discussions of this part lie in Chapters2and3. In Chapter2, by considering a finite number of items stored in the system, we relax the following basic assumption in the literature:required storage space of all the items equals their average inventory level, which is valid only if an infinite number of items are stored in each storage region. We first give the basic one way travel-time model based on a square-in-time (SIT) continuous storage rack. Then we relax the assumptions by considering non-SIT storage racks, a different type of ABC demand curve, different number of stored items, discrete storage racks and stochastic item demands, to validate the robustness of the basic model. After that, in Chapter3, we study the performance of different storage policies (including random storage, class-based storage and turnover-based storage) in a unit-load traditional warehouse with a finite number of items by considering the effect of realistic required storage space.
The response efficiency depends on not only the storage policies, but also the availability of the S/R machine. Consequently, in the second part of this thesis, we focus on how to maximize the expected profit of storage enterprises through optimal maintenance strategies. In Chapter4, we focus on the optimization problem of the storage enterprise to determine the optimal spare inventory and cost reduction effort when it does the equipment maintenance itself. In the discussion, different from the literature, we focus on the maximal profit of the enterprise considering the operational requirement (i.e., profitability) of the S/R machine instead of a given availability requirement. Additionally, we extend the model to an alliance model by considering multiple enterprises in the same market. Based on the analysis of Chapter4, Chapter5studies the maintenance strategy in a decentralized scenario when the support service is provided by the maintenance suppliers. A Stackelberg game model is adopted to describe the relationship between the storage enterprises and the maintenance suppliers based on performance-based contract.
Based on the analysis provided above, the main innovations and contributions of this thesis are summarized as follows:
1) Through the analysis in Chapter2, we find that commonly a small number (3to5) of classes is optimal for the warehousing system. This is in consistent with that a small number of classes is very close to warehousing practice where often only three storage classes are used. In addition, we find that the shortest travel time is fairly insensitive to the number of storage classes in a wide range around the optimum. This suggests a manager can select a near optimal number of storage classes in an easy way and he or she should not be worried about the impact of storage-class reconfigurations.
2) By considering the realistic required storage space of stored items in a warehousing system, we exam the effect of item demand structure (ABC demand curve) on storage policy performance in Chapter3. Different from the findings in the literature, we find that under the same storage policy, items with different ABC demand curves lead the system different average travel time/distance.
3) Based on the theoretical findings presented in point1) and2), we give the optimal item classification and warehouse shape factor for AS/RSs and aisle-based traditional warehouses.
4) A new maintenance strategy based on the profitability of the equipment is proposed in Chapter4. Through the discussion, we find that the optimal availability requirement of the equipment is not constant, but increasing with its profitability. Seeking high availability regardless with the profitability of the equipment will hurt the expect profit of the enterprise.
5) When the maintenance service is provided by the suppliers, we model the after-sales service supply chain through game models considering the profitability of the equipment in Chapter5. The findings tell that (i) the shared inventory strategy is not always better than dedicate spare inventory for each enterprise from the suppliers' perspective. When the unit cost of the spare part is relatively high, it is better for the supplier to set dedicate spare inventories.(ii) At the game equilibrium between the storage enterprises and the suppliers, all the enterprises will provide the same cost reimbursement to the suppliers even they hold different number of equipment with different profitability. This is a result of that with performance-based contract, each enterprise has two terms to influence the decisions of the supplier, especially the decision on spare inventory which influence the system availability directly. One is the cost reimbursement which provides a pull effect for the suppliers, and the other one is backorder punishment which provides a push effect. In the Nash game among the enterprises, it is a dominant strategy for the enterprises with large number of equipment or high profitability to select the same cost reimbursement as other ones and provide a high backorder punishment to push the spare inventory to the desired level.
基于上述市场环境,本文研究了影响仓储系统订单响应的两个重要因素：物品的存储策略和存取设备的可靠性。根据研究点的不同,本文的主要研究内容可以分为两个部分：
第一部分是关于存储策略的研究,从物品存储方式上优化系统对订单的响应时间,提高运作效率。本部分的研究分两章进行,其中第2章通过考虑仓储系统中所存储物品种类的有限性的影响,放松了现有文献中“物品所需存储空间等于其平均库存”的假设,对分类存储策略在自动存取系统(AS/RS)中的应用进行研究。文章首先以SIT的连续货架为基于分类存储策略的AS/RS建立存取物品的平均行程时间的基本模型,继而陆续通过放松相关假设的方式对基本模型进行扩展研究,证明了基本模型的鲁棒性。其中包括货架的形状、需求曲线的类型、存储物品种类数量的变化、离散的存储空间以及随机的物品需求等。此外,在第3章中,我们在考虑仓储系统中物品所需的实际存储空间的情况下对各类存储策略(包括随机存储策略、分类存储策略和全周转率存储策略)的运作绩效进行了评价。
仓储系统的订单响应效率不仅仅取决于系统所采取的物品存储策略,还会受到存取设备可靠性的影响。因此,本文第二部分的研究内容是如何确定企业对设备的维护策略,以实现企业利润最大化的目标。本文此部分的研究也由两章内容构成。第4章在考虑企业自已通过设立备件库存与维修车间的方式来维护设备的情形下,通过放弃设备可靠性目标,进而考虑设备盈利能力的方式,以企业期望利润最大化为决策目标,对企业的最优决策进行了研究。进而通过对扩展模型的分析,考察了以企业联盟的方式建立多个企业共用的维修车间和备件库存的决策方式的效果。随后,第5章在第4章的基础上,在维护服务外包的情形下对企业维护合同进行了优化研究。基于PBC的合同机制,用一个序贯博弈模型来刻画顾客(即持有设备的企业)和售后服务供应商之间的博弈关系,并通过纳什博弈模型描述了多个顾客之间的竞争关系。
基于上述研究内容,本文的主要创新点与贡献总结如下：
1)本文第2章在考虑仓储系统中所存储物品种类有限的情况下,发现传统文献中认为理论上最优的全周转率存储策略并不能为仓储系统提供最短的平均行程时间。解释了理论中“全周转率存储策略最优”的结论在实践中得不到企业应用的原因,即传统文献在考虑行程时间模型时仅考虑了分类为存取效率带来的提升,却忽视了分类为仓储系统带来的存储空间的膨胀。
2)通过考虑仓储系统所需要的实际存储空间,本文第3章考察了仓储系统中物品的需求结构(ABC需求曲线的曲度)对存储策略的绩效影响。在传统文献的研究中,我们发现对于任意的ABC需求曲线,随机存储策略为系统带来的平均行程时间是相同的。而在我们考虑不同物品需求结构所需要的实际存储空间时,我们发现随机存储策略为仓储系统带来的平均行程时间对于不同的ABC需求曲线存在着很大的变化。
3)基于上述1)和2)的理论结果,本文给出了AS/RS系统和基于通道的传统仓库的最优分类策略及仓库的最优形状。
4)本文第4章提出了考虑设备盈利能力的售后维护策略,并给出了相应的最优决策。研究发现,设备的最优可靠性需求是随其盈利能力灵活变化的。基于给定设备可靠性目标的决策方式可能会削减企业的利润,尤其是当企业一味的追求设备高可靠性时,企业可能会由于设备可靠性目标与其盈利能力不匹配而造成利润上的重大损失。
5)在企业设备维护服务外包的环境下,第5章用博弈论的方法对售后服务供应链进行了研究。研究发现,(i)对维修服务供应商来说,当备件的单位成本较低时,共享库存确实能够使服务供应商节省维护成本；而当备件的单位成本较高时,共享库存将会伤害供应商的利润,此时为各顾客设立专用备件库存是供应商的最优选择。(ii)当同一个市场中的多名顾客存从同一个售后服务供应商处获取设备维护服务,且供应商为顾客设立共享的备件库存时,由于顾客可以通过成本分摊和缺货惩罚两种途径激励供应商提供所需要的设备可靠性,所以规模较大的顾客可以通过设置与规模小的顾客相同的成本分摊来避免其搭便车行为,而从增加缺货惩罚的角度来迫使供应商提供必要的设备可靠性。因此,所有的顾客,无论其所持有的设备数量多少、盈利能力高低,他们都将为供应商提供相同数额的成本补贴。
Along with the development of Internet Technology and Electronic Commerce, logistics and supply chain becomes one of the most popular research topics since the end of last century. Wherein, warehousing systems, as important nodes in supply chains, receive great attentions from the scholars. In practice, the rapid development of B2C and C2C e-commerce brings the warehouses new challenges in order response.
