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American Journal of Applied Mathematics and Statistics. 2024, 12(2), 24-27
DOI: 10.12691/AJAMS-12-2-1
Original Research

Solving the Newsvendor Problem using Stochastic Approximation: A Kiefer-Wolfowitz Algorithm Approach

Quan Yuan1, , Zhixin Yang1 and Yayuan Xiao1

1Department of Mathematical Sciences, Ball State University, Muncie IN, USA

Pub. Date: April 21, 2024
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Cite this paper

Quan Yuan, Zhixin Yang and Yayuan Xiao. Solving the Newsvendor Problem using Stochastic Approximation: A Kiefer-Wolfowitz Algorithm Approach. American Journal of Applied Mathematics and Statistics. 2024; 12(2):24-27. doi: 10.12691/AJAMS-12-2-1

Abstract

This paper investigates the application of the Kiefer-Wolfowitz (KW) algorithm, a stochastic approximation technique, to solve the newsvendor problem under uncertain demand. The proposed approach enables the newsvendor to learn from observed profits and converge to the optimal order quantity, even when the demand distribution is unknown. Numerical experiments demonstrate the algorithm's effectiveness in handling stochastic demand and provide insights into its convergence properties. The paper highlights the potential of stochastic approximation methods in tackling inventory management challenges and discusses future research directions.

Keywords

Newsvendor problem, stochastic approximation, Kiefer-Wolfowitz algorithm

Copyright

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

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