Multivariate bounded process adjustment schemes

Govind, N.; Runger, George C.; Janakiram, Mani

doi:10.1080/16843703.2016.1208938

Cited by 3 publications

(3 citation statements)

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“…Their purpose was to acquire a better service level [23] . Govind et al proposed a multi-variable process adjustment state-space model when the adjustment cost is fixed [24] . To detect a range of shifts in the location parameter, Liu et al provided a sequential rank-based adaptive nonparametric cumulative sum control chart, the chart efficiently detected various magnitudes of shifts [25] .…”

Section: Introductionmentioning

confidence: 99%

Quadratic function based price adjustment strategy on monitoring process of power consumption load in smart grid

et al. 2022

International Journal of Electrical Power & Energy Systems

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Section: Introductionmentioning

confidence: 99%

Quadratic function based price adjustment strategy on monitoring process of power consumption load in smart grid

et al. 2022

International Journal of Electrical Power & Energy Systems

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“…Govind et al [14] s'interrogent sur la forme optimale du domaine limite à l'intérieur duquel il ne faut pas régler et étudient deux formes : ellipsoïdale correspondant à une limite sur l'écart quadratique moyen ; et parallélépipédique, correspondant à une ou deux limites sur chaque caractéristique.…”

Section: Introductionunclassified

Multivariate process adjustment by boundary contraction and taking the cost of each setting parameter into account

Pairel

2020

Int J Adv Manuf Technol

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“…The present paper is a first attempt in a particular case which is relatively tractable yet considerably useful in practice, when only two responses are influenced by two controllable factors. A recent paper by Govind et al (2018) presents an approach for multivariate dead band control where the optimal threshold that balances the frequency of adjustments with the off-target cost is obtained from simulating the process for different threshold values. In the present paper, in contrast, we follow an analytical treatment of the problem that naturally generalizes the original Box-Jenkins derivations to the bivariate case.…”

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A Bivariate Dead Band Process Adjustment Policy

del Castillo,

Goeb

2019

Preprint

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A bivariate extension to Box and Jenkins (1963) feedback adjustment problem is presented in this paper. The model balances the fixed cost of making an adjustment, which is assumed independent of the magnitude of the adjustments, with the cost of running the process off-target, which is assumed quadratic. It is also assumed that two controllable factors are available to compensate for the deviations from target of two responses in the presence of a bivariate IMA(1,1) disturbance. The optimal policy has the form of a "dead band", in which adjustments are justified only when the predicted process responses exceed some boundary in R 2 . This boundary indicates when the responses are predicted to be far enough from their targets that an additional adjustment or intervention in the process is justified. Although originally developed to control a machine tool, dead band control policies have application in other areas. For example, they could be used to control a disease through the application of a drug to a patient depending on the level of a substance in the body (e.g., diabetes control). This paper presents analytical formulae for the computation of the loss function that combines off-target and adjustment costs per time unit. Expressions are derived for the average adjustment interval and for the scaled mean square deviations from target. The minimization of the loss function and the practical use of the resulting dead band adjustment strategy is illustrated with an application to a semiconductor manufacturing process.

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Multivariate bounded process adjustment schemes

Cited by 3 publications

References 7 publications

Quadratic function based price adjustment strategy on monitoring process of power consumption load in smart grid

Quadratic function based price adjustment strategy on monitoring process of power consumption load in smart grid

Multivariate process adjustment by boundary contraction and taking the cost of each setting parameter into account

A Bivariate Dead Band Process Adjustment Policy

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