MNL选择下动态品类优化的统一算法框架
A Unified Algorithmic Framework for Dynamic Assortment Optimization under MNL Choice
AI总结：
针对无补货且存在缺货替代的两类动态品类与库存规划问题，提出基于MNL选择模型的统一算法框架，在近似保证、运行时间及不确定性与复杂约束处理上优于现有方法。
摘要：
我们研究在不进行补货的情况下，具有基于缺货的动态替代效应的品类与库存规划问题，并考虑两种不同情形：（1）顾客到达时能够看到所有可获得的产品，这是实体店中的典型场景；（2）卖方可选择向每位顾客提供可获得产品的一个子集，这在在线平台中更为常见。已知这两种情形在计算上都具有挑战性，而目前针对这两种情形的近似算法差异很大。我们在MNL选择模型下为这两种情形开发了一个统一的算法框架。我们的算法在近似保证和运行时间，以及管理顾客总数不确定性和处理更复杂约束的能力方面，改进了最先进的算法。在此过程中，我们建立了动态品类规划（针对MNL选择模型）的多种新颖性质，这些性质可能具有更广泛的用途。
英文摘要：
We consider assortment and inventory planning problems with dynamic stockout-based substitution effects, and without replenishment, in two different settings: (1) Customers can see all available products when they arrive, a typical scenario in physical stores. (2) The seller can choose to offer a subset of available products to each customer, which is more common on online platforms. Both settings are known to be computationally challenging, and the current approximation algorithms for the two settings are quite different. We develop a unified algorithm framework under the MNL choice model for both settings. Our algorithms improve on the state-of-the-art algorithms in terms of approximation guarantee and runtime, and the ability to manage uncertainty in the total number of customers and handle more complex constraints. In the process, we establish various novel properties of dynamic assortment planning (for the MNL choice model) that may be useful more broadly.
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