Performance evaluation of control chart for multiple assignable causes using genetic algorithm

Ahmed, Imtiaz; Sultana, Ineen; Paul, Sanjoy Kumar; Azeem, Abdullahil

doi:10.1007/s00170-013-5412-0

Cited by 16 publications

(4 citation statements)

References 26 publications

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“…The result is a new generation with (usually) better survival abilities. This process is repeated until the strings in the new generation are identical, or certain termination conditions are met (Ahmed et al, 2014).…”

Section: Genetic Algorithmmentioning

confidence: 99%

Development of an inventory model for two suppliers with random capacity considering supply disruption

Ahmed

Sultana

Azeem

2017

IJLSM

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Supply disruption occurs for diverse reasons including transportation problem, equipment failure, raw material shortages, natural calamities etc. As a result supplier may become unavailable at random times for random time length. In addition, suppliers may also have random capacities leading to uncertain yield in orders. Again under random supplier capacities, the retailer should order from a number of suppliers in order to diversify the risk associated with shortages. The objective of this paper is to develop an inventory model for two suppliers with random capacities considering supply disruption. A modified (Q, r) model is developed to tackle the problem of future supply uncertainty in response to the demand generated by Poisson process. Two suitable optimisation algorithms are applied to search for the optimal values of the decision variables which are state dependent order quantities and reorder point to minimise the cost per unit time. A hypothetical example and its solution are then provided to have a better understanding about the demonstration of the proposed model. Finally, sensitivity analysis is also carried out to have better insights about the model developed.

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Section: Genetic Algorithmmentioning

confidence: 99%

Development of an inventory model for two suppliers with random capacity considering supply disruption

Ahmed

Sultana

Azeem

2017

IJLSM

Self Cite

View full text Add to dashboard Cite

show abstract

“…And the related parameters of FSVM are also critical for assuring the recognition accuracy. The parameters needed to be optimized include the degree of polynomial kernel d, the width of Gaussian kernel γ , the combination coefficient of hybrid kernel β and the penalty factor of FSVM C. As a stochastic optimization algorithm simulating the natural selection and genetic mechanism in the process of biological evolution, GA has excellent global searching ability and has been widely used to solve optimization problems (Whitley 1994;Ventura and Yoon 2013;Ahmed et al 2014;Takeyasu and Kainosho 2014). Consequently, it is employed to achieve parameters optimization and input feature choice for FSVM-based classifiers.…”

Section: Optimizing the Input Feature Set And Parameters Of Fsvm Usinmentioning

confidence: 99%

Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function

2015

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Accurate control chart patterns recognition (CCPR) plays an essential role in the implementation of control charts. However, it is a challenging problem since nonrandom control chart patterns (CCPs) are normally distorted by "common process variations". In this paper, a novel method of CCPR by integrating fuzzy support vector machine (SVM) with hybrid kernel function and genetic algorithm (GA) is proposed. Firstly, two shape features and two statistical features that do not depend on the distribution parameters and number of samples are presented to explicitly describe the characteristics of CCPs. Then, a novel multiclass method based on fuzzy SVM with a hybrid kernel function is proposed. In this method, the influence of outliers on classification accuracy of SVM-based classifiers is weakened by assigning a degree of membership for every training sample. Meanwhile, a hybrid kernel function combining Gaussian kernel and polynomial kernel is adopted to further enhance the generalization ability of the classifiers. To solve the issue of features selection and parameters optimization, GA is used to simultaneously optimize the input features subsets and parameters of fuzzy SVM-based classifier. Finally, several simulation experiments and a real example are addressed to validate the feasibility and effectiveness of the proposed methodology. And the results of simulation experiments demonstrate that it can achieve excellent performance for CCPR and outperforms other approaches, such as learning B Pingyu Jiang vector quantization network, multi-layer perceptron network, probability neural network, fuzzy clustering and SVM, in term of recognition accuracy. The results of the practical cases manifest that the proposed method has application potential for solving the problem of control chart interpretation in real-world.

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“…Most of the research works, like Duncan (1956) as the pioneer and then followers presented an economic design of X control charts with only one assignable cause. However, Duncan (1971), Gibra (1981), Tagaras and Lee (1988), Chung (1991), Chen and Yang (2002), Yang, Su, and Pearn (2010), Ahmed, Sultana, Paul, and Azeem (2014) presented an economic design of X control charts with multiple assignable causes.…”

Section: Introductionmentioning

confidence: 99%

Integration of Multivariate Loss Function Approach in the Hotelling’s Charts under Banerjee-Rahim (1988) Weibull Shock Models

Naderi¹,

Seif²,

Moghadam³

2021

AJS

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A proper monitoring of stochastic systems is the control charts of statistical process control and drift in characteristics of output may be due to one or several assignable causes. Although many research works have been done on the economic design of control charts with single assignable cause, the economic statistical design of T^2 control chart under Weibull shock model with multiple assignable causes and considering multivariate Taguchi loss function has not been presented yet. Using Taguchi loss function in the concept of quality control charts with economic and economic statistical design leads to better decisions in the industry. Based on the optimization of the average cost per unit of time and taking into account the different combination values of Weibull distribution parameters, optimal design values ??of sample size, sampling interval and control limit coefficient were derived and calculated. Then the cost models under non-uniform and uniform sampling scheme were compared. The results revealed that the model under multiple assignable causes with Taguchi loss function has a lower cost than single assignable cause model and integrated model with non-uniform sampling has a lower cost than that with uniform sampling.

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Performance evaluation of control chart for multiple assignable causes using genetic algorithm

Cited by 16 publications

References 26 publications

Development of an inventory model for two suppliers with random capacity considering supply disruption

Development of an inventory model for two suppliers with random capacity considering supply disruption

Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function

Integration of Multivariate Loss Function Approach in the Hotelling’s Charts under Banerjee-Rahim (1988) Weibull Shock Models

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