Modeling lot-size with time-dependent demand based on stochastic programming and case study of drug supply in Chile

Fernando Rojas, Víctor Leiva, Peter Wanke, Camilo Lillo, Jimena Pascual

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

8 Citas (Scopus)

Resumen

The objective of this paper is to propose a lot-sizing methodology for an inventory system that faces time-dependent random demands and that seeks to minimize total cost as a function of order, purchase, holding and shortage costs. A two-stage stochastic programming framework is derived to optimize lot-sizing decisions over a time horizon. To this end, we simulate a demand time-series by using a generalized autoregressive moving average structure. The modeling includes covariates of the demand, which are used as predictors of this. We describe an algorithm that summarizes the methodology and we discuss its computational framework. A case study with unpublished real-world data is presented to illustrate the potential of this methodology. We report that the accuracy of the demand variance estimator improves when a temporal structure is considered, instead of assuming time-independent demand. The methodology is useful in decisions related to inventory logistics management when the demand shows patterns of temporal dependence.

Idioma originalInglés
Número de artículoe0212768
PublicaciónPLoS ONE
Volumen14
N.º3
DOI
EstadoPublicada - mar 2019
Publicado de forma externa

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