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 journalArticlepeer-review

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 languageEnglish
Pages (from-to)S347-S358
JournalThermal Science
Volume25
Issue numberSpecialIssue 2
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

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

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