Large-Scale Price Optimization via Network Flow
Large-Scale Price Optimization via Network Flow
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发表时间：
2016
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通讯作者：
Shinji Ito;R. Fujimaki
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作者：
Shinji Ito;R. Fujimaki
本文研究的是价格优化问题，即在需求预测模型的基础上，寻找最优的定价策略，使收入或利润最大化。虽然回归技术的最新进展已经能够揭示多个产品的价格-需求关系，但是大多数现有的价格优化方法，例如混合整数规划公式，由于其高计算成本而不能处理数十个或数百个产品。科普这一问题，提出了一种基于网络流算法的新方法.我们揭示了超模块的收入和交叉需求弹性之间的联系。在此基础上，我们提出了一个有效的算法，采用网络流算法。该算法可以处理成百上千的产品，并返回一个精确的最优解的假设下，关于交叉弹性的需求。即使在假设不成立的情况下，所提出的算法可以有效地找到近似的解决方案，以及其他国家的最先进的方法，实证结果表明。
This paper deals with price optimization, which is to find the best pricing strategy that maximizes revenue or profit, on the basis of demand forecasting models. Though recent advances in regression technologies have made it possible to reveal price-demand relationship of a number of multiple products, most existing price optimization methods, such as mixed integer programming formulation, cannot handle tens or hundreds of products because of their high computational costs. To cope with this problem, this paper proposes a novel approach based on network flow algorithms. We reveal a connection between supermodularity of the revenue and cross elasticity of demand. On the basis of this connection, we propose an efficient algorithm that employs network flow algorithms. The proposed algorithm can handle hundreds or thousands of products, and returns an exact optimal solution under an assumption regarding cross elasticity of demand. Even in case in which the assumption does not hold, the proposed algorithm can efficiently find approximate solutions as good as can other state-of-the-art methods, as empirical results show.