2020-01-26T01:57:40Z
http://www.jise.ir/?_action=export&rf=summon&issue=638
Journal of Industrial and Systems Engineering
jise
1735-8272
1735-8272
2009
2
4
A Comparison of the Mahalanobis-Taguchi System to A Standard Statistical Method for Defect Detection
Elizabeth A.
Cudney
David
Drain
Kioumars
Paryani
Naresh
Sharma
The Mahalanobis-Taguchi System is a diagnosis and forecasting method for multivariate data. Mahalanobis distance is a measure based on correlations between the variables and different patterns that can be identified and analyzed with respect to a base or reference group. This paper presents a comparison of the Mahalanobis-Taguchi System and a standard statistical technique for defect detection by identifying abnormalities. The objective of this research is to provide a method for defect detection with acceptable alpha (probability of type I) and beta (probability of type II) errors.
Mahalanobis distance
Mahalanobis-Taguchi System
Multivariate
Diagnosis
Alpha
(Probability of Type I) Error
Beta (Probability of Type II) error
Forecasting
2009
01
01
250
258
http://www.jise.ir/article_3992_d103181416ac8e3da5438f99d373ef41.pdf
Journal of Industrial and Systems Engineering
jise
1735-8272
1735-8272
2009
2
4
Hybrid Probabilistic Search Methods for Simulation Optimization
Alireza
Kabirian
Discrete-event simulation based optimization is the process of finding the optimum design of a stochastic system when the performance measure(s) could only be estimated via simulation. Randomness in simulation outputs often challenges the correct selection of the optimum. We propose an algorithm that merges Ranking and Selection procedures with a large class of random search methods for continuous simulation optimization problems. Under a mild assumption, we prove the convergence of the algorithm in probability to a global optimum. The new algorithm addresses the noise in simulation outputs while benefits the proven efficiency of random search methods.
Simulation Optimization
Random search
Ranking and Selection
Asymptotic
Convergence
2009
01
01
259
270
http://www.jise.ir/article_3993_6a98afff5a56c7462146db921527e267.pdf
Journal of Industrial and Systems Engineering
jise
1735-8272
1735-8272
2009
2
4
Using Regression based Control Limits and Probability Mixture Models for Monitoring Customer Behavior
Y.
Samimi
A.
Aghaie
In order to achieve the maximum flexibility in adaptation to ever changing customer’s expectations in customer relationship management, appropriate measures of customer behavior should be continually monitored. To this end, control charts adjusted for buyer’s/visitor’s prior intention to repurchase or visit again are suitable means taking into account the heterogeneity across customers. In the case of a subscription-based service provider, this paper discusses three types of adjusted control charts considering grouped data on attribute usage measures are available at each subscription period. With appreciating the characterizing effect of customer’s overall satisfaction on his future behavior, regression based models and probability mixture models are used to account for heterogeneity in customers’ mean usage rate. Besides adjusted Shewhart and CUSUM control charts for Bernoulli and Poisson distributed usage indicators, the likelihood ratio test based on mixture probability models are investigated in term of detect ability of the shifts in usage behavior through a comparative simulation study.
customer usage behavior
attribute control charts
mixture probability model
CUSUM control chart
2009
01
01
271
287
http://www.jise.ir/article_3994_c5619152a84d88f013c27eba3570c287.pdf
Journal of Industrial and Systems Engineering
jise
1735-8272
1735-8272
2009
2
4
Fast Generation of Deviates for Order Statistics by an Exact Method
Hashem
Mahlooji
Hossein
Abouee Mehrizi
Azin
Farzan
We propose an exact method for generating random deviates from continuous order statistics. This versatile method that generates Beta deviates as a middle step can be applied to any density function without resorting to numerical inversion. We also conduct an exhaustive investigation to document the merits of our method in generating deviates from any Beta distribution.
Order Statistics
Beta Variate
Exact Method
Rejection Method
Equal Probability Partition
2009
01
01
288
296
http://www.jise.ir/article_3995_e7a9603e6266f9b8be3176d325f0fdf0.pdf
Journal of Industrial and Systems Engineering
jise
1735-8272
1735-8272
2009
2
4
A Robust Dispersion Control Chart Based on M-estimate
Hamid
Shahriari
Alireza
Maddahi
Amir H.
Shokouhi
Process control charts are proven techniques for improving quality. Specifying the control limits is the most important step in designing a control chart. The presence of outliers may extremely affect the estimates of parameters using classical methods. Robust estimators which are not affected by outliers or the small departures from the model assumptions are applied in this paper to specify the control limits. All the robust estimators of dispersion which have been proposed during the last decade are evaluated and their performance in control charting is compared. The results indicate that the M-estimate is a better estimator of dispersion in the presence of outliers. We show that when the M-estimate with a bisquare ρ -function is used to estimate the dispersion, the S control chart has the best performance among all estimators.
Statistical process control
S chart
Robust statistics
M-estimate
2009
01
01
297
307
http://www.jise.ir/article_3996_3ced80aae9f4c7ad36913ea1fc175cfa.pdf