Under the business environment described above, this thesis studies the most important issues related with order response efficiency of warehousing systems:the storage policies of the system and the availability of the storage/retrieval (S/R) machine. According to these two research issues, this thesis can be partitioned into two parts.
The first part of this thesis focuses on storage policies of the warehousing systems, analyzes how to improve the operational efficiency based on different storage policies. The discussions of this part lie in Chapters2and3. In Chapter2, by considering a finite number of items stored in the system, we relax the following basic assumption in the literature:required storage space of all the items equals their average inventory level, which is valid only if an infinite number of items are stored in each storage region. We first give the basic one way travel-time model based on a square-in-time (SIT) continuous storage rack. Then we relax the assumptions by considering non-SIT storage racks, a different type of ABC demand curve, different number of stored items, discrete storage racks and stochastic item demands, to validate the robustness of the basic model. After that, in Chapter3, we study the performance of different storage policies (including random storage, class-based storage and turnover-based storage) in a unit-load traditional warehouse with a finite number of items by considering the effect of realistic required storage space.
The response efficiency depends on not only the storage policies, but also the availability of the S/R machine. Consequently, in the second part of this thesis, we focus on how to maximize the expected profit of storage enterprises through optimal maintenance strategies. In Chapter4, we focus on the optimization problem of the storage enterprise to determine the optimal spare inventory and cost reduction effort when it does the equipment maintenance itself. In the discussion, different from the literature, we focus on the maximal profit of the enterprise considering the operational requirement (i.e., profitability) of the S/R machine instead of a given availability requirement. Additionally, we extend the model to an alliance model by considering multiple enterprises in the same market. Based on the analysis of Chapter4, Chapter5studies the maintenance strategy in a decentralized scenario when the support service is provided by the maintenance suppliers. A Stackelberg game model is adopted to describe the relationship between the storage enterprises and the maintenance suppliers based on performance-based contract.
Based on the analysis provided above, the main innovations and contributions of this thesis are summarized as follows:
1) Through the analysis in Chapter2, we find that commonly a small number (3to5) of classes is optimal for the warehousing system. This is in consistent with that a small number of classes is very close to warehousing practice where often only three storage classes are used. In addition, we find that the shortest travel time is fairly insensitive to the number of storage classes in a wide range around the optimum. This suggests a manager can select a near optimal number of storage classes in an easy way and he or she should not be worried about the impact of storage-class reconfigurations.
2) By considering the realistic required storage space of stored items in a warehousing system, we exam the effect of item demand structure (ABC demand curve) on storage policy performance in Chapter3. Different from the findings in the literature, we find that under the same storage policy, items with different ABC demand curves lead the system different average travel time/distance.
3) Based on the theoretical findings presented in point1) and2), we give the optimal item classification and warehouse shape factor for AS/RSs and aisle-based traditional warehouses.
4) A new maintenance strategy based on the profitability of the equipment is proposed in Chapter4. Through the discussion, we find that the optimal availability requirement of the equipment is not constant, but increasing with its profitability. Seeking high availability regardless with the profitability of the equipment will hurt the expect profit of the enterprise.
5) When the maintenance service is provided by the suppliers, we model the after-sales service supply chain through game models considering the profitability of the equipment in Chapter5. The findings tell that (i) the shared inventory strategy is not always better than dedicate spare inventory for each enterprise from the suppliers' perspective. When the unit cost of the spare part is relatively high, it is better for the supplier to set dedicate spare inventories.(ii) At the game equilibrium between the storage enterprises and the suppliers, all the enterprises will provide the same cost reimbursement to the suppliers even they hold different number of equipment with different profitability. This is a result of that with performance-based contract, each enterprise has two terms to influence the decisions of the supplier, especially the decision on spare inventory which influence the system availability directly. One is the cost reimbursement which provides a pull effect for the suppliers, and the other one is backorder punishment which provides a push effect. In the Nash game among the enterprises, it is a dominant strategy for the enterprises with large number of equipment or high profitability to select the same cost reimbursement as other ones and provide a high backorder punishment to push the spare inventory to the desired level.
引文
Adams, N. D., Firth, R., Brown, T. W., & Misenheimer, L. P. (1996). Warehouse & Distribution Automation Handbook[M]. New York:McGraw-Hill.
Ahmadi-Khatir, A., Fotuhi-Firuzabad, M., & Goel, L. (2009). Customer choice of reliability in spinning reserve procurement and cost allocation using well-being analysis[J]. Electric Power Systems Research,79(10),1431-1440.
Alfredsson, P. (1997). Optimization of multi-echelon repairable item inventory systems with simultaneous location of repair facilities[J]. European Journal of Operational Research,99(3), 584-595.
Allen, S. G., & D'Esopo, D. A. (1968). An ordering policy for repairable stock items[J]. Operations Research,16(3),669-67'4.
Ang, M., Lim, Y. F., & Sim, M. (2012). Robust storage assignment in unit-load warehouses[J]. Management Science,58(11),2114-2130.
