Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10853
Title: Moment-based approximations for stochastic control model of type (s, S)
Authors: Kamislik, Asli Bektas
Baghezze, Feyrouz
Kesemen, Tulay
Khaniyev, Tahir
Keywords: Stochastic control model of type (s,S)
moment-based approximation
renewal reward process
Renewal-Reward Process
Inventory Model
Interference
Theorem
Publisher: Taylor & Francis Inc
Abstract: In this study, we propose an approximation for a renewal reward process that describes a stochastic control model of type (s, S) based on the first three moments of demand random variables. Various asymptotic expansions for this model exist in the literature. All these studies rely on the condition of knowing the distribution function of demand random variables and require obtaining the asymptotic expansion of the renewal function produced by them. However, obtaining a renewal function can be challenging for certain distribution families, and in some cases, the mathematical structure of the renewal function is difficult to apply. Therefore, in this study, simple and compact approximations are presented for the stochastic control model of type (s, S). The findings of this study rely on Kambo's method, through which we obtain approximations for the ergodic distribution, and the nth order ergodic moments of this process. To conclude the study, the accuracy of the proposed approximate formulas are examined through a specialized illustrative example. Moreover, it has been noted that the proposed approximation is more accurate than the approximations existing in the literature.
URI: https://doi.org/10.1080/03610926.2023.2268765
https://hdl.handle.net/20.500.11851/10853
ISSN: 0361-0926
1532-415X
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record



CORE Recommender

Page view(s)

38
checked on Nov 11, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.