Integrating Flexible Discrete Choice and Revenue Management Models

许多行业使用传统的收入管理（RM）系统来预测产品需求，并确定产品价格和可用性。 然而，传统的RM系统是在互联网成为主导分销渠道之前设计的，并且在客户对其所有产品的了解有限的时期。 基于选择的RM系统使用离散选择模型来预测需求，以更好地反映当今的购买环境。 虽然已经做了很多选择为基础的模型的优化，很少有研究已经进行了如何估计模型使用的需求数据从一个单一的公司。本研究的重点是基于选择的RM系统的参数估计使用边际与期望对数似然函数。 边际公式具有类似的数学结构，有限信息最大似然（LIML）估计，这是以前在交通规划领域，但尚未被应用到RM。 序贯估计技术，如LIML显示强大的承诺作为一个框架，使研究人员能够解决开放的研究问题，更现实的产品替代模式和竞争对手的产品信息的结合。 主要研究目标是：（1）集成嵌套logit和基于选择的RM模型，并纳入竞争信息;（2）了解基于选择的RM模型的数据要求;（3）在行业数据集上验证这些新模型。如果成功，本研究的结果将有助于通过推进估计RM选择参数所需的理论来塑造下一代RM系统的愿景。 通过在行业数据集上测试和验证模型，这项研究将能够就成功估计基于选择的RM参数所需的数据要求和相关行业设置提出建议。这项研究的结果将导致增加利润的公司和更好的产品和服务提供给客户。 先前的研究表明，基于选择的RM系统可能会增加1%-10%的收入;因此，这项研究的潜在影响是巨大的。 这项研究的结果也将适用于其他面临删失数据的学科，例如，因为缺货而对零售额进行审查

Many industries use traditional revenue management (RM) systems to forecast demand for products and to determine product prices and availabilities. However, traditional RM systems were designed before the internet was a dominant distribution channel and during a period in which customers had limited knowledge of all of their product offerings. Choice-based RM systems use discrete choice models to forecast demand in a way that better reflects today's purchasing environment. Although much has been done on the optimization of choice-based models, little research has been undertaken on how to estimate the model using demand data from a single firm. This research focuses on estimation of parameters for choice-based RM systems using marginal versus expected log likelihood functions. The marginal formulation has a similar mathematical structure as limited information maximum likelihood (LIML) estimators, which have previously been developed in the transportation planning field but have not yet been applied to RM. Sequential estimation techniques such as LIML show strong promise as a framework that enable researchers to solve open research questions related to the incorporation of more realistic product substitution patterns and competitors product information. The primary research goals are to: (1) integrate nested logit and choice-based RM models and incorporate competitive information; (2) understand data requirements for choice-based RM models; and, (3) validate these new models on industry datasets.If successful, the results of this research will help shape the vision for the next-generation RM systems by advancing theories needed to estimate RM choice parameters. Through testing and validating models on industry datasets, this research will be able to develop recommendations on data requirements and relevant industry settings needed to successfully estimate choice-based RM parameters. The results of this research will lead to increased profits for firms and better product and service offerings for customers. Previous research has suggested incremental revenue gains ranging from 1-10 percent may be possible with choice-based RM systems; thus, potential impacts of this research are substantial. The results of this research will also be applicable to other disciplines that face censored data, e.g., when retail sales are censored due to stock-outs.