Multi-product inventory modeling with demand forecasting and Bayesian optimization
The complexity of supply chains requires advanced methods to schedule companies' inventories. This paper presents a comparison of model forecasts of demand for multiple products, choosing the best among the following: autoregressive integrated moving average (ARIMA), exponential smoothing (ES), a Bayesian regression model (BRM), and a Bayesian dynamic linear model (BDLM). To this end, cases in which the time series is normally distributed are first simulated. Second, sales predictions for three products of a gas service station are estimated using the four models, revealing the BRM to be the best model. Subsequently, the multi-product inventory model is optimized. To define the policies for ordering, inventory, costs, and profits, a Bayesian search integrating elements of a Tabu search is used to improve the solution. This inventory model optimization process is then applied to the case of a gas service station in Colombia.
Main Authors: | Valencia-Cárdenas,Marisol, Díaz-Serna,Francisco Javier, Correa-Morales,Juan Carlos |
---|---|
Format: | Digital revista |
Language: | English |
Published: |
Universidad Nacional de Colombia
2016
|
Online Access: | http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0012-73532016000400029 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Classical and bayesian statistical methods for demand forecasting. A comparative analysis
by: Valencia Cárdenas, Marisol, et al.
Published: (2015) -
Bayesian forecasting technique for energy demand in Colombia
by: Tabares Muñoz, José Fernando, et al.
Published: (2014) -
Combination forecasting method using Bayesian models and a metaheuristic, case study
by: Higuita-Alzate,David, et al.
Published: (2018) -
Bayesian forecasting and dynamic models
by: West, Mike autor/a, et al.
Published: (1997) -
Bayesian Forecasting and Dynamic Models [electronic resource] /
by: West, Mike. author., et al.
Published: (1989)