首页|Studies Conducted at Technological University of Pereira on Machine Learning Rec ently Published (Methodology for Inventory Management in Neighborhood Stores Usi ng Machine Learning and Integer Linear Programming)
Studies Conducted at Technological University of Pereira on Machine Learning Rec ently Published (Methodology for Inventory Management in Neighborhood Stores Usi ng Machine Learning and Integer Linear Programming)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented. According to news originating from Technological Univers ity of Pereira by NewsRx correspondents, research stated, "Nowadays, inventory m anagement poses a challenge given the constant demands related to temporality, g eographic location, price variability, and budget availability, among others. In neighborhood shops, this process is manually done based on experience (the data generated are ignored), which is sometimes not enough to respond to changes." The news editors obtained a quote from the research from Technological Universit y of Pereira: "This shows the need to develop new strategies and tools that use data analysis techniques. Our methodology predicts the weekly demand for 14 comm on products in neighborhood stores, which is later refined based on investment c apital. The method is validated using a database built with synthetic informatio n extracted from statistical sampling. For the prediction model, three supervise d learning models are used: support vector machines (SVM), AutoRegressive models (Arx), and Gaussian processes (GP). This work proposes a restricted linear mode l given an inversion and the predicted quantity of products; the aim is to refin e the prediction while maximizing the shopkeeper's profit. Finally, the problem is solved by applying an integer linear programming paradigm. Tests regarding th e prediction and inventory adjustment stages are conducted, showing that the met hodology can predict the temporal dynamics of the data by inferring the statisti cal moments of the distributions used. It is shown that it is possible to obtain a maximum profit with a lower investment."
Technological University of PereiraCyb orgsEmerging TechnologiesInvestment and FinanceMachine Learning
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