joint statistical design of double sampling x-bar and s charts 指導教授：童超塵老師...

Joint statistical design of double sampling X-bar and s charts

指導教授：童超塵老師作者： David He *, Arsen Grigoryan主講人：廖乃毅

國立雲林科技大學工業工程與管理所

ContentsIntroductionThe joint DS X-bar and s chartsFormulation of joint statistical design of th

e DS X-bar and s chartsSolving the optimization problem using ge

netic algorithmPerformance of the joint DS X-bar and s cha

rtsConclusions


Introduction-AbstractThe statistical design of the joint DS X-bar and s

charts is defined and formulated as an optimization problem and solved using a genetic algorithm.

the joint DS X-bar and s charts offer a better statistical efficiency in terms of ARL than combined EWMA and CUSUM schemes, omnibus EWMA scheme over certain shift ranges.

In comparison with the STD, TSS and VSS X-bar and R charts, the joint DS charts offer a better statistical efficiency for all ranges of the shifts.


Introduction-EWMA(EEu)What’s EEu? -EEU was obtained by running a two-sided EWMA mean

chart and a high-side EWMA variance chart simultaneously.

-two-sided EWMA mean chart ：　　 against sample number t for t=1, 2, . . . (Crowder, 1987a,b, 1989; Lucas and Saccuci, 1990)

-high-side EWMA variance chart ：　　　 against sample number t for t=1, 2, . . .• (Crowder andHamilton, 1992) the sample variance.


Introduction-EWMA(EE)What’s EE? -EE consists of a two-sided EWMA mean chart and a tw

o-sided EWMA variance chart. -two-sided EWMA variance chart ： against sample number t for t=1, 2, . . ..　　　　 and (When process variance is equal to the target variance )


Introduction-combined two-sided CUSUM(CC)For the mean chart ：　　 St= and Tt= against sample number tFor the variance chart ： Vt= and Kt= against sample number t

2

1max 0, log

VUt tV S K

2

1min 0, log

t VLtSK K


Introduction-omnibus EWMAThe omnibus EWMA ：　　　　 for i=1,2…,where 0<λ<1 選擇上式中的一些 α ，其主要是當 σ≧ σ0時，展現

局　　　部的敏感度，以及在分散中增加敏感度。而當 σ≦σ0

　　時探索均數的小偏移是較有效的。


Introduction-STD X-bar and R charts

X-bar control chart ：　 warning limits: action limits : where 0<w<kR control chart ： warning limits: action limits : where wR(ni) and kR(ni) 是標準差相關範圍的數目 (R/σ) 。


Introduction-twos-tage sampling (TSS)samples of size n0 are taken from the process at regul

ar time intervals.If one item’s X value of the sample is close to the tar

get ,then the sampling is interrupted. Otherwise the sampling goes on to the second stage.

the X-bar and R values are computed based on the whole sample size n0 .


Introduction-DS X-bar & DS S chart

DS X-bar chart was developed to improve the statistical efficiency (in terms of ARL) without increased sampling, or alternatively, to reduce the sampling without reducing the statistical efficiency. Daudin (1992) and He and Grigoryan (2002, 2003)

the DS s charts result in a significant reduction in average sample size without decreasing the out-of-control ARL in comparison withthe traditional s charts. He and Grigoryan (2002, 2003)


The joint DS X-bar and s charts

取 n2 得 Y-bar

取 n2 得 S12


Formulation of joint statistical design of the DS X-bar and s charts( 一 )

發出警報的機率

發出警報的機率

the out-of-control ARL of the joint DS X-bar and s charts.


Formulation of joint statistical design of the DS X-bar and s charts( 二 )解釋上述最佳模式：　 -the probability of taking the 2’nd sample: - 取 n2的集合 :

-P(A∪B∪C)=

-α= 在製程均數及變異數沒有偏移時，卻下在管制外的結論。

-β= 在製程均數及變異數有偏移時，卻下在管制內的結論。


Formulation of joint statistical design of the DS X-bar and s charts( 三 )Note that since ARL1= the optimization

model (1)–(4) becomes:

　 where Pa1 ,Pa2 ,Pa1s,Pa2s : 第一、二階層在管制內的機率。

1

1X S

XX


Solving the optimization problem using genetic algorithm( 一 )optimization problem formulated by model (5)–(9) is

characterized by mixed continuous-discrete variables, and discontinuous and non-convex solution space.

The operation of the genetic algorithm : (a) create a random initial solution; (b) evaluate fitness, i.e., the objective function that min ARL;(c) reproduction and mutation; (d) generate new solutions.

GA find a global optimum solution with a high probability.


Solving the optimization problem using genetic algorithm( 二 )Crossover is made up in hope that new

chromosomes will have good parts of old chromosomes and maybe the new chromosomes will be better. However it is good to leave some part of population survive to next generation.

Mutation is made to prevent the search falling into local extremes, but it should not occur very often, because then GA will in fact change to random search.

In this paper the population size=1000, crossover probability=0.5 and mutation probability=0.06.


Performance of the joint DS X-bar and s chartsComparison with combined EWMA chart, combined C

USUM chart, and omnibus EWMA chart : - The ARL was calculated using computer simulation.(10000 independence runs) - Joint DS-1 was optimized for detecting the shift with δ=0.169 and λ=1.188. - All tabulated data for joint DS X-bar and s charts wer

e confirmed by using Monte Carlo simulation with MATLAB


Performance of the joint DS X-bar and s charts

for detecting the shift with δ=0.169 and λ=1.188



Joint DS-1 較佳

Joint DS-1

較佳


Performance of the joint DS X-bar and s chartsin process mean with δ≧0.75 and shifts in process sta

ndard deviation with λ≧1.3 the joint DS scheme is better.

the combined EWMA and CUSUM outperform the DS for shifts with δ≦ 0.5 and λ ≦1.2.

EWMA,CUSUM 在反應偏移上可能有延遲出現，這種延遲稱為” inertia problem”( 慣性問題 ) 。因此 EWMA,CUSUM 在探索小偏移勝過 DS 的優點必須忽略掉此問題 (inertia problem) 才可。


Performance of the joint DS X-bar and s chartsComparison with joint STD, VSS, and TSS charts : -The design parameters of all the schemes were chosen such that the in-control ARL = 433, and the average sample size=5 -Joint DS-2 was optimized for detecting the shift with δ=0.5 and λ=1.1. -Joint DS-3 was optimized for detecting the shift with δ=0.75 and λ=1.5. joint DS X-bar and s charts result in a better

statistical performance than the rest of the charts.


ConclusionsThe results of the comparison with the combined EW

MA and CUSUM, the omnibus EWMA show that in process mean with δ≧0.75 and shifts in process standard deviation with λ≧1.3 the joint DS scheme is better.

In comparison with the joint STD, TSS and VSS X and R charts,the results show the proposed joint DS X-bar and s chart scheme outperforms these schemes for all shifts in process mean with 0<δ≦1.0 and shifts in process standard deviation with 1.0<λ≦2.0.

joint statistical design of double sampling x-bar and s charts 指導教授： 童超塵 老師...

Documents

joint statistical design of double sampling x-bar and s charts 指導教授：童超塵老師...