@inproceedings{a1818d963c484a3c950ea8f7bccbf50c,
title = "An adaptive inventory control model for a supply chain with nonstationary customer demands",
abstract = "In this paper, we propose an adaptive inventory control model for a supply chain consisting of one supplier and multiple retailers with nonstationary customer demands. The objective of the adaptive inventory control model is to minimize inventory related cost. The inventory control parameter is safety lead time. Unlike most extant inventory control approaches, modeling the uncertainty of customer demand as a statistical distribution is not a prerequisite in this model. Instead, using a reinforcement learning technique called action-reward based learning, the control parameter is designed to adaptively change as customer demand pattern changes. A simulation based experiment was performed to compare the performance of the adaptive inventory control model.",
author = "Back, {Jun Geol} and Kim, {Chang Ouk} and Kwon, {Ick Hyun}",
year = "2006",
doi = "10.1007/11801603_102",
language = "English",
isbn = "3540366679",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "895--900",
booktitle = "PRICAI 2006",
address = "Germany",
note = "9th Pacific Rim International Conference on Artificial Intelligence ; Conference date: 07-08-2006 Through 11-08-2006",
}