상세정보
Export to Refworks부가기능
Data-Driven Learning Models With Applications to Retail Operations
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Data-Driven Learning Models With Applications to Retail Operations. |
| 개인저자 | Modaresi, Sajad. |
| 단체저자명 | Duke University. Business Administration. |
| 발행사항 | [S.l.]: Duke University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 214 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-02A(E). Dissertation Abstract International |
| ISBN | 9780438376687 |
| 학위논문주기 | Thesis (Ph.D.)--Duke University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-02(E), Section: A.
Adviser: Fernando Bernstein. |
| 요약 | Data-driven approaches to decision-making under uncertainty is at the center of many operational problems. These are problems in which there is an element of uncertainty (e.g., customer demand) that needs to be estimated (learned) from data (e. |
| 요약 | The first two essays in this dissertation study the classic exploration (i.e., parameter estimation) versus exploitation (i.e., optimization) trade-off from different perspectives. The first essay takes a theoretical approach and studies such |
| 요약 | The second essay considers the dynamic assortment personalization problem of an online retailer facing heterogeneous customers with unknown product preferences. We propose a prescriptive approach, called the dynamic clustering policy, for dynami |
| 요약 | Further focusing on retail operations, the final essay studies the interplay between a retailer's return and pricing policies and customers' purchasing decisions. We characterize the retailer's optimal prices in the cases with and without produc |
| 일반주제명 | Business administration. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
