Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models
Name
SSRN-id3354358.pdf
Description
Accepted version
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772.29 KB
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Adobe PDF
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5098910fb18179f4f151b24d1ec4cd6e
Author(s)
Qin, Hanzhang
Simchi-Levi, David
Wang, Li
Date Issued
2022
Journal
Management Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Qin, Hanzhang, Simchi-Levi, David and Wang, Li. 2022. "Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models." Management Science, 68 (9).
Version
Author's final manuscript
Abstract
We study the classic multiperiod joint pricing and inventory control problem in a data-driven setting. In this problem, a retailer makes periodic decisions on the prices and inventory levels of a product that she wishes to sell. The retailer’s objective is to maximize the expected profit over a finite horizon by matching the inventory level with a random demand, which depends on the price in each period. In reality, the demand functions or random noise distributions are usually difficult to know exactly, whereas past demand data are relatively easy to collect. We propose a data-driven approximation algorithm that uses precollected demand data to solve the joint pricing and inventory control problem. We assume that the retailer does not know the noise distributions or the true demand functions; instead, we assume either she has access to demand hypothesis sets and the true demand functions can be represented by nonnegative combinations of candidate functions in the demand hypothesis sets, or the true demand function is generalized linear. We prove the algorithm’s sample complexity bound: the number of data samples needed in each period to guarantee a near-optimal profit is [Formula: see text], where T is the number of periods, and ϵ is the absolute difference between the expected profit of the data-driven policy and the expected optimal profit. In a numerical study, we demonstrate the construction of demand hypothesis sets from data and show that the proposed data-driven algorithm solves the dynamic problem effectively and significantly improves the optimality gaps over the baseline algorithms. This paper was accepted by J. George Shanthikumar, big data analytics.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1287/MNSC.2021.4212
