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

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dc.contributor.authorKalaiarasi, K.
dc.contributor.authorSoundaria, R.
dc.contributor.authorKausar, Nasreen
dc.contributor.authorAgarwal, Praveen
dc.contributor.authorAydi, Hassen
dc.contributor.authorAlsamir, Habes
dc.date.accessioned2026-06-27T14:46:53Z
dc.date.issued2021
dc.description.abstractIn 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.en
dc.description.sponsorshipDeanship of Post Graduate and Scientific Research at Dar Al Uloom University
dc.description.urihttps://doi.org/10.2298/tsci21s2347k
dc.identifier.doi10.2298/tsci21s2347k
dc.identifier.eissn2334-7163
dc.identifier.endpageS358
dc.identifier.issn0354-9836
dc.identifier.startpageS347
dc.identifier.urihttps://hdl.handle.net/20.500.14981/64685
dc.identifier.volume25
dc.identifier.wos000734996500031
dc.language.isoeng
dc.publisherVINCA INST NUCLEAR SCI
dc.relation.ispartofTHERMAL SCIENCE
dc.rightsopenAccess
dc.subjecteconomic order quantity
dc.subjectoptimal total cost
dc.subjectmachine learning
dc.subjecttrapezoidal fuzzy number
dc.subjectLagrangian method
dc.subjectPYTHON
dc.subjectgraded mean integration representation method
dc.subjectThermodynamics
dc.titleOPTIMIZATION OF THE AVERAGE MONTHLY COST OF AN EOQ INVENTORY MODEL FOR DETERIORATING ITEMS IN MACHINE LEARNING USING PYTHON
dc.typeArticle
dspace.entity.typePublication
local.import.sourceWOS

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