高通胀率随机需求情景下的库存决策 - 机器学习方法

Q3 Engineering

IFAC-PapersOnLine Pub Date : 2024-01-01 DOI:10.1016/j.ifacol.2024.09.113

Vinu Siriwardena , Dilina Kosgoda , H. Niles Perera , Izabela Nielsen

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引用次数: 0

摘要

本研究利用 2022 年恶性通货膨胀期间从斯里兰卡一家领先零售公司获得的真实世界数据集，研究了恶性通货膨胀对库存决策的影响，并开发了一个机器学习模型来预测最佳订货量，以期降低库存持有成本。我们选择了六种不同的 ML 技术来确定最佳 ML 模型。使用均方根误差和 R 平方值来严格评估六种 ML 技术的性能。结果表明，随机森林是预测高通货膨胀情况下最佳订货量的最合适的 ML 模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Inventory Decisions Under Stochastic Demand Scenario with High Inflation Rate – Machine Learning Approach

This study examined the effects of hyperinflation on inventory decisions using a real-world dataset obtained from a leading retail company in Sri Lanka during the hyperinflation period of 2022 and developed a Machine Learning model to forecast optimal order quantities with the intention of lowering inventory holding costs. Six distinct ML techniques were selected to identify the best ML model. Root Mean Squared Error and R Squared values were used to rigorously evaluate the performance of the six ML techniques. The results suggest Random Forest as the most appropriate ML model to forecast optimal order quantities during a high inflation situation.

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来源期刊

IFAC-PapersOnLine Engineering-Control and Systems Engineering

CiteScore

1.70

自引率

0.00%

发文量

1122

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