A BAYESIAN NETWORK FOR QUANTIFYING UNCERTAINTY FOR DISTRIBUTION PLANNING MODEL

HUMBERTO  GARCÍA   CASTELLANOS; ADRIAN FRANCISCO LOERA CASTRO; CARLOS ALBERTO OCHOA ZEZATTI; JAIME SANCHEZ LEAL

doi:https://doi.org/10.6036/9764

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A BAYESIAN NETWORK FOR QUANTIFYING UNCERTAINTY FOR DISTRIBUTION PLANNING MODEL

SEPTEMBER 2020 - Volume: 95 - Pages: 465

DOI:

https://doi.org/10.6036/9764

Authors:

HUMBERTO GARCÍA CASTELLANOS

- ADRIAN FRANCISCO LOERA CASTRO - CARLOS ALBERTO OCHOA ZEZATTI -

JAIME SANCHEZ LEAL

Disciplines:

Organization and management of enterprises (LOGISTICA )

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Key words:: Planificación de la distribución, Red Bayesiana, Cadena de Suministro, Simulación de Monte Carlo, Modelo de programación lineal de enteros mixtos, Cuantificación de incertidumbre., Quantification of uncertainty, distribution planning, Bayesian Networks, supply chain management, logistics
Article type:: NOTA TECNICA / TECHNICAL NOTE
Section:: TECHNICAL NOTE

Uncertainty is a key issue in engineering design: optimal solutions generally do not involve safety margins, but real systems involve having uncertainty, variability, and error. For example, geometry, material parameters, boundary conditions, or even the model itself includes uncertainty. To provide safe designs, uncertainty must be considered in optimization models.

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