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Disentangling capacity control from price optimization

机译:解解价格优化的能力控制

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Volume per (1. leg 2. booking class 3. 16 DCPs)}? 0,5 bkgs Volume per (1. Itinerary/PoS 2. booking class 3. 23 DCPs)}? 0,01 bkgs Volume & price elasticity per (1. Itinerary/PoS 2. Booking class 3. 100 DCPs)}? 0,002 bkgs Volume & price elasticity per 1. Round-trip itinerary/PoS 2. customer segment 3. fare 4. ... With increasing level of detail 1. ... forecasts become harder to manage and analyze due to data volume 2. ... price elasticity parameters, and therefore marginal revenue forecasts, become unstable. Booking class based RM forecast models 1. ... are only valid for a given and fixed fare structure 2. ... depend on data that is not available in sufficient quality 2.1 multiple fares per booking class 2.2 inaccurate availabilities 2.3 little or no data for insignificant O&Ds.
机译:每(1.腿2.预订3. 16 dcps)}? 0.5 BKGS卷(1.行程/ POS 2.预订3. 23 DCPS)}? 0,01 bkgs卷和价格弹性每(1.行程/ POS 2.预订3. 100 DCPS)}? 0,002 BKGS音量和价格弹性每1.往返行程/ POS 2.客户段3.票价4. ......随着细节水平的增加1. ......由于数据第2卷,预测变得越来越难以管理和分析。 ...价格弹性参数,因此边际收入预测,变得不稳定。预订基于课程的RM预测模型1.仅适用于给定和固定票价结构2. ......依靠足够质量的数据不提供的数据2.1每台预订类别2.2不准确的可用性2.3很少或根本没有数据用于无足轻重的O&DS。

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