Design of Self-regulating Planning Model

Purpose: This research aims to develop a dynamic and self-regulated application that considers demand forecasts, based on linear regression as a basic algorithm for machine learning. Methodology: This research uses aggregate planning and machine learning along with inventory policies through the sol...

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Autores:
Espitia Rincon, Maria Paula
Sanabria Martínez, David Alejandro
Abril Juzga, Kevin Alberto
Santos Hernández, Andrés Felipe
Tipo de recurso:
Part of book
Fecha de publicación:
2019
Institución:
Escuela Colombiana de Ingeniería Julio Garavito
Repositorio:
Repositorio Institucional ECI
Idioma:
spa
OAI Identifier:
oai:repositorio.escuelaing.edu.co:001/1855
Acceso en línea:
https://repositorio.escuelaing.edu.co/handle/001/1855
Palabra clave:
Aprendizaje automático (Inteligencia artificial) - Modelos matemáticos
Regresión lineal
Análisis de regresión
Programación lineal
Machine learning - Mathematical models
Linear Programming
Linear Regression,
Aggregate Planning
Cost Minimization
Rights
openAccess
License
http://purl.org/coar/access_right/c_abf2
Description
Summary:Purpose: This research aims to develop a dynamic and self-regulated application that considers demand forecasts, based on linear regression as a basic algorithm for machine learning. Methodology: This research uses aggregate planning and machine learning along with inventory policies through the solver excel tool to make optimal decisions at the distribution center to reduce costs and guarantee the level of service. Findings: The findings after this study pertain to planning supply tactics in real-time, self-regulation of information in real-time and optimization of the frequency of the supply. Originality: An application capable of being updated in real-time by updating data by the planning director, which will show the optimal aggregate planning and the indicators of the costs associated with the picking operation of a company with 12000 SKU's (Stock Keeping Unit), in which a retail trade of 65 stores is carried out.