Combination forecasting method using Bayesian models and a metaheuristic, case study
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| Format: | Artículo científico |
| Langue: | en |
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Universidad Nacional de Colombia
2018
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| _version_ | 1876442357673492480 |
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| author | David Higuita Alzate |
| author_facet | David Higuita Alzate |
| contents | Combination forecasting method using Bayesian models and a metaheuristic, case study David Higuita Alzate Marisol Valencia Cárdenas Juan Carlos Correa Morales Ingeniería forecasts optimization theory Bayesian statistics Statistics and probability Planning of demand forecasting for perishable products is important for any type of industry that manufactures or distributes, especially if it has a seasonal behavior and a difficult to predict variability. This paper proposes a metaheuristic based on Ant Colony Optimization (ACO) for the combination of forecasts of multiple products, based on three models: Mixed Linear Model (MLM), Bayesian Regression Model with Innovation (BRM) and Dynamic Linear Bayesian Model (BDLM), which are part of the proposed combination whose process is based on minimizing the Mean of Absolute percentage Error (SMAPE) indicator. It is found that the BDLM and BRM methodologies obtain good results on an individual basis, being better BRM, however, the ACO algorithm designed yields a better result, facilitating an adequate prediction of the demand of several products of a company in the meat buffer sector. 2018 artículo científico 0012-7353 https://www.redalyc.org/articulo.oa?id=49658894042 https://www.redalyc.org/journal/496/49658894042/ https://www.redalyc.org/journal/496/49658894042/html/ https://www.redalyc.org/journal/496/49658894042/49658894042.epub https://www.redalyc.org/journal/496/49658894042/movil 10.15446/dyna.v85n207.68424 en http://www.redalyc.org/revista.oa?id=496 Dyna application/pdf Universidad Nacional de Colombia Dyna (Colombia) Num.207 Vol.85 |
| format | Artículo científico |
| id | redalyc_49658894042 |
| institution | Redalyc |
| language | en |
| publishDate | 2018 |
| publisher | Universidad Nacional de Colombia |
| spellingShingle | Combination forecasting method using Bayesian models and a metaheuristic, case study David Higuita Alzate Ingeniería forecasts optimization theory Bayesian statistics Statistics and probability Combination forecasting method using Bayesian models and a metaheuristic, case study David Higuita Alzate Marisol Valencia Cárdenas Juan Carlos Correa Morales Ingeniería forecasts optimization theory Bayesian statistics Statistics and probability Planning of demand forecasting for perishable products is important for any type of industry that manufactures or distributes, especially if it has a seasonal behavior and a difficult to predict variability. This paper proposes a metaheuristic based on Ant Colony Optimization (ACO) for the combination of forecasts of multiple products, based on three models: Mixed Linear Model (MLM), Bayesian Regression Model with Innovation (BRM) and Dynamic Linear Bayesian Model (BDLM), which are part of the proposed combination whose process is based on minimizing the Mean of Absolute percentage Error (SMAPE) indicator. It is found that the BDLM and BRM methodologies obtain good results on an individual basis, being better BRM, however, the ACO algorithm designed yields a better result, facilitating an adequate prediction of the demand of several products of a company in the meat buffer sector. 2018 artículo científico 0012-7353 https://www.redalyc.org/articulo.oa?id=49658894042 https://www.redalyc.org/journal/496/49658894042/ https://www.redalyc.org/journal/496/49658894042/html/ https://www.redalyc.org/journal/496/49658894042/49658894042.epub https://www.redalyc.org/journal/496/49658894042/movil 10.15446/dyna.v85n207.68424 en http://www.redalyc.org/revista.oa?id=496 Dyna application/pdf Universidad Nacional de Colombia Dyna (Colombia) Num.207 Vol.85 |
| title | Combination forecasting method using Bayesian models and a metaheuristic, case study |
| topic | Ingeniería forecasts optimization theory Bayesian statistics Statistics and probability |
| url | https://www.redalyc.org/articulo.oa?id=49658894042 https://www.redalyc.org/journal/496/49658894042/ https://www.redalyc.org/journal/496/49658894042/html/ https://www.redalyc.org/journal/496/49658894042/49658894042.epub https://www.redalyc.org/journal/496/49658894042/movil |