Ashayeri, J., Heuts, R., Valkenburg, M., Veraart, H., & Wilhelm, M. (2002). A geometrical approach to computing expected cycle times for zonebased storage layouts in AS/RS[J]. International Journal of Production Research,40(17),4467-4483.
Bakshi, N., Kim, S.-H., & Sawa, N. (2013). Signaling and contract choice in after-sales service[J]. Working Paper, Yale University.
Bartholdi, J. J., & Hackman, S. T. (2011). Warehouse & distribution science[M]. Available on line at:http://www.isye.gatech.edu/～jjb/wh/book/editions/wh-sci-0.95.pdf [accessed July 2013].
Basten, R. J. I., van der Heijden, M. C., & Schutten, J. M. J. (2012). Joint optimization of level of repair analysis and spare parts stocks[J]. European Journal of Operational Research,222(3), 474-483.
Bellman, R. (1956). Dynamic programming and Lagrange multipliers[J]. Proceedings of the National Academy of Sciences of the United States of America,42(10),767.
Bender, P. (1981). Mathematical.modeling of the 20/80 rule:theory and practice[J]. Journal of Business Logistics,2(2),139-157.
Berkowitz, D., Gupta, J. N. D., Simpson, J. T., & McWilliams, J. B. (2005). Defining and implementing performance based logistics in government[J]. Defense Acquisition Review Journal,11(3),255-267.
Bozer, Y. A., Schorn, E. C., & Sharp, G. P. (1990). Geometric approaches to solve the Chebyshev traveling salesman problem[J]. IIE Transactions,22(3),238-254.
Brammer, K., & Malmborg, C. (1987). A transient state model for predicting maintenance requirements[J]. Engineering Costs and Production Economics,11(2),87-98.
Butler, R. W., & Finelli, G. B. (1993). The infeasibility of quantifying the reliability of life-critical real-time software[J]. IEEE Transactions on Software Engineering,19(1),3-12.
Cai, K.-Y., Wen, C.-Y., & Zhang, M.-L. (1991). A critical review on software reliability modeling[J]. Reliability Engineering & System Safety,32(3),357-371.
Chen, F. (2005). Salesforce incentives, market information, and production/inventory planning[J]. Management Science,51(1),60-75.
Chung, J. Y., & Whang, T. (2011). The impact of low cost carriers on Korean Island tourism[J]. Journal of Transport Geography,19(6),1335-1340.
Cohen, M. A., Agrawal, N., & Agrawal, V. (2006a). Achieving breakthrough service delivery through dynamic asset deployment strategies[J]. Interfaces,36(3),259-271.
Cohen, M. A., Agrawal, N., & Agrawal, V. (2006b). Winning in the aftermarket[J]. Harvard Business Review,84(5),129-138.
Cohen, M. A., Kleindorfer, P. R., & Lee, H. L. (1986). Optimal stocking policies for low usage items in multi-echelon inventory systems[J]. Naval Research Logistics Quarterly,33(1), 17-38.
Cohen, M. A., Kleindorfer, P. R., Lee, H. L., & Pyke, D. F. (1992). Multi-item service constrained (s, S) policies for spare parts logistics systems[J]. Naval Research Logistics (NRL),39(4),561-577.
Dai, Y.-S., Xie, M., & Poh, K.-L. (2008). Availability modeling and cost optimization for the grid resource management system[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A:Systems and Humans,38(1),170-179.
De Koster, M. B. M. (2010). Warehouse math[M]. In L. Kroon, T. Li & R. Zuidwijk (Eds.), Liber Amicorum in Memory of Jo van Nunen (pp.179-186). Breda:Dinalog.
De Koster, R., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking:A literature review[J]. European Journal of Operational Research,182(2),481-501.
De Koster, R. B. M., Le-Duc, T., & Zaerpour, N. (2012). Determining the number of zones in a pick-and-sort order picking system[J]. International Journal of Production Research,50(3), 757-771.
Dennis, M. J., & Kambil, A. (2003). Service management:Building profits after the sale[J]. Supply Chain Management Review,7(1),42-48.
Dhakar, T. S., Schmidt, C. P., & Miller, D. M. (1994). Base stock level determination for high cost low demand critical repairable spares[J]. Computers & Operations Research,21(4),411-420.
Eynan, A., & Rosenblatt, M. J. (1994). Eatablishing zones in single-command class-based rectangular AS/RS[J]. IIE Transactions,26(1),38-46.
Feeney, G. J., & Sherbrooke, C. C. (1966). The (S-1, S) inventory policy under compound poisson demand[J]. Management Science,12(5),391-411.
Findlay, P., Kobbacy, K., & Goodman, D. (1989). Optimization of the daily production rates for an offshore oilfield[J]. Journal of the Operational Research Society,1079-1088.
Florian, M., Lenstra, J. K., & Rinnooy Kan, A. (1980). Deterministic production planning: Algorithms and complexity [J]. Management Science,26(7),669-679.
Fraja, G., & Delbono, F. (1990). Game theoretic models of mixed oligopoly[J]. Journal of Economic Surveys,4(1),1-17.
Gaiardelli, P., Saccani, N., & Songini, L. (2007). Performance measurement of the after-sales service network—Evidence from the automotive industry[J]. Computers in Industry,58(7), 698-708.
Gajpal, P. P., Ganesh, L., & Rajendran, C. (1994). Criticality analysis of spare parts using the analytic hierarchy process[J]. International Journal of Production Economics,35(1),293-297.
Goetschalckx, M., & Ratliff, H. D. (1990). Shared storage policies based on the duration stay of unit loads[J]. Management Science,36(9),1120-1132.
Graves, S. C., Hausman, W. H., & Schwarz, L. B. (1977). Storage-retrieval interleaving in automatic warehousing systems[J]. Management Science,23(9),935-945.
Gross, D. (1981). SASPRO Ⅱ—A spare and server provisioning model[J]. Computers & Operations Research,8(3),197-207.
Gross, D., & Ince, J. F. (1978). Spares provisioning for repairable items:Cyclic queues in light traffic[J]. AIIE transactions,10(3),307-314.
Gross, D., & Pinkus, C. E. (1979). Designing a support system for repairable items[J]. Computers & Operations Research,6(2),59-68.
Gruneberg, S., Hughes, W., & Ancell, D. (2007). Risk under performance-based contracting in the UK construction sector[J]. Construction Management and Economics,25(7),691-699.
Gu, J., Goetschalckx, M., & McGinnis, L. F. (2007). Research on warehouse operation:A comprehensive review[J]. European Journal of Operational Research,177(1),1-21.
Guajardo, J. A., Cohen, M. A., Kim, S.-H., & Netessine, S. (2012). Impact of performance-based contracting on product reliability:An empirical analysis[J]. Management Science,58(5), 961-979.
Gue, K. R., & Meller, R. D. (2009). Aisle configurations for unit-load warehouses[J]. IIE Transactions,41(3),171-182.
