Fuzzy Inventory Implementation of Minimum Value unused Storing Profitable by Executing Python
Year: 2024
Type: article
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.
Source: Decision Making Advances
DOI: 10.31181/dma31202524
Author: Kalaiarasi Kalaiselvan
Institution: Bharathidasan University
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4401599159