Approximation Schemes for Capacity-Constrained Assortment Optimization Under the Nested Logit Model

• 成果类型: Article; Early Access
• 署名作者: Segev, Danny
• 署名单位: Tel Aviv University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.2336
• 发表日期: 2022
• 关键词: assortment optimization; approximation scheme; dynamic programming

摘要

The main contribution of this paper resides in proposing a carefully crafted dynamic programming approach for capacitated assortment optimization under the nested logit model in its utmost generality. Specifically, we show that the optimal revenue can be efficiently approached within any degree of accuracy by synthesizing ideas related to continuous-state dynamic programming, state space discretization, and sensitivity analysis of modified revenue functions. These developments allow us to devise the first fully polynomial-time approximation scheme in this context, thus resolving fundamental open questions posed in previous papers.