Flowshop simulator using different sampling methods

Tari, Megdouda; Dahmani, Abdelnasser

doi:10.1007/bf02944312

Cited by 8 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is safe by reducing substantially the risk of sampling bias, efficient by producing estimates with lower variances and convenient by removing the need to determine in advance the sample size. The efficiency of RDS over DS and SRS is proved by several comparisons, for instance, on a flow shop system and a production system [16,17]. In [9], there is a discussion deducing that RDS outperforms DS, SRS, LHS and QMC methods and it is capable of substantially reducing the cost of running simulation experiments, if properly applied.…”

Section: Introductionmentioning

confidence: 99%

A comparison of methods for selecting values of simulation input variables

Ourbih-Tari

Guebli

2015

ESAIM: PS

View full text Add to dashboard Cite

Refined descriptive sampling (RDS) is a method of sampling that can be used to produce input values for estimation of expectation of functions of output variables. This paper gives a generalization of RDS method for K input variables. An estimator of RDS is defined and shown to be unbiased and efficient compared to simple random sampling with respect to variance criterion for a class of estimators. The efficiency of RDS algorithm is discussed at the end of the paper.

show abstract

Section: Introductionmentioning

confidence: 99%