随机考虑集选择模型的收入界限

IF 0.8 4区管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Letters Pub Date : 2024-01-18 DOI:10.1016/j.orl.2024.107070

Wentao Lu

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引用次数: 0

摘要

随机考虑集选择模型是最近提出的一种选择模型，可以捕捉消费者的随机选择行为。随机考虑集选择模型的一个优点是，它可以适应一些多项式 Logit 模型无法解释的现象。在本文中，我们证明了当随机考虑集模型中产品的关注概率和偏好顺序发生变化时的收益边界。当消费者对产品的偏好发生变化时，所提出的边界可用于控制收益差异，并可用于非平稳环境下的在线分类优化等设置。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Bounds on revenue for the random consideration set choice model

The random consideration set choice model is a recently proposed choice model that can capture the stochastic choice behavior of consumers. One advantage of the random consideration set choice model is that it can accommodate some phenomena that cannot be explained by the multinomial logit model. In this paper, we prove revenue bounds when the attention probabilities and preference order over products change for the random consideration set model. The bounds proposed can be used to control revenue differences when consumers' preference over product changes and can be useful for settings like online assortment optimization with a non-stationary environment.

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来源期刊

Operations Research Letters 管理科学-运筹学与管理科学

CiteScore

2.10

自引率

9.10%

发文量

111

审稿时长

83 days

期刊介绍： Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.