Fuzzy Inventory Implementation of Minimum Value unused Storing Profitable by Executing Python

Kalaiarasi Kalaiselvan

doi:10.31181/dma31202524

Authors

Kalaiarasi Kalaiselvan Department of Mathematics, Cauvery College for Women (Autonomous),(Affiliated to Bharathidasan University), Tiruchirappalli, Tamil Nadu, India https://orcid.org/0000-0001-6705-5354

DOI:

https://doi.org/10.31181/dma31202524

Keywords:

Fuzzy inventory, Economic Order Quantity, Total cost

Abstract

Stock management holdings would be required in this scenario to reduce losses and increase profits. In light of this, we propose in this article that you build a model by combining time series data with machine learning machines using algorithms and employing the Lagrangian method. Combinatorial optimization is used to discover opportunities for acquiring stock at a more affordable cost and sell a portion of the unused stock to make more money for the corporation. To guarantee production, we use time series and machine learning models to forecast stock prices and demand forecasts. The Inventory Management system can be employed to oversee shop merchandise and keep appraised of every product in stock, as well as to verify the store's previous purchases. We then incorporate these projections into a combinatorial optimization algorithm to aid in the decision-making process (buy, sell, or hold), and we additionally determine the exact amounts to buy or sell in the appropriate sequence. By minimizing the sharp model in fuzzy optimization via Python programming.

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