论文标题
马尔可夫连锁选择模型下的收入管理，并具有共同价格和分类决策
Revenue Management Under the Markov Chain Choice Model with Joint Price and Assortment Decisions
论文作者
论文摘要
找到最佳的产品价格和产品分类是收入管理中的两个基本问题。通常，卖方需要共同确定价格和分类，同时管理容量有限的资源网络。但是，尚无一种有效解决此类问题的可处理方法。研究价格和分类的静态关节优化的现有论文不能纳入资源限制。然后，我们通过资源限制和价格界限研究收入管理问题，随着时间的推移，价格和产品分类需要共同确定。我们表明，在马尔可夫链（MC）选择模型（该模型均包含多项式logit（MNL）模型）下，我们可以将基于选择的关节优化问题重新制定为可拖动的凸圆锥圆锥优化问题。我们还证明，即使对资源的限制，具有恒定价格向量的最佳解决方案也存在。此外，当没有资源限制时，具有恒定分类和价格向量的解决方案可以是最佳的。
Finding the optimal product prices and product assortment are two fundamental problems in revenue management. Usually, a seller needs to jointly determine the prices and assortment while managing a network of resources with limited capacity. However, there is not yet a tractable method to efficiently solve such a problem. Existing papers studying static joint optimization of price and assortment cannot incorporate resource constraints. Then we study the revenue management problem with resource constraints and price bounds, where the prices and the product assortments need to be jointly determined over time. We showed that under the Markov chain (MC) choice model (which subsumes the multinomial logit (MNL) model), we could reformulate the choice-based joint optimization problem as a tractable convex conic optimization problem. We also proved that an optimal solution with a constant price vector exists even with constraints on resources. In addition, a solution with both constant assortment and price vector can be optimal when there is no resource constraint.