Gupta, A., & Albright, S. C. (1992). Steady-state approximations for a multi-echelon multi-indentured repairable-item inventory system[J]. European Journal of Operational Research,62(3),340-353.
Haneveld, W. K., & Teunter, R. (1997). Optimal provisioning strategies for slow moving spare parts with small lead times[J]. Journal of the Operational Research Society,184-194.
Hastings, N. (1968). Some notes on dynamic programming and replacement[J]. Journal of the Operational Research Society,19(4),453-464.
Hausman, W. H., Schwarz, L. B., & Graves, S. C. (1976). Optimal storage assignment in automatic warehousing systems[J]. Management Science,22(6),629-638.
Heragu, S. S. (2006). Facilities Design[M]:Lincoln, NE:iUniverse.
Heskett, J. L. (1963). Cube-per-order index-a key to warehouse stock location[J]. Transportation and distribution Management,3(4),27-31.
Heskett, J. L. (1964). Putting the cube-per-order index to work in warehouse layout[J]. Transportation and distribution Management,4(8),23-30.
Hodgson, T. J. (1982). A combined approach to the pallet loading problem[J]. IIE Transactions, 14(3),175-182.
Jarvis, J. M., & McDowell, E. D. (1991). Optimal product layout in an order picking warehouse[J]. HE Transactions,23(1),93-102.
Jin, B., & Sternquist, B. (2003). The influence of retail environment on price perceptions:An exploratory study of US and Korean students[J]. International Marketing Review,20(6), 643-660.
Johnson, M. E., & Brandeau, M. L. (1996). Stochastic modeling for automated material handling system design and control[J]. Transportation Science,30(4),330-350.
Kang, W. H., Song, J. H., & Gardoni, P. L. (2008). Matrix-based system reliability method and applications to bridge networks[J]. Reliability Engineering & System Safety,93(11), 1584-1593.
Kapalka, B. A., Katircioglu, K., & Puterman, M. L. (1999). Retail inventory control with lost sales, service constraints, and fractional lead times[J]. Production and Operations Management, 8(4),393-408.
Kennedy, W., Wayne Patterson, J., & Fredendall, L. D. (2002). An overview of recent literature on spare parts inventories[J]. International Journal of Production Economics,76(2),201-215.
Kim, S. H. (2011). Strategic reliability investments in multi-indenture supply chains[J]. Working Paper, Yale University.
Kim, S. H., Cohen, M. A., & Netessine, S. (2007). Performance contracting in after-sales service supply chains[J]. Management Science,53(12),1843-1858.
Kim, S. H., Cohen, M. A., & Netessine, S. (2011). Reliability or inventory? Analysis of product support contracts in the defense industry[J]. Working Paper, Yale University.
Kim, S. H., Cohen, M. A., Netessine, S., & Veeraraghavan, S. (2010). Contracting for infrequent restoration and recovery of mission-critical systems[J]. Management Science,56(9), 1551-1567.
Koh, S., Kim, B., & Kim, B. (2002). Travel time model for the warehousing system with a tower crane S/R machine[J]. Computers & Industrial Engineering,43(3),495-507.
Kohlas, J., & Pasquier, J. (1981). Optimization of spare parts for hierarchicall decomposable systems[J]. European Journal of Operational Research,8(3),294-300.
Kouvelis, P., & Papanicolaou, V. (1995). Expected travel-time and optimal boundary formulas for a 2-class-based automated storage retrieval system[J]. International Journal of Production Research,33(10),2889-2905.
Larson, T. N., March, H., & Kusiak, A. (1997). A heuristic approach to warehouse layout with class-based storage[J]. IIE Transactions,29(4),337-348.
Le-Duc, T., & De Koster, R. B. M. (2005). Travel distance estimation and storage zone optimization in a 2-block class-based storage strategy warehouse[J]. International Journal of Production Research,43(17),3561-3581.
Lutze, H., & Ozer,O. (2008). Promised lead-time contracts under asymmetric information[J]. Operations Research,56(4),898-915.
Mettas, A. (2000). Reliability allocation and optimization for complex systems [C].Proceedings of 2000 Annual Reliability and Maintainability Symposium,216-221.
Mirzahosseinian, H., & Piplani, R. (2011). A study of repairable parts inventory system operating under performance-based contract[J]. European Journal of Operational Research,214(2), 256-261.
Muckstadt, J. A. (2005). Analysis and algorithms for service parts supply chains:Springer New York.
Nagarajan, M., & Sosic, G. (2008). Game-theoretic analysis of cooperation among supply chain agents:Review and extensions[J]. European Journal of Operational Research,187(3), 719-745.
Nahmias, S. (1981). Managing repairable item inventory systems:A review[J]. TIMS Studies in the Management Sciences,16,253-277.
Nakagawa, T. (1981). A summary of periodic replacement with minimal repair at failure[J]. Journal of the Operations Research Society of Japan,24(3),213-228.
Ng, H. K. T., Filardo, G., & Zheng, G. (2008). Confidence interval estimating procedures for standardized incidence rates[J]. Computational Statistics & Data Analysis,52(7),3501-3516.
Ng, I. C. L., Maull, R., & Yip, N. (2009). Outcome-based contracts as a driver for systems thinking and service-dominant logic in service science:Evidence from the defence industry[J]. European Management Journal,27(6),377-387.
Ng, I. C. L., & Nudurupati, S. S. (2010). Outcome-based service contracts in the defence industry-mitigating the challenges[J]. Journal of Service Management,21(5),656-674.
Oikonomou, K. N. (1993). System reliability calculations based on incomplete information[J]. IEEE Transactions on Systems Man and Cybernetics,23(5),1235-1254.
Oliva, R., & Kallenberg, R. (2003). Managing the transition from products to services[J]. International Journal of Service Industry Management,14(2),160-172.
Oppermann, M., & Cooper, M. (1999). Outbound travel and quality of life:The effect of airline price wars[J]. Journal of Business Research,44(3),179-188.
Park, B. C. (2006). Performance of automated storage/retrieval systems with non-square-in-time racks and two-class storage[J]. International Journal of Production Research,44(6), 1107-1123.
Park, B. C., Foley, R. D., White, J. A., & Frazelle, E. H. (2003). Dual command travel times and miniload system throughput with turnover-based storage[J]. IIE Transactions,35(4), 343-355.
Petersen, C. G. (2002). Considerations in order picking zone configuration[J]. International Journal of Operations & Production Management,27(7),793-805.
Petersen, C. G., & Aase, G. (2004). A comparison of picking, storage, and routing policies in manual order picking[J]. International Journal of Production Economics,92(1),11-19.
Petersen, C. G., Aase, G., & Heiser, D. R. (2004). Improving orderpicking performance through the implementation of classbased storage[J]. International Journal of Physical Distribution & Logistics Management,34(7),535-544.
