论文标题
在基于排名的选择模型下具有不可用效果的基于排名的选择模型下的强大分类优化
Robust Assortment Optimization under a Ranking-based Choice Model with Product Unavailability Effect
论文作者
论文摘要
分类计划问题是零售行业任何公司收入管理策略的核心作品。在本文中，我们研究了基于顺序排名的选择模型下的可替代产品的强大分类优化问题。我们的建模方法结合了累积效果，即在客户的购买决策中找到多个不可用的产品。为了模拟客户探索要购买的产品的高度不确定的顺序，我们提出了一种双层优化方法，以最大程度地在客户偏好列表中最大程度地提高产品最糟糕的收入。我们提供了一种多项式时间算法，该算法可在我们选择模型下最佳地解决不受约束的分类问题问题的特殊情况。对于该问题的一般约束版本，我们设计了一个解决方案程序，其中包括单层重新印象和切削平面方法，以迭代地拧紧解决方案空间。我们还提供了一种贪婪的算法，该算法可以快速解决较小的最佳差距。
The assortment planning problem is a central piece in the revenue management strategy of any company in the retail industry. In this paper, we study a robust assortment optimization problem for substitutable products under a sequential ranking-based choice model. Our modeling approach incorporates the cumulative effect of finding multiple unavailable products on the customers' purchase decisions. To model the highly uncertain order in which a customer explores the products to buy, we present a bi-level optimization approach to maximize the expected revenue under the worst-case order of products in the preference lists of customers. We provide a polynomial-time algorithm that optimally solves a special case of the unconstrained assortment planning problem under our choice model. For the general constrained version of the problem, we devise a solution procedure that includes a single-level reformulation and a cutting-plane approach to iteratively tighten the solution space. We also provide a greedy algorithm that can quickly solve large instances with small optimality gaps.
