基于Logit离散选择模型的品类优化问题综述

发布时间：2018-07-17 08:59

【摘要】：品类优化问题(Assortment Optimization Problem)是收益管理的经典问题.它研究零售商在满足运营约束的前提下,应如何从给定产品集合中选择一个子集提供给消费者,以最大化预期收益.该问题的核心在于如何准确地刻画消费者在面对细分产品时的选择行为、建立相应的优化模型并设计高效率的求解算法.基于Logit离散选择模型的品类优化问题:首先,介绍了基于Multinomial Logit模型的品类优化问题.然后介绍了两个更复杂的变种:第一个是基于两层以及多层Nested Logit模型的品类优化问题,这类问题可合理刻画细分产品之间的"替代效应";第二个是基于Mixtures of Multinomial Logits模型的品类优化问题,这类问题可充分考虑消费者群体的异质性.随后,介绍了数据驱动的品类优化问题的相关进展.最后,指出该问题未来可能的若干研究方向.
[Abstract]:Assortment Optimization problem is a classical problem in income management. It studies how retailers can choose a subset from a given set of products to provide to consumers in order to maximize the expected income under the premise of satisfying the operational constraints. The core of the problem lies in how to accurately depict the consumer's choice behavior in the face of subdivision product, establish the corresponding optimization model and design the efficient solution algorithm. Category optimization problem based on logit discrete selection model: firstly, the category optimization problem based on Multinomial logit model is introduced. Then two more complex variants are introduced: the first is the category optimization problem based on the two-layer and multi-layer nested logit model. This kind of problem can reasonably describe the "substitution effect" between subdivision products, and the second is the category optimization problem based on Mixtures of Multinomial logs model, which can fully consider the heterogeneity of consumer groups. Then, the related progress of data-driven category optimization problem is introduced. Finally, some possible research directions of the problem in the future are pointed out.
【作者单位】：清华大学工业工程系;
【基金】：国家自然科学基金(No.71622006)
【分类号】：O224

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