Petersen, C. G., & Schmenner, R. W. (1999). An evaluation of routing and volume-based storage policies in an order picking operation[J]. Decision Sciences,30(2),481-501.
Petrovic, D., Petrovic, R., Senborn, A., & Vujosevic, M. (1990). A microcomputer expert system for advising on stocks in spare parts inventory systems[J]. Engineering Costs and Production Economics,19(1),365-370.
Plambeck, E. L., & Taylor, T. A. (2006). Partnership in a dynamic production system with unobservable actions and noncontractible output[J]. Management Science,52(10), 1509-1527.
Pohjola, M. (1983). Nash and Stackelberg solutions in a differential game model of capitalismfJ]. Journal of Economic Dynamics and Control,6,173-186.
Pohl, L. M., Meller, R. D., & Gue, K. R. (2011). Turnover-based storage in non-traditional unit-load warehouse designs[J]. IIE Transactions,43(10),703-720.
Port of Hamburg (2013). Container port throughput in a global comparison[P/OL]. http://www.hafen-hamburg.de/en/content/container-port-throughput-global-comparison [access:2013.08.10].
Psaraftis, H. N. (1980). A dynamic programming approach for sequencing groups of identical jobs[J]. Operations Research,28(6),1347-1359.
Rao, S. S., & Adil, G. K. (2013). Optimal class boundaries, number of aisles and pick list size for low-level order picking systems[J]. IIE Transactions,45(12),1309-1321.
Rappold, J. A., & Van Roo, B. D. (2009). Designing multi-echelon service parts networks with finite repair capacity[J]. European Journal of Operational Research,199(3),781-792.
Roels, G., Karmarkar, U. S., & Carr, S. (2010). Contracting for collaborative services[J]. Management Science,56(5),849-863.
Roodbergen, K. J. (2012). Storage assignment for order picking in multiple-block warehouses[M]. In R. Manzini (Ed.), Warehousing in the Global Supply Chain (pp.139-155):Springer London.
Roodbergen, K. J., & Vis, I. F. A. (2009). A survey of literature on automated storage and retrieval systems[J]. European Journal of Operational Research,194(2),343-362.
Rosenblatt, M. J., & Eynan, A. (1989). Deriving the optimal boundaries for class-based automatic storage-retrieval systems[J]. Management Science,35(12),1519-1524.
Rustenburg, W., van Houtum, G.-J., & Zijm, W. (2001). Spare parts management at complex technology-based organizations:An agenda for research[J]. International Journal of Production Economics,71(1),177-193.
Ryan, R., Xu, H., Xing, L., & Zhou, M. (2010). Automated modeling of dynamic reliability block diagrams using colored petri nets[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A:Systems and Humans,40(2),337-351.
Sen, A. (2008). The US fashion industry:A supply chain review[J]. International Journal of Production Economics,114(2),571-593.
Shanghai International Port (Group) Co., L. (2013). Throughput of port of Shanghai[P/OL]. http://www.portshanghai.com.cn/jtwbs/webpages/server teu-html [accessed 2013.08.10].
Sherbrooke, C. C. (1992). Optimal inventory modeling of systems:multi-echelon techniques[M]. New York:John Wiley & Sons.
Silver, E. A. (1972). Inventory allocation among an assembly and its repairable subassemblies[J]. Naval Research Logistics Quarterly,19(2),261-280.
Simpson, V. P. (1978). Optimum solution structure for a repairable inventory problem[J]. Operations Research,26(2),270-281.
Su, X., & Zenios, S. A. (2006). Recipient choice can address the efficiency-equity trade-off in kidney transplantation:A mechanism design model[J]. Management Science,52(11), 1647-1660.
Teunter, R. H., Babai, M. Z., & Syntetos, A. A. (2010). ABC classification:Service levels and inventory costs[J]. Production and Operations Management,19(3),343-352.
Thonemann, U. W., & Brandeau, M. L. (1998). Optimal storage assignment policies for automated storage and retrieval systems with stochastic demands[J]. Management Science,44(1), 142-148.
Tompkins, J. A., White, J. A., Bozer, Y. A., & Tanchoco, J. M. A. (2010). Facilities Planning[M] (Fourth Edition ed.). New York:John Wiley and Sons.
Van den Berg, J. P. (1996). Class-based storage allocation in a single-command warehouse with space requirement constraints[J]. International Journal of Industrial Engineering,3,21-28.
Van Kampen, T. J., Akkerman, R., & van Donk, D. P. (2012). SKU classification:a literature review and conceptual framework[J]. International Journal of Operations & Production Management,32(7),850-876.
Veinott Jr, A. F. (1965). Optimal policy for a multi-product, dynamic, nonstationary inventory problem[J]. Management Science,12(3),206-222.
Von Neumann, J., & Morgenstern, O. (1944). Game theory and economic behavior[M]:Princeton: Princeton University Press.
Wang, W. (2011). A joint spare part and maintenance inspection optimisation model using the Delay-Time concept[J]. Reliability Engineering & System Safety,96(11),1535-1541.
Wang, W. (2012). A stochastic model for joint spare parts inventory and planned maintenance optimisation[J]. European Journal of Operational Research,216(1),127-139.
Wang, Y., & Li, L. (2012). Effects of uncertainty in both component reliability and load demand on multistate system reliability[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A:Systems and Humans,42(4),958-969.
Xing, L., & Levitin, G. (2011). Combinatorial algorithm for reliability analysis of multistate systems with propagated failures and failure isolation effect[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A:Systems and Humans,41(6),1156-1165.
Yamada, S., & Osaki, S. (1985). Cost-reliability optimal release policies for software systems[J]. IEEE Transactions on Reliability, R-34(5),422-424.
Yeralan, S., Dieck, A. J., & Darwin, R. F. (1986). Economically optimum maintenance, repair and buffering operations in manufacturing systems[J]. The Engineering Economist,31(4), 279-292.
Yu, Y., & De Koster, M. B. M. (2009). Optimal zone boundaries for two-class-based compact three-dimensional automated storage and retrieval systems[J]. ⅡE Transactions,41(3), 194-208.
Zaerpour, N., De Koster, M. B. M., & Yu, Y. (2013). Storage policies and optimal shape of a storage system[J]. International Journal of Production Research,51(23-24),6891-6899.
Zio, E. (2009). Reliability engineering:Old problems and new challenges[J]. Reliability Engineering & System Safety,94(2),125-141.
Ahmadi-Khatir, A., Fotuhi-Firuzabad, M., & Goel, L. (2009). Customer choice of reliability in spinning reserve procurement and cost allocation using well-being analysis[J]. Electric Power Systems Research,79(10),1431-1440.
Alfredsson, P. (1997). Optimization of multi-echelon repairable item inventory systems with simultaneous location of repair facilities[J]. European Journal of Operational Research,99(3), 584-595.
