Uittreksel
| Taal | Engels |
|---|---|
| Pagina's | 5826-5840 |
| Tijdschrift | International Journal of Production Research |
| Volume | 55 |
| Nummer van het tijdschrift | 19 |
| DOI's | |
| Status | Gepubliceerd - 2017 |
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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 tijdschrift › Tijdschriftartikel › Academic › peer review
TY - JOUR
T1 - Simulation of inventory systems with unknown input models: a data-driven approach
AU - Akcay,A.
AU - Corlu,C.G.
PY - 2017
Y1 - 2017
N2 - 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
AB - 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
U2 - 10.1080/00207543.2017.1343503
DO - 10.1080/00207543.2017.1343503
M3 - Article
VL - 55
SP - 5826
EP - 5840
JO - International Journal of Production Research
T2 - International Journal of Production Research
JF - International Journal of Production Research
SN - 0020-7543
IS - 19
ER -