Elreedy Dina, Atiya Amir F, Shaheen Samir I
Computer Engineering Department, Cairo University, Giza, 12613 Egypt.
Soft comput. 2021;25(17):11711-11733. doi: 10.1007/s00500-021-06047-y. Epub 2021 Jul 25.
The price demand relation is a fundamental concept that models how price affects the sale of a product. It is critical to have an accurate estimate of its parameters, as it will impact the company's revenue. The learning has to be performed very efficiently using a small window of a few test points, because of the rapid changes in price demand parameters due to seasonality and fluctuations. However, there are conflicting goals when seeking the two objectives of revenue maximization and demand learning, known as the learn/earn trade-off. This is akin to the exploration/exploitation trade-off that we encounter in machine learning and optimization algorithms. In this paper, we consider the problem of price demand function estimation, taking into account its exploration-exploitation characteristic. We design a new objective function that combines both aspects. This objective function is essentially the revenue minus a term that measures the error in parameter estimates. Recursive algorithms that optimize this objective function are derived. The proposed method outperforms other existing approaches.
价格需求关系是一个基本概念，它对价格如何影响产品销售进行建模。准确估计其参数至关重要，因为这将影响公司的收入。由于季节性和波动导致价格需求参数快速变化，必须使用几个测试点的小窗口非常高效地进行学习。然而，在追求收入最大化和需求学习这两个目标时存在相互冲突的目标，即所谓的学习/收益权衡。这类似于我们在机器学习和优化算法中遇到的探索/利用权衡。在本文中，我们考虑价格需求函数估计问题，同时考虑其探索-利用特性。我们设计了一个结合这两个方面的新目标函数。这个目标函数本质上是收入减去一个衡量参数估计误差的项。推导了优化此目标函数的递归算法。所提出的方法优于其他现有方法。
