MARC보기
LDR02320nam u200409 4500
001000000418058
00520190215162535
008181129s2018 |||||||||||||||||c||eng d
020 ▼a 9780438376687
035 ▼a (MiAaPQ)AAI10823700
035 ▼a (MiAaPQ)duke:14756
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 658
1001 ▼a Modaresi, Sajad.
24510 ▼a Data-Driven Learning Models With Applications to Retail Operations.
260 ▼a [S.l.]: ▼b Duke University., ▼c 2018.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2018.
300 ▼a 214 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: A.
500 ▼a Adviser: Fernando Bernstein.
5021 ▼a Thesis (Ph.D.)--Duke University, 2018.
520 ▼a 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.
520 ▼a 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
520 ▼a 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
520 ▼a 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
590 ▼a School code: 0066.
650 4 ▼a Business administration.
690 ▼a 0310
71020 ▼a Duke University. ▼b Business Administration.
7730 ▼t Dissertation Abstracts International ▼g 80-02A(E).
773 ▼t Dissertation Abstract International
790 ▼a 0066
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998596 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 201812 ▼f 2019
990 ▼a ***1012033