Iranian Institute of Industrial Engineering
Journal of Industrial and Systems Engineering
1735-8272
2
4
2009
01
01
A Comparison of the Mahalanobis-Taguchi System to A Standard Statistical Method for Defect Detection
250
258
EN
Elizabeth A.
Cudney
Missouri University of Science and Technology, Rolla, Missouri 65409 USA
David
Drain
Missouri University of Science and Technology, Rolla, Missouri 65409 USA
Kioumars
Paryani
Lawrence Technological University, Southfield, Michigan, 48075 USA
Naresh
Sharma
Missouri University of Science and Technology, Rolla, Missouri 65409 USA
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
http://www.jise.ir/article_3992.html
http://www.jise.ir/article_3992_d103181416ac8e3da5438f99d373ef41.pdf
Iranian Institute of Industrial Engineering
Journal of Industrial and Systems Engineering
1735-8272
2
4
2009
01
01
Hybrid Probabilistic Search Methods for Simulation Optimization
259
270
EN
Alireza
Kabirian
Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Black Engineering,
Ames, IA 50011, USA
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
http://www.jise.ir/article_3993.html
http://www.jise.ir/article_3993_6a98afff5a56c7462146db921527e267.pdf
Iranian Institute of Industrial Engineering
Journal of Industrial and Systems Engineering
1735-8272
2
4
2009
01
01
Using Regression based Control Limits and Probability Mixture Models for Monitoring Customer Behavior
271
287
EN
Y.
Samimi
Department of Industrial Engineering, KN Toosi University of Technology, Tehran, Iran. 1999143344
A.
Aghaie
Department of Industrial Engineering, KN Toosi University of Technology, Tehran, Iran. 1999143344
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
http://www.jise.ir/article_3994.html
http://www.jise.ir/article_3994_c5619152a84d88f013c27eba3570c287.pdf
Iranian Institute of Industrial Engineering
Journal of Industrial and Systems Engineering
1735-8272
2
4
2009
01
01
Fast Generation of Deviates for Order Statistics by an Exact Method
288
296
EN
Hashem
Mahlooji
Sharif University of Technology, Iran
mahlooji@sharif.edu
Hossein
Abouee Mehrizi
The University of Toronto, Ontario, Canada
Azin
Farzan
University of Washington, Seatle, WA, USA
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
http://www.jise.ir/article_3995.html
http://www.jise.ir/article_3995_e7a9603e6266f9b8be3176d325f0fdf0.pdf
Iranian Institute of Industrial Engineering
Journal of Industrial and Systems Engineering
1735-8272
2
4
2009
01
01
A Robust Dispersion Control Chart Based on M-estimate
297
307
EN
Hamid
Shahriari
Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
Alireza
Maddahi
Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
Amir H.
Shokouhi
Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
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
http://www.jise.ir/article_3996.html
http://www.jise.ir/article_3996_3ced80aae9f4c7ad36913ea1fc175cfa.pdf