Allen, S. G., & D'Esopo, D. A. (1968). An ordering policy for repairable stock items[J]. Operations Research,16(3),669-67'4.
Ang, M., Lim, Y. F., & Sim, M. (2012). Robust storage assignment in unit-load warehouses[J]. Management Science,58(11),2114-2130.
Ashayeri, J., Heuts, R., Valkenburg, M., Veraart, H., & Wilhelm, M. (2002). A geometrical approach to computing expected cycle times for zonebased storage layouts in AS/RS[J]. International Journal of Production Research,40(17),4467-4483.
Bakshi, N., Kim, S.-H., & Sawa, N. (2013). Signaling and contract choice in after-sales service[J]. Working Paper, Yale University.
Bartholdi, J. J., & Hackman, S. T. (2011). Warehouse & distribution science[M]. Available on line at:http://www.isye.gatech.edu/～jjb/wh/book/editions/wh-sci-0.95.pdf [accessed July 2013].
Basten, R. J. I., van der Heijden, M. C., & Schutten, J. M. J. (2012). Joint optimization of level of repair analysis and spare parts stocks[J]. European Journal of Operational Research,222(3), 474-483.
Bellman, R. (1956). Dynamic programming and Lagrange multipliers[J]. Proceedings of the National Academy of Sciences of the United States of America,42(10),767.
Bender, P. (1981). Mathematical.modeling of the 20/80 rule:theory and practice[J]. Journal of Business Logistics,2(2),139-157.
Berkowitz, D., Gupta, J. N. D., Simpson, J. T., & McWilliams, J. B. (2005). Defining and implementing performance based logistics in government[J]. Defense Acquisition Review Journal,11(3),255-267.
Bozer, Y. A., Schorn, E. C., & Sharp, G. P. (1990). Geometric approaches to solve the Chebyshev traveling salesman problem[J]. IIE Transactions,22(3),238-254.
Brammer, K., & Malmborg, C. (1987). A transient state model for predicting maintenance requirements[J]. Engineering Costs and Production Economics,11(2),87-98.
Butler, R. W., & Finelli, G. B. (1993). The infeasibility of quantifying the reliability of life-critical real-time software[J]. IEEE Transactions on Software Engineering,19(1),3-12.
Cai, K.-Y., Wen, C.-Y., & Zhang, M.-L. (1991). A critical review on software reliability modeling[J]. Reliability Engineering & System Safety,32(3),357-371.
Chen, F. (2005). Salesforce incentives, market information, and production/inventory planning[J]. Management Science,51(1),60-75.
Chung, J. Y., & Whang, T. (2011). The impact of low cost carriers on Korean Island tourism[J]. Journal of Transport Geography,19(6),1335-1340.
Cohen, M. A., Agrawal, N., & Agrawal, V. (2006a). Achieving breakthrough service delivery through dynamic asset deployment strategies[J]. Interfaces,36(3),259-271.
Cohen, M. A., Agrawal, N., & Agrawal, V. (2006b). Winning in the aftermarket[J]. Harvard Business Review,84(5),129-138.
Cohen, M. A., Kleindorfer, P. R., & Lee, H. L. (1986). Optimal stocking policies for low usage items in multi-echelon inventory systems[J]. Naval Research Logistics Quarterly,33(1), 17-38.
Cohen, M. A., Kleindorfer, P. R., Lee, H. L., & Pyke, D. F. (1992). Multi-item service constrained (s, S) policies for spare parts logistics systems[J]. Naval Research Logistics (NRL),39(4),561-577.
Dai, Y.-S., Xie, M., & Poh, K.-L. (2008). Availability modeling and cost optimization for the grid resource management system[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A:Systems and Humans,38(1),170-179.
De Koster, M. B. M. (2010). Warehouse math[M]. In L. Kroon, T. Li & R. Zuidwijk (Eds.), Liber Amicorum in Memory of Jo van Nunen (pp.179-186). Breda:Dinalog.
De Koster, R., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking:A literature review[J]. European Journal of Operational Research,182(2),481-501.
De Koster, R. B. M., Le-Duc, T., & Zaerpour, N. (2012). Determining the number of zones in a pick-and-sort order picking system[J]. International Journal of Production Research,50(3), 757-771.
Dennis, M. J., & Kambil, A. (2003). Service management:Building profits after the sale[J]. Supply Chain Management Review,7(1),42-48.
Dhakar, T. S., Schmidt, C. P., & Miller, D. M. (1994). Base stock level determination for high cost low demand critical repairable spares[J]. Computers & Operations Research,21(4),411-420.
Eynan, A., & Rosenblatt, M. J. (1994). Eatablishing zones in single-command class-based rectangular AS/RS[J]. IIE Transactions,26(1),38-46.
Feeney, G. J., & Sherbrooke, C. C. (1966). The (S-1, S) inventory policy under compound poisson demand[J]. Management Science,12(5),391-411.
Findlay, P., Kobbacy, K., & Goodman, D. (1989). Optimization of the daily production rates for an offshore oilfield[J]. Journal of the Operational Research Society,1079-1088.
Florian, M., Lenstra, J. K., & Rinnooy Kan, A. (1980). Deterministic production planning: Algorithms and complexity [J]. Management Science,26(7),669-679.
Fraja, G., & Delbono, F. (1990). Game theoretic models of mixed oligopoly[J]. Journal of Economic Surveys,4(1),1-17.
Gaiardelli, P., Saccani, N., & Songini, L. (2007). Performance measurement of the after-sales service network—Evidence from the automotive industry[J]. Computers in Industry,58(7), 698-708.
Gajpal, P. P., Ganesh, L., & Rajendran, C. (1994). Criticality analysis of spare parts using the analytic hierarchy process[J]. International Journal of Production Economics,35(1),293-297.
Goetschalckx, M., & Ratliff, H. D. (1990). Shared storage policies based on the duration stay of unit loads[J]. Management Science,36(9),1120-1132.
Graves, S. C., Hausman, W. H., & Schwarz, L. B. (1977). Storage-retrieval interleaving in automatic warehousing systems[J]. Management Science,23(9),935-945.
Gross, D. (1981). SASPRO Ⅱ—A spare and server provisioning model[J]. Computers & Operations Research,8(3),197-207.
Gross, D., & Ince, J. F. (1978). Spares provisioning for repairable items:Cyclic queues in light traffic[J]. AIIE transactions,10(3),307-314.
Gross, D., & Pinkus, C. E. (1979). Designing a support system for repairable items[J]. Computers & Operations Research,6(2),59-68.
Gruneberg, S., Hughes, W., & Ancell, D. (2007). Risk under performance-based contracting in the UK construction sector[J]. Construction Management and Economics,25(7),691-699.
Gu, J., Goetschalckx, M., & McGinnis, L. F. (2007). Research on warehouse operation:A comprehensive review[J]. European Journal of Operational Research,177(1),1-21.
Guajardo, J. A., Cohen, M. A., Kim, S.-H., & Netessine, S. (2012). Impact of performance-based contracting on product reliability:An empirical analysis[J]. Management Science,58(5), 961-979.
Gue, K. R., & Meller, R. D. (2009). Aisle configurations for unit-load warehouses[J]. IIE Transactions,41(3),171-182.
Gupta, A., & Albright, S. C. (1992). Steady-state approximations for a multi-echelon multi-indentured repairable-item inventory system[J]. European Journal of Operational Research,62(3),340-353.
Haneveld, W. K., & Teunter, R. (1997). Optimal provisioning strategies for slow moving spare parts with small lead times[J]. Journal of the Operational Research Society,184-194.
Hastings, N. (1968). Some notes on dynamic programming and replacement[J]. Journal of the Operational Research Society,19(4),453-464.
Hausman, W. H., Schwarz, L. B., & Graves, S. C. (1976). Optimal storage assignment in automatic warehousing systems[J]. Management Science,22(6),629-638.
Heragu, S. S. (2006). Facilities Design[M]:Lincoln, NE:iUniverse.
Heskett, J. L. (1963). Cube-per-order index-a key to warehouse stock location[J]. Transportation and distribution Management,3(4),27-31.
Heskett, J. L. (1964). Putting the cube-per-order index to work in warehouse layout[J]. Transportation and distribution Management,4(8),23-30.
Hodgson, T. J. (1982). A combined approach to the pallet loading problem[J]. IIE Transactions, 14(3),175-182.
Jarvis, J. M., & McDowell, E. D. (1991). Optimal product layout in an order picking warehouse[J]. HE Transactions,23(1),93-102.
Jin, B., & Sternquist, B. (2003). The influence of retail environment on price perceptions:An exploratory study of US and Korean students[J]. International Marketing Review,20(6), 643-660.
Johnson, M. E., & Brandeau, M. L. (1996). Stochastic modeling for automated material handling system design and control[J]. Transportation Science,30(4),330-350.
Kang, W. H., Song, J. H., & Gardoni, P. L. (2008). Matrix-based system reliability method and applications to bridge networks[J]. Reliability Engineering & System Safety,93(11), 1584-1593.
Kapalka, B. A., Katircioglu, K., & Puterman, M. L. (1999). Retail inventory control with lost sales, service constraints, and fractional lead times[J]. Production and Operations Management, 8(4),393-408.
Kennedy, W., Wayne Patterson, J., & Fredendall, L. D. (2002). An overview of recent literature on spare parts inventories[J]. International Journal of Production Economics,76(2),201-215.
Kim, S. H. (2011). Strategic reliability investments in multi-indenture supply chains[J]. Working Paper, Yale University.
Kim, S. H., Cohen, M. A., & Netessine, S. (2007). Performance contracting in after-sales service supply chains[J]. Management Science,53(12),1843-1858.
Kim, S. H., Cohen, M. A., & Netessine, S. (2011). Reliability or inventory? Analysis of product support contracts in the defense industry[J]. Working Paper, Yale University.
Kim, S. H., Cohen, M. A., Netessine, S., & Veeraraghavan, S. (2010). Contracting for infrequent restoration and recovery of mission-critical systems[J]. Management Science,56(9), 1551-1567.
Koh, S., Kim, B., & Kim, B. (2002). Travel time model for the warehousing system with a tower crane S/R machine[J]. Computers & Industrial Engineering,43(3),495-507.
Kohlas, J., & Pasquier, J. (1981). Optimization of spare parts for hierarchicall decomposable systems[J]. European Journal of Operational Research,8(3),294-300.
Kouvelis, P., & Papanicolaou, V. (1995). Expected travel-time and optimal boundary formulas for a 2-class-based automated storage retrieval system[J]. International Journal of Production Research,33(10),2889-2905.
Larson, T. N., March, H., & Kusiak, A. (1997). A heuristic approach to warehouse layout with class-based storage[J]. IIE Transactions,29(4),337-348.
Le-Duc, T., & De Koster, R. B. M. (2005). Travel distance estimation and storage zone optimization in a 2-block class-based storage strategy warehouse[J]. International Journal of Production Research,43(17),3561-3581.
Lutze, H., & Ozer,O. (2008). Promised lead-time contracts under asymmetric information[J]. Operations Research,56(4),898-915.
Mettas, A. (2000). Reliability allocation and optimization for complex systems [C].Proceedings of 2000 Annual Reliability and Maintainability Symposium,216-221.
Mirzahosseinian, H., & Piplani, R. (2011). A study of repairable parts inventory system operating under performance-based contract[J]. European Journal of Operational Research,214(2), 256-261.
Muckstadt, J. A. (2005). Analysis and algorithms for service parts supply chains:Springer New York.
Nagarajan, M., & Sosic, G. (2008). Game-theoretic analysis of cooperation among supply chain agents:Review and extensions[J]. European Journal of Operational Research,187(3), 719-745.
Nahmias, S. (1981). Managing repairable item inventory systems:A review[J]. TIMS Studies in the Management Sciences,16,253-277.
Nakagawa, T. (1981). A summary of periodic replacement with minimal repair at failure[J]. Journal of the Operations Research Society of Japan,24(3),213-228.
Ng, H. K. T., Filardo, G., & Zheng, G. (2008). Confidence interval estimating procedures for standardized incidence rates[J]. Computational Statistics & Data Analysis,52(7),3501-3516.
Ng, I. C. L., Maull, R., & Yip, N. (2009). Outcome-based contracts as a driver for systems thinking and service-dominant logic in service science:Evidence from the defence industry[J]. European Management Journal,27(6),377-387.
Ng, I. C. L., & Nudurupati, S. S. (2010). Outcome-based service contracts in the defence industry-mitigating the challenges[J]. Journal of Service Management,21(5),656-674.
Oikonomou, K. N. (1993). System reliability calculations based on incomplete information[J]. IEEE Transactions on Systems Man and Cybernetics,23(5),1235-1254.
Oliva, R., & Kallenberg, R. (2003). Managing the transition from products to services[J]. International Journal of Service Industry Management,14(2),160-172.
Oppermann, M., & Cooper, M. (1999). Outbound travel and quality of life:The effect of airline price wars[J]. Journal of Business Research,44(3),179-188.
Park, B. C. (2006). Performance of automated storage/retrieval systems with non-square-in-time racks and two-class storage[J]. International Journal of Production Research,44(6), 1107-1123.
Park, B. C., Foley, R. D., White, J. A., & Frazelle, E. H. (2003). Dual command travel times and miniload system throughput with turnover-based storage[J]. IIE Transactions,35(4), 343-355.
Petersen, C. G. (2002). Considerations in order picking zone configuration[J]. International Journal of Operations & Production Management,27(7),793-805.
