Robust economic and economic-statistical design of EWMA control chart

Amiri, Amirhossein; Moslemi, Amir; Doroudyan, Mohammad Hadi

doi:10.1007/s00170-014-6667-9

Cited by 20 publications

(4 citation statements)

References 24 publications

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“…If the median θ 0 (or other percentile) is also unknown, we have to use the ESD‐WRS scheme whereas the ESD‐Sign scheme cannot be used directly. Some similar illustrations may be shown using the data and the example presented in, for example, Amiri et al We skip more number of examples for brevity.…”

Section: Examples Of Applicationmentioning

confidence: 99%

Nonparametric cost‐minimized Shewhart‐type process monitoring with restricted false alarm probability

Mukherjee

2019

Quality & Reliability Eng

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Traditional Duncan-type models for cost-efficient process monitoring often inflate type I error probability. Nevertheless, controlling the probability of type I error or false alarms is one of the key issues in sequential monitoring of specific process characteristics. To this end, researchers often recommend economic-statistical designs. Such designs assign an upper bound on type I error probability to avoid excessive false alarms while achieving cost optimality. In the context of process monitoring, there is a plethora of research on parametric approaches of controlling type I error probability along with the cost optimization. In the nonparametric setup, most of the existing works on process monitoring address one of the two issues but not both simultaneously. In this article, we present two distribution-free cost-efficient Shewhart-type schemes for sequentially monitoring process location with restricted false alarm probability, based, respectively, on the sign and Wilcoxon rank-sum statistics. We consider the one-sided shift in location parameter in an unknown continuous univariate process. Nevertheless, one can easily extend our proposed schemes to monitor the two-sided process shifts. We evaluate and compare the actual performance of the two monitoring schemes employing extensive computer simulation based on Monte Carlo. We investigate the effects of the size of the reference sample and the false alarm constraint. Finally, we provide two illustrative examples, each based on a realistic situation in the industry.

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Section: Examples Of Applicationmentioning

confidence: 99%

Nonparametric cost‐minimized Shewhart‐type process monitoring with restricted false alarm probability

Mukherjee

2019

Quality & Reliability Eng

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“…Niaki et al [10] compared the ESD and economical design of EWMA control charts and introduced a particle swarm optimization method to solve it. Amiri et al [11] created an EWMA control chart with a scenario-based robust economic and ESD to consider economic and statistical criteria and uncertainty. Katebi and Pourtaheri [12] examined the ESD of the Poisson EWMA control charts for nonconformity monitoring.…”

Section: Introductionmentioning

confidence: 99%

Designing a Log-Logistic-Based EWMA Control Chart Using MOPSO and VIKOR Approaches for Monitoring Cardiac Surgery Performance

Pour,

Azizi,

Rahimzadeh

et al. 2023

Decis. Mak. Appl. Manag. Eng.

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This study aims to develop a risk-adjusted Exponentially Weighted Moving Average (EWMA) control chart for computer-based performance monitoring in cardiac surgery. Patients have distinct risk factors that impact the surgical process before it even begins. As a result, risk adjustment is carried out utilizing the Accelerated Failure Time (AFT) model to consider these factors. Before using the risk-adjusted EWMA chart, the optimal parameter design should be established, considering the required statistical and economic factors. A model for Multi-Objective Decision Making (MODM) with multiple assignable causes has been proposed to accomplish this. The model is solved using a two-stage methodology based on the Multi-Objective Particle Swarm Optimization (MOPSO) method and the VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method. An actual case study for cardiovascular patients has been undertaken to demonstrate the performance and effectiveness of the suggested model. The multi-objective and pure economic models have been thoroughly compared. The economic model with statistical constraints and the multi-objective model have also been compared again. The findings suggest that the multi-objective design of the risk-adjusted EWMA chart exhibits higher statistical performance in both cases against a small augment in cost.

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“…Here, we briefly review some researches on the economic design of EWMA control charts. To consider the uncertainty among the cost and process parameters in practice, Amiri et al [10] developed a process monitoring strategy based on the robust economic and economic-statistical design of the EWMA control chart. Considering measurement error and taking multiple measurement errors, as well as linear and quadratic Taguchi loss function of poor quality products, Saghaei et al [11] proposed the economic model of EWMA control charts.…”

Section: Introductionmentioning

confidence: 99%

Economic Design of EWMA Control Charts with Variable Sampling Intervals for Monitoring the Mean and Standard Deviation under Preventive Maintenance and Taguchi’s Loss Functions

2021

Mathematical Problems in Engineering

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The control chart and the maintenance management need to control process quality and reduce out-of-control cost. They are two key tools in the production process; however, they have usually been analyzed separately in the literature. Moreover, the existing studies integrating these two concepts suffer from three significant drawbacks as follows: (1) using control charts with fixed parameters to monitor the process, so that the small and middle shifts are detected slowly; (2) monitoring the mean and standard deviation separately, whereas, in real condition, the mean and standard deviation should be monitored simultaneously; (3) quality loss function is not usually used to design economic model, which leads to a large social quality loss in the monitoring process of control chart. To eliminate these weaknesses, the economic design of the exponential weighted moving average (EWMA) control chart with variable sampling intervals (VSI) for monitoring the mean and standard deviation under preventive maintenance and Taguchi’s loss functions is proposed. The optimal values of the parameters are determined to minimize the loss-per-item in an average cycle process. In addition, a genetic algorithm is used in a numerical example to search for the optimal values of the parameters. According to the sensitivity analysis, the effect of the model parameters on the solution of the economic model is obtained. Finally, the comparison study shows that the VSI EWMA control charts designed by the joint economic model are expected to reduce loss.

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Robust economic and economic-statistical design of EWMA control chart

Cited by 20 publications

References 24 publications

Nonparametric cost‐minimized Shewhart‐type process monitoring with restricted false alarm probability

Nonparametric cost‐minimized Shewhart‐type process monitoring with restricted false alarm probability

Designing a Log-Logistic-Based EWMA Control Chart Using MOPSO and VIKOR Approaches for Monitoring Cardiac Surgery Performance

Economic Design of EWMA Control Charts with Variable Sampling Intervals for Monitoring the Mean and Standard Deviation under Preventive Maintenance and Taguchi’s Loss Functions

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