Simulation of inventory systems with unknown input models: a data-driven approach

A. Akcay, C.G. Corlu

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

3 Citaties (Scopus)

Uittreksel

Stochastic simulation is a commonly used tool by practitioners for evaluating the performance of inventory policies. A typical inventory simulation starts with the determination of the best-fit input models (e.g. probability distribution function of the demand random variable) and then obtains a performance measure estimate under these input models. However, this sequential approach ignores the uncertainty around the input models, leading to inaccurate performance measures, especially when there is limited historical input data. In this paper, we take an alternative approach and propose a simulation replication algorithm that jointly estimates the input models and the performance measure, leading to a credible interval for the performance measure under input-model uncertainty. Our approach builds on a nonparametric Bayesian input model and frees the inventory manager from making any restrictive assumptions on the functional form of the input models. Focusing on a single-product inventory simulation, we show that the proposed method improves the estimation of the service levels when compared to the traditional practice of using the best-fit or the empirical distribution as the unknown demand distribution
TaalEngels
Pagina's5826-5840
TijdschriftInternational Journal of Production Research
Volume55
Nummer van het tijdschrift19
DOI's
StatusGepubliceerd - 2017

Vingerafdruk

Simulation
Inventory systems
Random variables
Probability distributions
Distribution functions
Managers
Performance measures
Uncertainty
Stochastic simulation
Service levels
Inventory policy
Replication
Distribution function
Model uncertainty
Probability distribution
Empirical distribution
Functional form

Citeer dit

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Simulation of inventory systems with unknown input models: a data-driven approach. / Akcay, A.; Corlu, C.G.

In: International Journal of Production Research, Vol. 55, Nr. 19, 2017, blz. 5826-5840.

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

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