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

K. Kalaiarasi; R. Soundaria; Nasreen Kausar; Praveen Agarwal; Hassen Aydi; Habes Alsamir

doi:10.2298/TSCI21S2347K

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

K. Kalaiarasi, R. Soundaria, Nasreen Kausar, Praveen Agarwal, Hassen Aydi, Habes Alsamir^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

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.

Original language	English
Pages (from-to)	S347-S358
Journal	Thermal Science
Volume	25
Issue number	SpecialIssue 2
DOIs	https://doi.org/10.2298/TSCI21S2347K
Publication status	Published - 2021
Externally published	Yes

Keywords

Lagrangian method
PYTHON
economic order quantity
graded mean integration representation method
machine learning
optimal total cost
trapezoidal fuzzy number

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10.2298/TSCI21S2347K

Cite this

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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.",

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AU - Soundaria, R.

AU - Kausar, Nasreen

AU - Agarwal, Praveen

AU - Aydi, Hassen

AU - Alsamir, Habes

PY - 2021

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AB - 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.

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

Abstract

Keywords

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