Petersen, C. G., & Aase, G. (2004). A comparison of picking, storage, and routing policies in manual order picking[J]. International Journal of Production Economics,92(1),11-19.
Petersen, C. G., Aase, G., & Heiser, D. R. (2004). Improving orderpicking performance through the implementation of classbased storage[J]. International Journal of Physical Distribution & Logistics Management,34(7),535-544.
Petersen, C. G., & Schmenner, R. W. (1999). An evaluation of routing and volume-based storage policies in an order picking operation[J]. Decision Sciences,30(2),481-501.
Petrovic, D., Petrovic, R., Senborn, A., & Vujosevic, M. (1990). A microcomputer expert system for advising on stocks in spare parts inventory systems[J]. Engineering Costs and Production Economics,19(1),365-370.
Plambeck, E. L., & Taylor, T. A. (2006). Partnership in a dynamic production system with unobservable actions and noncontractible output[J]. Management Science,52(10), 1509-1527.
Pohjola, M. (1983). Nash and Stackelberg solutions in a differential game model of capitalismfJ]. Journal of Economic Dynamics and Control,6,173-186.
Pohl, L. M., Meller, R. D., & Gue, K. R. (2011). Turnover-based storage in non-traditional unit-load warehouse designs[J]. IIE Transactions,43(10),703-720.
Port of Hamburg (2013). Container port throughput in a global comparison[P/OL]. http://www.hafen-hamburg.de/en/content/container-port-throughput-global-comparison [access:2013.08.10].
Psaraftis, H. N. (1980). A dynamic programming approach for sequencing groups of identical jobs[J]. Operations Research,28(6),1347-1359.
Rao, S. S., & Adil, G. K. (2013). Optimal class boundaries, number of aisles and pick list size for low-level order picking systems[J]. IIE Transactions,45(12),1309-1321.
Rappold, J. A., & Van Roo, B. D. (2009). Designing multi-echelon service parts networks with finite repair capacity[J]. European Journal of Operational Research,199(3),781-792.
Roels, G., Karmarkar, U. S., & Carr, S. (2010). Contracting for collaborative services[J]. Management Science,56(5),849-863.
Roodbergen, K. J. (2012). Storage assignment for order picking in multiple-block warehouses[M]. In R. Manzini (Ed.), Warehousing in the Global Supply Chain (pp.139-155):Springer London.
Roodbergen, K. J., & Vis, I. F. A. (2009). A survey of literature on automated storage and retrieval systems[J]. European Journal of Operational Research,194(2),343-362.
Rosenblatt, M. J., & Eynan, A. (1989). Deriving the optimal boundaries for class-based automatic storage-retrieval systems[J]. Management Science,35(12),1519-1524.
Rustenburg, W., van Houtum, G.-J., & Zijm, W. (2001). Spare parts management at complex technology-based organizations:An agenda for research[J]. International Journal of Production Economics,71(1),177-193.
Ryan, R., Xu, H., Xing, L., & Zhou, M. (2010). Automated modeling of dynamic reliability block diagrams using colored petri nets[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A:Systems and Humans,40(2),337-351.
Sen, A. (2008). The US fashion industry:A supply chain review[J]. International Journal of Production Economics,114(2),571-593.
Shanghai International Port (Group) Co., L. (2013). Throughput of port of Shanghai[P/OL]. http://www.portshanghai.com.cn/jtwbs/webpages/server teu-html [accessed 2013.08.10].
Sherbrooke, C. C. (1992). Optimal inventory modeling of systems:multi-echelon techniques[M]. New York:John Wiley & Sons.
Silver, E. A. (1972). Inventory allocation among an assembly and its repairable subassemblies[J]. Naval Research Logistics Quarterly,19(2),261-280.
Simpson, V. P. (1978). Optimum solution structure for a repairable inventory problem[J]. Operations Research,26(2),270-281.
Su, X., & Zenios, S. A. (2006). Recipient choice can address the efficiency-equity trade-off in kidney transplantation:A mechanism design model[J]. Management Science,52(11), 1647-1660.
Teunter, R. H., Babai, M. Z., & Syntetos, A. A. (2010). ABC classification:Service levels and inventory costs[J]. Production and Operations Management,19(3),343-352.
Thonemann, U. W., & Brandeau, M. L. (1998). Optimal storage assignment policies for automated storage and retrieval systems with stochastic demands[J]. Management Science,44(1), 142-148.
Tompkins, J. A., White, J. A., Bozer, Y. A., & Tanchoco, J. M. A. (2010). Facilities Planning[M] (Fourth Edition ed.). New York:John Wiley and Sons.
Van den Berg, J. P. (1996). Class-based storage allocation in a single-command warehouse with space requirement constraints[J]. International Journal of Industrial Engineering,3,21-28.
Van Kampen, T. J., Akkerman, R., & van Donk, D. P. (2012). SKU classification:a literature review and conceptual framework[J]. International Journal of Operations & Production Management,32(7),850-876.
Veinott Jr, A. F. (1965). Optimal policy for a multi-product, dynamic, nonstationary inventory problem[J]. Management Science,12(3),206-222.
Von Neumann, J., & Morgenstern, O. (1944). Game theory and economic behavior[M]:Princeton: Princeton University Press.
Wang, W. (2011). A joint spare part and maintenance inspection optimisation model using the Delay-Time concept[J]. Reliability Engineering & System Safety,96(11),1535-1541.
Wang, W. (2012). A stochastic model for joint spare parts inventory and planned maintenance optimisation[J]. European Journal of Operational Research,216(1),127-139.
Wang, Y., & Li, L. (2012). Effects of uncertainty in both component reliability and load demand on multistate system reliability[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A:Systems and Humans,42(4),958-969.
Xing, L., & Levitin, G. (2011). Combinatorial algorithm for reliability analysis of multistate systems with propagated failures and failure isolation effect[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A:Systems and Humans,41(6),1156-1165.
Yamada, S., & Osaki, S. (1985). Cost-reliability optimal release policies for software systems[J]. IEEE Transactions on Reliability, R-34(5),422-424.
Yeralan, S., Dieck, A. J., & Darwin, R. F. (1986). Economically optimum maintenance, repair and buffering operations in manufacturing systems[J]. The Engineering Economist,31(4), 279-292.
Yu, Y., & De Koster, M. B. M. (2009). Optimal zone boundaries for two-class-based compact three-dimensional automated storage and retrieval systems[J]. ⅡE Transactions,41(3), 194-208.
Zaerpour, N., De Koster, M. B. M., & Yu, Y. (2013). Storage policies and optimal shape of a storage system[J]. International Journal of Production Research,51(23-24),6891-6899.
Zio, E. (2009). Reliability engineering:Old problems and new challenges[J]. Reliability Engineering & System Safety,94(2),125-141.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |