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Spatial pricing means a retailer price discriminates its customers based on their geographic locations. In this article, we study how an online retailer should jointly allocate multiple products and facilitate spatial price discrimination to maximize profits. When deciding between a centralized product allocation ((i.e., different products are allocated to the same fulfillment center) and decentralized product allocation (ie, different products are allocated to different fulfillment centers), the retailer faces the tradeoff between shipment pooling (ie, shipping multiple products in one package), and demand localization (ie, stocking products to satisfy local demand) based on its understanding of customers' product valuations. In our basic model, we consider two widely used spatial pricing policies: free on board (FOB) pricing that charges each customer the exact amount of shipping cost, and uniform delivered (UD) pricing that provides free shipping. We propose a stylized model and find that centralized product allocation is preferred when demand localization effect is relatively low or shipment pooling benefit is relatively high under both spatial pricing policies. Moreover, centralized product allocation is more preferred under the FOB pricing which encourages the purchase of virtual bundles of multiple products. Furthermore, we respectively extend the UD and FOB pricing policies to flat rate shipping (ie, the firm charges a constant shipping fee for each purchase), and linear rate shipping (ie, the firm sets the shipping fee as a fixed proportion of firm's actual fulfillment costs). While similar observations from the basic model still hold, we find the firm can improve its profit by sharing the fulfillment cost with its customers via the flat rate or linear rate shipping fee structure. 相似文献
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无衍射艾里光束的横向自加速特性使得光束在自由空间沿弯曲路径进行传输成为可能。利用一维傍轴波动方程对新型无衍射艾里光束的横向自加速特性进行了仿真研究。研究表明,当入射波长一定时,艾里光束的横向加速度大小与传播距离呈正比关系,而与任意横向尺度的大小呈反比关系。同时,利用波印廷矢量对艾里光束横向自加速的内在机理进行了研究。最后,利用通用型液晶空间光调制器对二维艾里光束的产生和横向自加速特性进行了实验研究,所得结果与仿真计算结果取得了较好的一致性。 相似文献
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We present, analyze, and compare three random search methods for solving stochastic optimization problems with uncountable feasible regions. Our adaptive search with resampling (ASR) approach is a framework for designing provably convergent algorithms that are adaptive and may consequently involve local search. The deterministic and stochastic shrinking ball (DSB and SSB) approaches are also convergent, but they are based on pure random search with the only difference being the estimator of the optimal solution [the DSB method was originally proposed and analyzed by Baumert and Smith]. The three methods use different techniques to reduce the effects of noise in the estimated objective function values. Our ASR method achieves this goal through resampling of already sampled points, whereas the DSB and SSB approaches address it by averaging observations in balls that shrink with time. We present conditions under which the three methods are convergent, both in probability and almost surely, and provide a limited computational study aimed at comparing the methods. Although further investigation is needed, our numerical results suggest that the ASR approach is promising, especially for difficult problems where the probability of identifying good solutions using pure random search is small. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010 相似文献
84.
大椭圆航法及其导航参数计算 总被引:3,自引:0,他引:3
提出了基于地球椭球模型的大椭圆航法,采用解析几何中的空间向量分析,推导出了初始航向角、航程和偏航量的计算公式.算例分析表明,该公式可以解决航海中不同参考椭球下大椭圆航法的导航参数计算问题,航程计算精度高达10-3m. 相似文献
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