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Discrete Choice Models and Operations Problems in Online Retailing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Discrete Choice Models and Operations Problems in Online Retailing./
作者:	Tang, Zhuodong.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	208 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
標題:	Optimization. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29221675
ISBN:	9798438718062

Discrete Choice Models and Operations Problems in Online Retailing.
Tang, Zhuodong.

Discrete Choice Models and Operations Problems in Online Retailing. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 208 p.

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--Hong Kong University of Science and Technology (Hong Kong), 2021.

This item must not be sold to any third party vendors.

Discrete choice models (DMs) have been used to understand customers' behavior andpredict demand for a long history. In recent years, the rapid development of e-commercebrings big challenges to traditional DCMs. First, firms can get access to much more data andhave stronger data-processing power. The prediction accuracy of parametric models improvesmarginally with big data because of misspecification errors. How to strengthen the predictivepower and use the data more efficiently? Second, traditional DCMs assume a customer selectsexactly one alternative, but it is convenient to purchase multiple products in online shopping.How to model and estimate customers' multiple purchase behaviors? How to optimize priceand assortment? Is there a Nash equilibrium under competition? Third, most existing literaturefocuses on product-level optimization, but now firms have greater flexibility when designing aproduct's features. How should they design and price products to maximize profits?This thesis answers the above three questions in online retailing. The first part proposes abinary choice forest that can capture any customer's behavior. We apply the random forestalgorithm with strong predictive power. We prove theoretical results including consistency anderror bound. The numerical studies on real data show our framework can outperform the bestparametric model. The second part studies price, assortment, and competition problems underthe Threshold Utility Model (TUM). We show the advantages of TUM compared to traditionalmodels. We solve the price and assortment optimization problem and show the existence ofNash equilibrium under competition. The third part solves the product line design problemthat a firm jointly optimizes the price and feature configurations of products under the BasicAttraction Model (BAM). We develop algorithms based on the K-shortest path and an FPTAS.We also provide performance guarantees under a special case of mixed BAM.

ISBN: 9798438718062Subjects--Topical Terms:

891104
Optimization.

Discrete Choice Models and Operations Problems in Online Retailing.
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