The prosperity of the daily deal business has attracted more sellers to participate in daily deal campaigns with offering discounted deals via online platforms like Groupon and Juhuasuan. This gives rise to a new challenge for online platforms on how to efficiently organize a limited number of sellers to conduct daily deal campaigns. Our paper makes the first attempt to understand how different seller organization formats can influence the firms’ equilibrium strategies and profits in the daily deal market. We focus on two prevalent seller organization formats. (1) The seller agglomeration strategy: the platform (e.g., Groupon) does not distinguish the sellers’ type in each round of the campaign. (2) The seller segmentation strategy: the platform (e.g., Juhuasuan) organizes sellers of the same type in each round. Comparing to the agglomeration strategy, we show that the segmentation strategy can eliminate internal information asymmetry among competing sellers and thus can improve the sellers’ pricing efficiency and facilitate the platform to charge a higher percentage fee. This uncovers the value of seller segmentation and theoretically explains why platforms should carefully segmentate sellers in daily deal campaigns, although considerable efforts are required to enroll sellers.
Chain type coal-fired hot blast furnace boiler has a strong coupling, large delay, large inertia characteristics. Control effect of control method of mathematic modeling method and the classical routine of it is very difficult to produce the ideal. The predictive control theory combined with neural network theory. Through the model correction and rolling optimization control method of the system is good to overcome the effects of model error and time-varying process. The experimental results showed that neural network predictive control system is improved effectively the static precision and dynamic characteristic. It has better practicability of boiler temperature of this kind of large time delay system.
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