Near-optimal data-driven approximation schemes for joint pricing and inventory control models
Name
1065525187-MIT.pdf
Description
Full printable version
Size
4.14 MB
Format
Adobe PDF
Checksum (MD5)
c79ab440d152edb00e187dfec76a2d5a
Author(s)
Qin, Hanzhang(Scientist in civil and environmental engineering)Massachusetts Institute of Technology
Advisor(s)
David Simchi-Levi.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
The thesis studies the classical multi-period joint pricing and inventory control problem in a data-driven setting. In the problem, a retailer makes periodic decisions of the prices and inventory levels of an item that the retailer wishes to sell. The objective is to match the inventory level with a random demand that depends on the price in each period, while maximizing the expected profit over finite horizon. In reality, the demand functions or the distribution of the random noise are usually unavailable, whereas past demand data are relatively easy to collect. A novel data-driven nonparametric algorithm is proposed, which uses the past demand data to solve the joint pricing and inventory control problem, without assuming the parameters of the demand functions and the noise distributions are known. Explicit sample complexity bounds are given, on the number of data samples needed to guarantee a near-optimal profit. A simulation study suggests that the algorithm is efficient in practice.
Description
Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 95-96).
Subjects
Civil and Environmental Engineering.
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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