An Innovative Optimization and Computational Framework for Assortment Problems Under Consider-Then-Rank Choice Models

考虑然后排序选择模型下分类问题的创新优化和计算框架

基本信息

  • 批准号:
    1537536
  • 负责人:
  • 金额:
    $ 30万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2015
  • 资助国家:
    美国
  • 起止时间:
    2015-09-01 至 2019-08-31
  • 项目状态:
    已结题

项目摘要

The challenge of finding an assortment of selected alternatives (e.g., products or services) that maximize the total revenue, profit or welfare in the face of heterogeneous customer segments, who have different preferences across alternatives, has been recognized by an increasing number of industries to be a major strategic and operational driver of success. This generic class of problems captures fundamental planning challenges, such as: "What selection of products should an e-retailer display for each search query?", "How does a brick and mortar retailer determine the product assortment in each store?" and "What services should a central planner offer to maximize the social welfare of heterogeneous population?" In spite of increased awareness to the importance of assortment decisions, and an increasing number of available commercial software tools to support them, many if not most firms, still struggle to make effective and data-driven assortment decisions. A key challenge is how to accurately and effectively capture a customer choice model, namely the preferences of customers across alternatives. The increasing availability of `big' data allows us to build more granular models of how customers choose. Unfortunately, these same granular choice models give rise to assortment optimization models that are extremely challenging to solve. The goal of this project is to develop a unified approach to study the relationship between documented behavioral features regarding how customers make purchasing choices, and the computational tractability of the corresponding assortment optimization problems. The grant aims to significantly advance the theoretical understanding of the learning and computational limitations of various assortment models, and the development of effective computational schemes to solve practical assortment problems at large scale.This project aims to develop an innovative optimization and computational dynamic-programming based framework to study and solve assortment optimization problems under consider-then-rank choice models, which have been studied extensively in marketing and psychology. Under consider-than-rank choice models, customers are assumed to make choices in two phases. First they apply various heuristics to establish a consideration set of products they are willing to consider, and then they rank within the consideration set. Given an assortment, customers are assumed to choose the most preferred product available from within their consideration set. If successful, the framework to be developed would allow the study of how different assumptions regarding the heuristics customers apply to form their respective consideration sets and rankings affect the computational tractability of the resulting assortment problems. This will be done via an innovative graphical description of the underlying dynamic program that gives rise to "minimal" enumeration of dynamic programming sub-problems. The theoretical analysis will focus on developing tight bounds on the number sub-problems. Moreover, the "minimal" enumeration techniques will be leveraged to develop efficient practical algorithms to solve large scale practical assortment problems. Collaboration with industry partners will be used to enhance the practical impact of this research project, and to enrich the classroom experience for students.
面对不同的客户群体,他们在不同的选择中有不同的偏好,找到一种选择的替代方案(例如,产品或服务),使总收入、利润或福利最大化,这一挑战已经被越来越多的行业认识到是成功的主要战略和运营驱动因素。这类问题抓住了基本的规划挑战,例如:“电子零售商应该为每个搜索查询显示什么样的产品选择?”、“实体零售商如何确定每家商店的产品分类?”以及“中央计划者应该提供什么样的服务,以最大限度地提高异质人群的社会福利?”尽管越来越多的人意识到分类决策的重要性,并且越来越多的可用商业软件工具来支持它们,但许多(如果不是大多数的话)公司仍然在努力做出有效的和数据驱动的分类决策。一个关键的挑战是如何准确有效地捕获客户选择模型,即客户在不同选择中的偏好。越来越多的“大”数据的可用性使我们能够建立更细粒度的模型,了解客户如何选择。不幸的是,这些相同的粒度选择模型会产生极具挑战性的分类优化模型。该项目的目标是开发一种统一的方法来研究关于客户如何做出购买选择的记录行为特征之间的关系,以及相应分类优化问题的计算可追溯性。该基金旨在显著推进对各种分类模型的学习和计算限制的理论理解,并开发有效的计算方案来解决大规模的实际分类问题。本项目旨在开发一个创新的优化和基于计算动态规划的框架来研究和解决在考虑-排名选择模型下的分类优化问题,这在市场营销和心理学中已经得到了广泛的研究。在“考虑高于排名”的选择模型中,假设客户分两个阶段做出选择。首先,他们运用各种启发式方法来建立一个他们愿意考虑的产品的考虑集,然后他们在考虑集中进行排名。给定一个分类,假设客户从他们的考虑集中选择最喜欢的产品。如果成功,要开发的框架将允许研究关于启发式客户应用的不同假设如何形成各自的考虑集和排名影响最终分类问题的计算可追溯性。这将通过对底层动态规划的一种创新的图形描述来实现,这种描述产生了动态规划子问题的“最小”枚举。理论分析将集中于发展数子问题的紧界。此外,“最小”枚举技术将被用来开发有效的实用算法来解决大规模的实际分类问题。与业界合作伙伴的合作将被用来提高这个研究项目的实际影响,并丰富学生的课堂体验。

项目成果

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Retsef Levi其他文献

Fr053 LOW VOLUME BOWEL PREPARATION IN HOSPITALIZED ADULT PATIENTS IS ASSOCIATED WITH REDUCTIONS IN LENGTH OF STAY
  • DOI:
    10.1016/s0016-5085(21)01216-6
  • 发表时间:
    2021-05-01
  • 期刊:
  • 影响因子:
  • 作者:
    Christopher L. Sun;Darrick K. Li;Ana Cecilia Zenteno;Marjory A. Bravard;Peter Carolan;Bethany Daily;Sami Elamin;Jasmine Ha;Amber B. Moore;Kyan C. Safavi;Brian J. Yun;Peter Dunn;James Richter;Retsef Levi
  • 通讯作者:
    Retsef Levi

Retsef Levi的其他文献

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{{ truncateString('Retsef Levi', 18)}}的其他基金

CAREER: New Algorithmic Approaches to Computationally Challenging Stochastic Supply Chain and Revenue Management Models
职业:具有计算挑战性的随机供应链和收入管理模型的新算法方法
  • 批准号:
    0846554
  • 财政年份:
    2009
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
MSPA-MCS: Collaborative Research: Algorithms for Near-Optimal Multistage Decision-Making under Uncertainty: Online Learning from Historical Samples
MSPA-MCS:协作研究:不确定性下近乎最优的多阶段决策算法:历史样本在线学习
  • 批准号:
    0732175
  • 财政年份:
    2007
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant

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