THERMAL SCIENCE

International Scientific Journal

K. Kalaiarasi

,

R. Soundaria

,

Nasreen Kausar

,

Praveen Agarwal

,

Hassen Aydi

,

Habes Alsamir

OPTIMIZATION OF THE AVERAGE MONTHLY COST OF AN EOQ INVENTORY MODEL FOR DETERIORATING ITEMS IN MACHINE LEARNING USING PYTHON

ABSTRACT
In many stock disintegration issues of the real world, the decay pace of certain things might be influenced by other contiguous things. Depending on the situation, the influence of weakened items can be reduced by eliminating them through examination. We specify a model that impacts the average monthly cost, and the non-linear programming Lagrangian method is solved the specified model. The fuzzify inventory model is used to determine the lowest cost by employing a trapezoidal fuzzy number, and the defuzzification process is performed using the graded mean integration representation method. To test the model, we created a CSV file, used PYTHON (version 3.8.5), we developed a program to predict the economic order quantity and total cost.
KEYWORDS
economic order quantity, optimal total cost, machine learning, trapezoidal fuzzy number, Lagrangian method, PYTHON, graded mean integration representation method
PAPER SUBMITTED: 2021-06-15
PAPER REVISED: 2021-07-05
PAPER ACCEPTED: 2021-07-18
PUBLISHED ONLINE: 2021-12-18
DOI REFERENCE: https://doi.org/10.2298/TSCI21S2347K
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2021, VOLUME 25, ISSUE Special issue 2, PAGES [347 - 358]
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Optimization of the average monthly cost of an EOQ inventory model for deteriorating items in machine learning using PYTHON

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence